uni-leipzig-open-access/json/s41597-023-02096-0
2024-01-25 14:46:53 +01:00

1 line
No EOL
90 KiB
Text

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T07:29:50Z","timestamp":1705044590613},"reference-count":205,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T00:00:00Z","timestamp":1680912000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T00:00:00Z","timestamp":1680912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100011937","name":"Nieders\u00e4chsische Ministerium f\u00fcr Wissenschaft und Kultur","doi-asserted-by":"publisher"},{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher"},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led to a high demand for catalogues of spectral indices and tools for their computation. However, most of these resources are either closed-source, outdated, unconnected to a catalogue or lacking a common Application Programming Interface (API). Here we present \u201cAwesome Spectral Indices\u201d (ASI), a standardized catalogue of spectral indices for Earth system research. ASI provides a comprehensive machine readable catalogue of spectral indices, which is linked to a Python library. ASI delivers a broad set of attributes for each spectral index, including names, formulas, and source references. The catalogue can be extended by the user community, ensuring that ASI remains current and enabling a wider range of scientific applications. Furthermore, the Python library enables the application of the catalogue to real-world data and thereby facilitates the efficient use of remote sensing resources in multiple Earth system domains.<\/jats:p>","DOI":"10.1038\/s41597-023-02096-0","type":"journal-article","created":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T13:02:46Z","timestamp":1680958966000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research"],"prefix":"10.1038","volume":"10","author":[{"given":"David","family":"Montero","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2745-9535","authenticated-orcid":false,"given":"C\u00e9sar","family":"Aybar","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3031-613X","authenticated-orcid":false,"given":"Miguel D.","family":"Mahecha","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3249-3703","authenticated-orcid":false,"given":"Francesco","family":"Martinuzzi","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8761-2821","authenticated-orcid":false,"given":"Maximilian","family":"S\u00f6chting","sequence":"additional","affiliation":[]},{"given":"Sebastian","family":"Wieneke","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,8]]},"reference":[{"key":"2096_CR1","doi-asserted-by":"publisher","unstructured":"Liang, S. & Wang, J. A systematic view of remote sensing. In Advanced Remote Sensing, chap. 1, 1\u201357, https:\/\/doi.org\/10.1016\/b978-0-12-815826-5.00001-5, second edn (Elsevier, 2020).","DOI":"10.1016\/b978-0-12-815826-5.00001-5"},{"key":"2096_CR2","doi-asserted-by":"publisher","first-page":"201","DOI":"10.5194\/ESD-11-201-2020","volume":"11","author":"MD Mahecha","year":"2020","unstructured":"Mahecha, M. D. et al. Earth system data cubes unravel global multivariate dynamics. Earth System Dynamics 11, 201\u2013234, https:\/\/doi.org\/10.5194\/ESD-11-201-2020 (2020).","journal-title":"Earth System Dynamics"},{"key":"2096_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s40823-020-00054-9","volume":"5","author":"MA Crowley","year":"2020","unstructured":"Crowley, M. A. & Cardille, J. A. Remote Sensing\u2019s Recent and Future Contributions to Landscape Ecology. Current Landscape Ecology Reports 5, 45\u201357, https:\/\/doi.org\/10.1007\/s40823-020-00054-9 (2020).","journal-title":"Current Landscape Ecology Reports"},{"key":"2096_CR4","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1109\/MGRS.2021.3066260","volume":"9","author":"J Emmanuel Johnson","year":"2021","unstructured":"Emmanuel Johnson, J., Laparra, V., Piles, M. & Camps-Valls, G. Gaussianizing the Earth: Multidimensional information measures for Earth data analysis. IEEE Geoscience and Remote Sensing Magazine 9, 191\u2013208, https:\/\/doi.org\/10.1109\/MGRS.2021.3066260 (2021).","journal-title":"IEEE Geoscience and Remote Sensing Magazine"},{"key":"2096_CR5","doi-asserted-by":"publisher","first-page":"102703","DOI":"10.1016\/J.JAG.2022.102703","volume":"107","author":"Y Zhao","year":"2022","unstructured":"Zhao, Y. & Zhu, Z. ASI: An artificial surface Index for Landsat 8 imagery. International Journal of Applied Earth Observation and Geoinformation 107, 102703, https:\/\/doi.org\/10.1016\/J.JAG.2022.102703 (2022).","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"2096_CR6","doi-asserted-by":"publisher","first-page":"111383","DOI":"10.1016\/J.RSE.2019.111383","volume":"233","author":"J Xiao","year":"2019","unstructured":"Xiao, J. et al. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sensing of Environment 233, 111383, https:\/\/doi.org\/10.1016\/J.RSE.2019.111383 (2019).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR7","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/J.RSE.2015.11.032","volume":"185","author":"MA Wulder","year":"2016","unstructured":"Wulder, M. A. et al. The global Landsat archive: Status, consolidation, and direction. Remote Sensing of Environment 185, 271\u2013283, https:\/\/doi.org\/10.1016\/J.RSE.2015.11.032 (2016).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR8","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/J.RSE.2017.06.031","volume":"202","author":"N Gorelick","year":"2017","unstructured":"Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202, 18\u201327, https:\/\/doi.org\/10.1016\/J.RSE.2017.06.031 (2017).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR9","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","volume":"8","author":"CJ Tucker","year":"1979","unstructured":"Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8, 127\u2013150, https:\/\/doi.org\/10.1016\/0034-4257(79)90013-0 (1979).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR10","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1080\/01431160304987","volume":"24","author":"Y Zha","year":"2003","unstructured":"Zha, Y., Gao, J. & Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing 24, 583\u2013594, https:\/\/doi.org\/10.1080\/01431160304987 (2003).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR11","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/J.RSE.2005.04.014","volume":"97","author":"AM Smith","year":"2005","unstructured":"Smith, A. M. et al. Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African Savannahs. Remote Sensing of Environment 97, 92\u2013115, https:\/\/doi.org\/10.1016\/J.RSE.2005.04.014 (2005).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR12","doi-asserted-by":"publisher","first-page":"2753","DOI":"10.1080\/01431160600954704","volume":"28","author":"AM Smith","year":"2007","unstructured":"Smith, A. M. et al. Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS. International Journal of Remote Sensing 28, 2753\u20132775, https:\/\/doi.org\/10.1080\/01431160600954704 (2007).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR13","unstructured":"Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. Monitoring vegetation systems in the Great Plains with ERTS. Tech. Rep., NASA (1974)."},{"key":"2096_CR14","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","volume":"25","author":"AR Huete","year":"1988","unstructured":"Huete, A. R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295\u2013309, https:\/\/doi.org\/10.1016\/0034-4257(88)90106-X (1988).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR15","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1109\/IGARSS.1994.399618","volume":"4","author":"GA Riggs","year":"1994","unstructured":"Riggs, G. A., Hall, D. K. & Salomonson, V. V. Snow index for the Landsat Thematic Mapper and moderate resolution imaging spectroradiometer. International Geoscience and Remote Sensing Symposium (IGARSS) 4, 1942\u20131944, https:\/\/doi.org\/10.1109\/IGARSS.1994.399618 (1994).","journal-title":"International Geoscience and Remote Sensing Symposium (IGARSS)"},{"key":"2096_CR16","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1080\/01431169608948714","volume":"17","author":"SK McFeeters","year":"1996","unstructured":"McFeeters, S. K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17, 1425\u20131432, https:\/\/doi.org\/10.1080\/01431169608948714 (1996).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR17","doi-asserted-by":"publisher","author":"Y Zeng","year":"2022","unstructured":"Zeng, Y. et al. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth and Environment https:\/\/doi.org\/10.1038\/s43017-022-00298-5 (2022).","journal-title":"Nature Reviews Earth and Environment","DOI":"10.1038\/s43017-022-00298-5"},{"key":"2096_CR18","doi-asserted-by":"publisher","unstructured":"Xue, J. & Su, B. Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors 2017, https:\/\/doi.org\/10.1155\/2017\/1353691 (2017).","DOI":"10.1155\/2017\/1353691"},{"key":"2096_CR19","doi-asserted-by":"publisher","first-page":"111676","DOI":"10.1016\/J.RSE.2020.111676","volume":"240","author":"P Yang","year":"2020","unstructured":"Yang, P., van der Tol, C., Campbell, P. K. & Middleton, E. M. Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence. Remote Sensing of Environment 240, 111676, https:\/\/doi.org\/10.1016\/J.RSE.2020.111676 (2020).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR20","doi-asserted-by":"publisher","unstructured":"Zeng, Y. et al. Estimating near-infrared reflectance of vegetation from hyperspectral data. Remote Sensing of Environment 267, https:\/\/doi.org\/10.1016\/j.rse.2021.112723 (2021).","DOI":"10.1016\/j.rse.2021.112723"},{"key":"2096_CR21","doi-asserted-by":"publisher","unstructured":"Badgley, G., Field, C. B. & Berry, J. A. Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances 3, https:\/\/doi.org\/10.1126\/SCIADV.1602244\/SUPPL_FILE\/1602244_SM.PDF (2017).","DOI":"10.1126\/SCIADV.1602244\/SUPPL_FILE\/1602244_SM.PDF"},{"key":"2096_CR22","doi-asserted-by":"publisher","first-page":"7447","DOI":"10.1126\/SCIADV.ABC7447\/SUPPL_FILE\/ABC7447_SM.PDF","volume":"7","author":"G Camps-Valls","year":"2021","unstructured":"Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Science Advances 7, 7447\u20137473, https:\/\/doi.org\/10.1126\/SCIADV.ABC7447\/SUPPL_FILE\/ABC7447_SM.PDF (2021).","journal-title":"Science Advances"},{"key":"2096_CR23","doi-asserted-by":"publisher","first-page":"5403","DOI":"10.1080\/0143116042000274015","volume":"25","author":"J Dash","year":"2004","unstructured":"Dash, J. & Curran, P. J. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing 25, 5403\u20135413, https:\/\/doi.org\/10.1080\/0143116042000274015 (2004).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR24","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/J.ISPRSJPRS.2013.04.007","volume":"82","author":"WJ Frampton","year":"2013","unstructured":"Frampton, W. J., Dash, J., Watmough, G. & Milton, E. J. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing 82, 83\u201392, https:\/\/doi.org\/10.1016\/J.ISPRSJPRS.2013.04.007 (2013).","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"2096_CR25","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/J.RSE.2013.08.029","volume":"140","author":"GL Feyisa","year":"2014","unstructured":"Feyisa, G. L., Meilby, H., Fensholt, R. & Proud, S. R. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment 140, 23\u201335, https:\/\/doi.org\/10.1016\/J.RSE.2013.08.029 (2014).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR26","doi-asserted-by":"publisher","first-page":"3025","DOI":"10.1080\/01431160600589179","volume":"27","author":"H Xu","year":"2006","unstructured":"Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing 27, 3025\u20133033, https:\/\/doi.org\/10.1080\/01431160600589179 (2006).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR27","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/J.JAG.2018.01.018","volume":"68","author":"X Wang","year":"2018","unstructured":"Wang, X. et al. A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation 68, 73\u201391, https:\/\/doi.org\/10.1016\/J.JAG.2018.01.018 (2018).","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"2096_CR28","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.3390\/W13121647","volume":"13","author":"W Jiang","year":"2021","unstructured":"Jiang, W. et al. An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery. Water 13, 1647, https:\/\/doi.org\/10.3390\/W13121647 (2021).","journal-title":"Water"},{"key":"2096_CR29","first-page":"109","volume":"7","author":"MdP Mart\u00edn","year":"1998","unstructured":"Mart\u00edn, Md. P. & Chuvieco, E. Cartograf\u00eda de grandes incendios forestales en la pen\u00ednsula ib\u00e9rica a partir de im\u00e1genes NOAA-AVHRR. Serie Geogr\u00e1fica 7, 109\u2013128 (1998).","journal-title":"Serie Geogr\u00e1fica"},{"key":"2096_CR30","doi-asserted-by":"publisher","first-page":"364","DOI":"10.3390\/ECRS-2-05177","volume":"2","author":"F Filipponi","year":"2018","unstructured":"Filipponi, F. BAIS2: Burned Area Index for Sentinel-2. Proceedings 2, 364, https:\/\/doi.org\/10.3390\/ECRS-2-05177 (2018).","journal-title":"Proceedings"},{"key":"2096_CR31","doi-asserted-by":"publisher","first-page":"2774","DOI":"10.3390\/RS11232774","volume":"11","author":"A Dixit","year":"2019","unstructured":"Dixit, A., Goswami, A. & Jain, S. Development and Evaluation of a New \u201cSnow Water Index (SWI)\u201d for Accurate Snow Cover Delineation. Remote Sensing 11, 2774, https:\/\/doi.org\/10.3390\/RS11232774 (2019).","journal-title":"Remote Sensing"},{"key":"2096_CR32","unstructured":"Kawamura, M., Jayamanna, S. & Tsujiko, Y. Relation Between Social and Environmental Conditions in Colombo. Sri Lanka and the Urban Index Estimated by Satellite Remote Sensing Data. In XVIIIth ISPRS Congress, 321\u2013326 (1996)."},{"key":"2096_CR33","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3390\/LAND7030081","volume":"7","author":"A Rasul","year":"2018","unstructured":"Rasul, A. et al. Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates. Land 7, 81, https:\/\/doi.org\/10.3390\/LAND7030081 (2018).","journal-title":"Land"},{"key":"2096_CR34","doi-asserted-by":"publisher","first-page":"3826","DOI":"10.1109\/IGARSS.2005.1525743","volume":"6","author":"H Lin","year":"2005","unstructured":"Lin, H., Wang, J., Liu, S., Qu, Y. & Wan, H. Studies on urban areas extraction from Landsat TM images. International Geoscience and Remote Sensing Symposium (IGARSS) 6, 3826\u20133829, https:\/\/doi.org\/10.1109\/IGARSS.2005.1525743 (2005).","journal-title":"International Geoscience and Remote Sensing Symposium (IGARSS)"},{"key":"2096_CR35","doi-asserted-by":"publisher","first-page":"231","DOI":"10.3390\/LAND10030231","volume":"10","author":"CT Nguyen","year":"2021","unstructured":"Nguyen, C. T., Chidthaisong, A., Diem, P. K. & Huo, L. Z. A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8. Land 10, 231, https:\/\/doi.org\/10.3390\/LAND10030231 (2021).","journal-title":"Land"},{"key":"2096_CR36","unstructured":"Henrich, V. et al. Development of an online indices database: Motivation, concept and implementation. In 6th EARSeL Imaging Spectroscopy SIG Workshop Innovative Tool for Scientific and Commercial Environment Applications (Tel Aviv, 2009)."},{"key":"2096_CR37","unstructured":"Henrich, V., Krauss, G., G\u00f6tze, C. & Sandow, C. IDB-www.indexdatabase.de Entwicklung einer Datenbank f\u00fcr Fernerkundungsindizes Ziele und Eigenschaften der IDB. Tech. Rep., AK Fernerkundung, Bochum (2012)."},{"key":"2096_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/S40965-017-0031-6","volume":"2","author":"M Grizonnet","year":"2017","unstructured":"Grizonnet, M. et al. Orfeo ToolBox: open source processing of remote sensing images. Open Geospatial Data, Software and Standards 2, 1\u20138, https:\/\/doi.org\/10.1186\/S40965-017-0031-6 (2017).","journal-title":"Open Geospatial Data, Software and Standards"},{"key":"2096_CR39","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.5194\/GMD-8-1991-2015","volume":"8","author":"O Conrad","year":"2015","unstructured":"Conrad, O. et al. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geoscientific Model Development 8, 1991\u20132007, https:\/\/doi.org\/10.5194\/GMD-8-1991-2015 (2015).","journal-title":"Geoscientific Model Development"},{"key":"2096_CR40","doi-asserted-by":"publisher","unstructured":"Hoyer, S. & Hamman, J. J. xarray: N-D labeled Arrays and Datasets in Python. Journal of Open Research Software 5, https:\/\/doi.org\/10.5334\/JORS.148 (2017).","DOI":"10.5334\/JORS.148"},{"key":"2096_CR41","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1078\/0176-1617-01176","volume":"161","author":"AA Gitelson","year":"2004","unstructured":"Gitelson, A. A. Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation. Journal of Plant Physiology 161, 165\u2013173, https:\/\/doi.org\/10.1078\/0176-1617-01176 (2004).","journal-title":"Journal of Plant Physiology"},{"key":"2096_CR42","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.3390\/RS14071727","volume":"14","author":"E Alcaras","year":"2022","unstructured":"Alcaras, E., Costantino, D., Guastaferro, F., Parente, C. & Pepe, M. Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 Imagery. Remote Sensing 14, 1727, https:\/\/doi.org\/10.3390\/RS14071727 (2022).","journal-title":"Remote Sensing"},{"key":"2096_CR43","doi-asserted-by":"publisher","unstructured":"Alvarez-Mozos, J., Villanueva, J., Arias, M. & Gonzalez-Audicana, M. Correlation Between NDVI and Sentinel-1 Derived Features for Maize. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 6773\u20136776, https:\/\/doi.org\/10.1109\/IGARSS47720.2021.9554099 (Institute of Electrical and Electronics Engineers (IEEE), 2021).","DOI":"10.1109\/IGARSS47720.2021.9554099"},{"key":"2096_CR44","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1080\/01431160310001618031","volume":"25","author":"A Apan","year":"2004","unstructured":"Apan, A., Held, A., Phinn, S. & Markley, J. Detecting sugarcane \u2018orange rust\u2019 disease using EO-1 Hyperion hyperspectral imagery. International Journal of Remote Sensing 25, 489\u2013498, https:\/\/doi.org\/10.1080\/01431160310001618031 (2004).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR45","doi-asserted-by":"publisher","first-page":"2777","DOI":"10.3390\/RS13142777","volume":"13","author":"M Arreola-Esquivel","year":"2021","unstructured":"Arreola-Esquivel, M. et al. Non-Binary Snow Index for Multi-Component Surfaces. Remote Sensing 13, 2777, https:\/\/doi.org\/10.3390\/RS13142777 (2021).","journal-title":"Remote Sensing"},{"key":"2096_CR46","doi-asserted-by":"publisher","first-page":"2957","DOI":"10.3390\/RS4102957","volume":"4","author":"AR As-syakur","year":"2012","unstructured":"As-syakur, A. R., Adnyana, I. W. S., Arthana, I. W. & Nuarsa, I. W. Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area. Remote Sensing 4, 2957\u20132970, https:\/\/doi.org\/10.3390\/RS4102957 (2012).","journal-title":"Remote Sensing"},{"key":"2096_CR47","doi-asserted-by":"publisher","first-page":"3053","DOI":"10.1109\/IGARSS.2002.1026867","volume":"5","author":"A Bannari","year":"2002","unstructured":"Bannari, A., Asalhi, H. & Teillet, P. M. Transformed difference vegetation index (TDVI) for vegetation cover mapping. International Geoscience and Remote Sensing Symposium (IGARSS) 5, 3053\u20133055, https:\/\/doi.org\/10.1109\/IGARSS.2002.1026867 (2002).","journal-title":"International Geoscience and Remote Sensing Symposium (IGARSS)"},{"key":"2096_CR48","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1109\/IGARSS.1989.576128","volume":"3","author":"F Baret","year":"1989","unstructured":"Baret, F., Guyot, G. & Major, D. J. TSAVI: A vegetation index which minimizes soil brightness effects on LAI and APAR estimation. Digest - International Geoscience and Remote Sensing Symposium (IGARSS) 3, 1355\u20131358, https:\/\/doi.org\/10.1109\/IGARSS.1989.576128 (1989).","journal-title":"Digest - International Geoscience and Remote Sensing Symposium (IGARSS)"},{"key":"2096_CR49","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/0034-4257(91)90009-U","volume":"35","author":"F Baret","year":"1991","unstructured":"Baret, F. & Guyot, G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 35, 161\u2013173, https:\/\/doi.org\/10.1016\/0034-4257(91)90009-U (1991).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR50","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/J.JAG.2015.02.012","volume":"39","author":"J Bendig","year":"2015","unstructured":"Bendig, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation 39, 79\u201387, https:\/\/doi.org\/10.1016\/J.JAG.2015.02.012 (2015).","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"2096_CR51","doi-asserted-by":"publisher","first-page":"640","DOI":"10.2134\/AGRONJ1968.00021962006000060016X","volume":"60","author":"GS Birth","year":"1968","unstructured":"Birth, G. S. & McVey, G. R. Measuring the Color of Growing Turf with a Reflectance Spectrophotometer. Agronomy Journal 60, 640\u2013643, https:\/\/doi.org\/10.2134\/AGRONJ1968.00021962006000060016X (1968).","journal-title":"Agronomy Journal"},{"key":"2096_CR52","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.3390\/RS12152359","volume":"12","author":"V Blanco","year":"2020","unstructured":"Blanco, V. et al. Potential of UAS-Based Remote Sensing for Estimating Tree Water Status and Yield in Sweet Cherry Trees. Remote Sensing 12, 2359, https:\/\/doi.org\/10.3390\/RS12152359 (2020).","journal-title":"Remote Sensing"},{"key":"2096_CR53","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1080\/10106049.2018.1497094","volume":"34","author":"R Bouhennache","year":"2018","unstructured":"Bouhennache, R., Bouden, T., Taleb-Ahmed, A. & Cheddad, A. A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery. Geocarto International 34, 1531\u20131551, https:\/\/doi.org\/10.1080\/10106049.2018.1497094 (2018).","journal-title":"Geocarto International"},{"key":"2096_CR54","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","volume":"76","author":"NH Broge","year":"2001","unstructured":"Broge, N. H. & Leblanc, E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76, 156\u2013172, https:\/\/doi.org\/10.1016\/S0034-4257(00)00197-8 (2001).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR55","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1080\/01431169308904370","volume":"14","author":"C Buschmann","year":"1993","unstructured":"Buschmann, C. & Nagel, E. In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing 14, 711\u2013722, https:\/\/doi.org\/10.1080\/01431169308904370 (1993).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR56","unstructured":"Cao, Y.-G., Li-Juan, Y. & Zheng, Z.-Z. Extraction of Information on Geology Hazard from Multi-Polarization SAR Images. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1529\u20131532 (2008)."},{"key":"2096_CR57","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/S0034-4257(02)00037-8","volume":"82","author":"P Ceccato","year":"2002","unstructured":"Ceccato, P., Flasse, S. & Gr\u00e9goire, J. M. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach. Remote Sensing of Environment 82, 188\u2013197, https:\/\/doi.org\/10.1016\/S0034-4257(02)00037-8 (2002).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR58","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","volume":"22","author":"JM Chen","year":"1996","unstructured":"Chen, J. M. Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications. Canadian Journal of Remote Sensing 22, 229\u2013242, https:\/\/doi.org\/10.1080\/07038992.1996.10855178 (1996).","journal-title":"Canadian Journal of Remote Sensing"},{"key":"2096_CR59","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/0034-4257(89)90076-X","volume":"29","author":"JG Clevers","year":"1989","unstructured":"Clevers, J. G. Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture. Remote Sensing of Environment 29, 25\u201337, https:\/\/doi.org\/10.1016\/0034-4257(89)90076-X (1989).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR60","doi-asserted-by":"publisher","unstructured":"Coffelt, J. L. & Livingston, R. K. Second U.S. Geological Survey Wildland Fire Workshop: Los Alamos, New Mexico, October 31-November 3, 2000. Tech. Rep., USGS, https:\/\/doi.org\/10.3133\/OFR0211 (2002).","DOI":"10.3133\/OFR0211"},{"key":"2096_CR61","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/0034-4257(90)90085-Z","volume":"34","author":"RE Crippen","year":"1990","unstructured":"Crippen, R. E. Calculating the vegetation index faster. Remote Sensing of Environment 34, 71\u201373, https:\/\/doi.org\/10.1016\/0034-4257(90)90085-Z (1990).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR62","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/S0034-4257(98)00046-7","volume":"66","author":"B Datt","year":"1998","unstructured":"Datt, B. Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves. Remote Sensing of Environment 66, 111\u2013121, https:\/\/doi.org\/10.1016\/S0034-4257(98)00046-7 (1998).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR63","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","volume":"74","author":"CS Daughtry","year":"2000","unstructured":"Daughtry, C. S., Walthall, C. L., Kim, M. S., De Colstoun, E. B. & McMurtrey, J. E. Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment 74, 229\u2013239, https:\/\/doi.org\/10.1016\/S0034-4257(00)00113-9 (2000).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR64","doi-asserted-by":"publisher","first-page":"112763","DOI":"10.1016\/J.RSE.2021.112763","volume":"268","author":"B Dechant","year":"2022","unstructured":"Dechant, B. et al. NIRVP: A robust structural proxy for sun-induced chlorophyll fluorescence and photosynthesis across scales. Remote Sensing of Environment 268, 112763, https:\/\/doi.org\/10.1016\/J.RSE.2021.112763 (2022).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR65","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/J.JAG.2015.02.010","volume":"39","author":"Y Deng","year":"2015","unstructured":"Deng, Y., Wu, C., Li, M. & Chen, R. RNDSI: A ratio normalized difference soil index for remote sensing of urban\/suburban environments. International Journal of Applied Earth Observation and Geoinformation 39, 40\u201348, https:\/\/doi.org\/10.1016\/J.JAG.2015.02.010 (2015).","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"2096_CR66","first-page":"1385","volume":"132","author":"R Escadafal","year":"1991","unstructured":"Escadafal, R. & Huete, A. Etude des propri\u00e9t\u00e9s spectrales des sols arides appliqu\u00e9e \u00e0 l\u2019am\u00e9lioration des indices de v\u00e9g\u00e9tation obtenus par t\u00e9l\u00e9d\u00e9tection. Comptes Rendus de l\u2019Acad\u00e9mie des Sciences 132, 1385\u20131391 (1991).","journal-title":"Comptes Rendus de l\u2019Acad\u00e9mie des Sciences"},{"key":"2096_CR67","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/J.ECOLIND.2015.03.037","volume":"56","author":"RC Estoque","year":"2015","unstructured":"Estoque, R. C. & Murayama, Y. Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI\/TIRS imageries: A comparative assessment of various spectral indices. Ecological Indicators 56, 205\u2013217, https:\/\/doi.org\/10.1016\/J.ECOLIND.2015.03.037 (2015).","journal-title":"Ecological Indicators"},{"key":"2096_CR68","doi-asserted-by":"publisher","first-page":"167","DOI":"10.11873\/J.ISSN.1004-0323.2009.2.167","volume":"24","author":"D Feng","year":"2009","unstructured":"Feng, D. A New Method for Fast Information Extraction of Water Bodies Using Remotely Sensed Data. Remote Sensing Technology and Application 24, 167\u2013171, https:\/\/doi.org\/10.11873\/J.ISSN.1004-0323.2009.2.167 (2009).","journal-title":"Remote Sensing Technology and Application"},{"key":"2096_CR69","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/J.RSE.2015.12.055","volume":"175","author":"A Fisher","year":"2016","unstructured":"Fisher, A., Flood, N. & Danaher, T. Comparing Landsat water index methods for automated water classification in eastern Australia. Remote Sensing of Environment 175, 167\u2013182, https:\/\/doi.org\/10.1016\/J.RSE.2015.12.055 (2016).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR70","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1109\/TGRS.2003.819190","volume":"41","author":"F Gerard","year":"2003","unstructured":"Gerard, F. et al. Forest Fire Scar Detection in the Boreal Forest with Multitemporal SPOT-VEGETATION Data. IEEE Transactions on Geoscience and Remote Sensing 41, 2575\u20132585, https:\/\/doi.org\/10.1109\/TGRS.2003.819190 (2003).","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"2096_CR71","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/0034-4257(87)90088-5","volume":"22","author":"AR Gillespie","year":"1987","unstructured":"Gillespie, A. R., Kahle, A. B. & Walker, R. E. Color enhancement of highly correlated images. II. Channel ratio and \u201cchromaticity\u201d transformation techniques. Remote Sensing of Environment 22, 343\u2013365, https:\/\/doi.org\/10.1016\/0034-4257(87)90088-5 (1987).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR72","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/1011-1344(93)06963-4","volume":"22","author":"A Gitelson","year":"1994","unstructured":"Gitelson, A. & Merzlyak, M. N. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology 22, 247\u2013252, https:\/\/doi.org\/10.1016\/1011-1344(93)06963-4 (1994).","journal-title":"Journal of Photochemistry and Photobiology B: Biology"},{"key":"2096_CR73","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","volume":"143","author":"A Gitelson","year":"1994","unstructured":"Gitelson, A. & Merzlyak, M. N. Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. Journal of Plant Physiology 143, 286\u2013292, https:\/\/doi.org\/10.1016\/S0176-1617(11)81633-0 (1994).","journal-title":"Journal of Plant Physiology"},{"key":"2096_CR74","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","volume":"58","author":"AA Gitelson","year":"1996","unstructured":"Gitelson, A. A., Kaufman, Y. J. & Merzlyak, M. N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58, 289\u2013298, https:\/\/doi.org\/10.1016\/S0034-4257(96)00072-7 (1996).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR75","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/S0176-1617(96)80284-7","volume":"148","author":"AA Gitelson","year":"1996","unstructured":"Gitelson, A. A. & Merzlyak, M. N. Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll. Journal of Plant Physiology 148, 494\u2013500, https:\/\/doi.org\/10.1016\/S0176-1617(96)80284-7 (1996).","journal-title":"Journal of Plant Physiology"},{"key":"2096_CR76","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1562\/0031","volume":"74","author":"AA Gitelson","year":"2001","unstructured":"Gitelson, A. A., Merzlyak, M. N., Chivkunova & Olga, B. Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves. Photochemistry and Photobiology 74, 38\u201345, https:\/\/doi.org\/10.1562\/0031 (2001).","journal-title":"Photochemistry and Photobiology"},{"key":"2096_CR77","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","volume":"80","author":"AA Gitelson","year":"2002","unstructured":"Gitelson, A. A., Kaufman, Y. J., Stark, R. & Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 80, 76\u201387, https:\/\/doi.org\/10.1016\/S0034-4257(01)00289-9 (2002).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR78","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1078\/0176-1617-00887","volume":"160","author":"AA Gitelson","year":"2003","unstructured":"Gitelson, A. A., Gritz, Y. & Merzlyak, M. N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 160, 271\u2013282, https:\/\/doi.org\/10.1078\/0176-1617-00887 (2003).","journal-title":"Journal of Plant Physiology"},{"key":"2096_CR79","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1080\/02757259409532252","volume":"10","author":"NS Goel","year":"1994","unstructured":"Goel, N. S. & Qin, W. Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation. Remote Sensing Reviews 10, 309\u2013347, https:\/\/doi.org\/10.1080\/02757259409532252 (1994).","journal-title":"Remote Sensing Reviews"},{"key":"2096_CR80","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1109\/TGRS.2003.812910","volume":"41","author":"P Gong","year":"2003","unstructured":"Gong, P., Pu, R., Biging, G. S. & Larrieu, M. R. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 41, 1355\u20131362, https:\/\/doi.org\/10.1109\/TGRS.2003.812910 (2003).","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"2096_CR81","doi-asserted-by":"publisher","unstructured":"Gu, Y. et al. A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophysical Research Letters 34, https:\/\/doi.org\/10.1029\/2006GL029127 (2007).","DOI":"10.1029\/2006GL029127"},{"key":"2096_CR82","doi-asserted-by":"publisher","first-page":"5055","DOI":"10.3390\/S20185055","volume":"20","author":"Y Guo","year":"2020","unstructured":"Guo, Y. et al. Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV. Sensors 20, 5055, https:\/\/doi.org\/10.3390\/S20185055 (2020).","journal-title":"Sensors"},{"key":"2096_CR83","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","volume":"81","author":"D Haboudane","year":"2002","unstructured":"Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J. & Dextraze, L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81, 416\u2013426, https:\/\/doi.org\/10.1016\/S0034-4257(02)00018-4 (2002).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR84","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/J.RSE.2003.12.013","volume":"90","author":"D Haboudane","year":"2004","unstructured":"Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. & Strachan, I. B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337\u2013352, https:\/\/doi.org\/10.1016\/J.RSE.2003.12.013 (2004).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR85","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TGRS.2007.904836","volume":"46","author":"D Haboudane","year":"2008","unstructured":"Haboudane, D., Tremblay, N., Miller, J. R. & Vigneault, P. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 46, 423\u2013436, https:\/\/doi.org\/10.1109\/TGRS.2007.904836 (2008).","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"2096_CR86","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3390\/RS13010105","volume":"13","author":"J Han","year":"2020","unstructured":"Han, J., Zhang, Z. & Cao, J. Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1\/2. Remote Sensing 13, 105, https:\/\/doi.org\/10.3390\/RS13010105 (2020).","journal-title":"Remote Sensing"},{"key":"2096_CR87","doi-asserted-by":"publisher","first-page":"2547","DOI":"10.2135\/CROPSCI2007.01.0031","volume":"47","author":"DW Hancock","year":"2007","unstructured":"Hancock, D. W. & Dougherty, C. T. Relationships between Blue- and Red-based Vegetation Indices and Leaf Area and Yield of Alfalfa. Crop Science 47, 2547\u20132556, https:\/\/doi.org\/10.2135\/CROPSCI2007.01.0031 (2007).","journal-title":"Crop Science"},{"key":"2096_CR88","first-page":"77","volume":"49","author":"MA Hardisky","year":"1983","unstructured":"Hardisky, M. A., Klemas, V. & Smart, R. M. The Influence of Soil Salinity, Growth Form, and Leaf Moisture on-the Spectral Radiance of Spartina alterniflora Canopies. Photogrammetric Engineering and Remote Sensing 49, 77\u201383 (1983).","journal-title":"Photogrammetric Engineering and Remote Sensing"},{"key":"2096_CR89","doi-asserted-by":"publisher","first-page":"4801","DOI":"10.1080\/01431160500239008","volume":"26","author":"ZA Holden","year":"2005","unstructured":"Holden, Z. A., Smith, A. M., Morgan, P., Rollins, M. G. & Gessler, P. E. Evaluation of novel thermally enhanced spectral indices for mapping fire perimeters and comparisons with fire atlas data. International Journal of Remote Sensing 26, 4801\u20134808, https:\/\/doi.org\/10.1080\/01431160500239008 (2005).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR90","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","volume":"59","author":"AR Huete","year":"1997","unstructured":"Huete, A. R., Liu, H. Q., Batchily, K. & Van Leeuwen, W. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment 59, 440\u2013451, https:\/\/doi.org\/10.1016\/S0034-4257(96)00112-5 (1997).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR91","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","volume":"30","author":"ER Hunt","year":"1989","unstructured":"Hunt, E. R. & Rock, B. N. Detection of changes in leaf water content using Near- and Middle-Infrared reflectances. Remote Sensing of Environment 30, 43\u201354, https:\/\/doi.org\/10.1016\/0034-4257(89)90046-1 (1989).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR92","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/J.JAG.2012.07.020","volume":"21","author":"ER Hunt","year":"2013","unstructured":"Hunt, E. R. et al. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation 21, 103\u2013112, https:\/\/doi.org\/10.1016\/J.JAG.2012.07.020 (2013).","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"2096_CR93","doi-asserted-by":"publisher","first-page":"3833","DOI":"10.1016\/J.RSE.2008.06.006","volume":"112","author":"Z Jiang","year":"2008","unstructured":"Jiang, Z., Huete, A. R., Didan, K. & Miura, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112, 3833\u20133845, https:\/\/doi.org\/10.1016\/J.RSE.2008.06.006 (2008).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR94","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1080\/17538947.2018.1495770","volume":"12","author":"H Jiang","year":"2018","unstructured":"Jiang, H. et al. A shadow- eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains. International Journal of Digital Earth 12, 1013\u20131029, https:\/\/doi.org\/10.1080\/17538947.2018.1495770 (2018).","journal-title":"International Journal of Digital Earth"},{"key":"2096_CR95","doi-asserted-by":"publisher","first-page":"663","DOI":"10.2307\/1936256","volume":"50","author":"CF Jordan","year":"1969","unstructured":"Jordan, C. F. Derivation of Leaf-Area Index from Quality of Light on the Forest Floor. Ecology 50, 663\u2013666, https:\/\/doi.org\/10.2307\/1936256 (1969).","journal-title":"Ecology"},{"key":"2096_CR96","doi-asserted-by":"publisher","first-page":"3583","DOI":"10.1080\/014311697216810","volume":"18","author":"C Jurgens","year":"1997","unstructured":"Jurgens, C. The modified normalized difference vegetation index (mNDVI) a new index to determine frost damages in agriculture based on Landsat TM data. International Journal of Remote Sensing 18, 3583\u20133594, https:\/\/doi.org\/10.1080\/014311697216810 (1997).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR97","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/S0034-4257(01)00190-0","volume":"77","author":"A Karnieli","year":"2001","unstructured":"Karnieli, A., Kaufman, Y. J., Remer, L. & Wald, A. AFRI\u2013aerosol free vegetation index. Remote Sensing of Environment 77, 10\u201321, https:\/\/doi.org\/10.1016\/S0034-4257(01)00190-0 (2001).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR98","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1109\/36.134076","volume":"30","author":"YJ Kaufman","year":"1992","unstructured":"Kaufman, Y. J. & Tanr\u00e9, D. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing 30, 261\u2013270, https:\/\/doi.org\/10.1109\/36.134076 (1992).","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"2096_CR99","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1006\/ANBO.1997.0544","volume":"81","author":"S Kawashima","year":"1998","unstructured":"Kawashima, S. & Nakatani, M. An Algorithm for Estimating Chlorophyll Content in Leaves Using a Video Camera. Annals of Botany 81, 49\u201354, https:\/\/doi.org\/10.1006\/ANBO.1997.0544 (1998).","journal-title":"Annals of Botany"},{"key":"2096_CR100","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1080\/01431160802385459","volume":"30","author":"AK Keshri","year":"2008","unstructured":"Keshri, A. K., Shukla, A. & Gupta, R. P. ASTER ratio indices for supraglacial terrain mapping. International Journal of Remote Sensing 30, 519\u2013524, https:\/\/doi.org\/10.1080\/01431160802385459 (2008).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR101","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1109\/IGARSS.2001.976856","volume":"3","author":"Y Kim","year":"2001","unstructured":"Kim, Y. & Van Zyl, J. Comparison of forest parameter estimation techniques using SAR data. International Geoscience and Remote Sensing Symposium (IGARSS) 3, 1395\u20131397, https:\/\/doi.org\/10.1109\/IGARSS.2001.976856 (2001).","journal-title":"International Geoscience and Remote Sensing Symposium (IGARSS)"},{"key":"2096_CR102","doi-asserted-by":"publisher","first-page":"15969","DOI":"10.3390\/RS71215805","volume":"7","author":"Y Kwak","year":"2015","unstructured":"Kwak, Y. et al. Prompt Proxy Mapping of Flood Damaged Rice Fields Using MODIS-Derived Indices. Remote Sensing 7, 15969\u201315988, https:\/\/doi.org\/10.3390\/RS71215805 (2015).","journal-title":"Remote Sensing"},{"key":"2096_CR103","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/J.RSE.2006.07.012","volume":"106","author":"JP Lacaux","year":"2007","unstructured":"Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A. & Lafaye, M. Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment 106, 66\u201374, https:\/\/doi.org\/10.1016\/J.RSE.2006.07.012 (2007).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR104","doi-asserted-by":"publisher","first-page":"249","DOI":"10.3390\/RS9030249","volume":"9","author":"H Li","year":"2017","unstructured":"Li, H. et al. Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index. Remote Sensing 9, 249, https:\/\/doi.org\/10.3390\/RS9030249 (2017).","journal-title":"Remote Sensing"},{"key":"2096_CR105","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1080\/22797254.2020.1738900","volume":"53","author":"S Liu","year":"2020","unstructured":"Liu, S., Zheng, Y., Dalponte, M. & Tong, X. A novel fire index-based burned area change detection approach using Landsat-8 OLI data. European Journal of Remote Sensing 53, 104\u2013112, https:\/\/doi.org\/10.1080\/22797254.2020.1738900 (2020).","journal-title":"European Journal of Remote Sensing"},{"key":"2096_CR106","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1080\/10106040108542184","volume":"16","author":"M Louhaichi","year":"2001","unstructured":"Louhaichi, M., Borman, M. M. & Johnson, D. E. Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat. Geocarto International 16, 65\u201370, https:\/\/doi.org\/10.1080\/10106040108542184 (2001).","journal-title":"Geocarto International"},{"key":"2096_CR107","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1080\/01431169008955053","volume":"11","author":"DJ Major","year":"1990","unstructured":"Major, D. J., Baret, F. & Guyot, G. A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing 11, 727\u2013740, https:\/\/doi.org\/10.1080\/01431169008955053 (1990).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR108","doi-asserted-by":"publisher","first-page":"S221","DOI":"10.1016\/J.FORECO.2006.08.248","volume":"234","author":"MP Mart\u00edn","year":"2006","unstructured":"Mart\u00edn, M. P., G\u00f3mez, I. & Chuvieco, E. Burnt Area Index (BAIM) for burned area discrimination at regional scale using MODIS data. Forest Ecology and Management 234, S221, https:\/\/doi.org\/10.1016\/J.FORECO.2006.08.248 (2006).","journal-title":"Forest Ecology and Management"},{"key":"2096_CR109","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/S0034-4257(98)00030-3","volume":"66","author":"R Mathieu","year":"1998","unstructured":"Mathieu, R., Pouget, M., Cervelle, B. & Escadafal, R. Relationships between Satellite-Based Radiometric Indices Simulated Using Laboratory Reflectance Data and Typic Soil Color of an Arid Environment. Remote Sensing of Environment 66, 17\u201328, https:\/\/doi.org\/10.1016\/S0034-4257(98)00030-3 (1998).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR110","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1034\/J.1399-3054.1999.106119.X","volume":"106","author":"MN Merzlyak","year":"1999","unstructured":"Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B. & Rakitin, V. Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum 106, 135\u2013141, https:\/\/doi.org\/10.1034\/J.1399-3054.1999.106119.X (1999).","journal-title":"Physiologia Plantarum"},{"key":"2096_CR111","doi-asserted-by":"publisher","unstructured":"Meyer, G. E., Hindman, T. W. & Laksmi, K. Machine vision detection parameters for plant species identification. In Proc. SPIE 3543, Precision Agriculture and Biological Quality, vol. 3543, 327\u2013335, https:\/\/doi.org\/10.1117\/12.336896 (SPIE, 1999).","DOI":"10.1117\/12.336896"},{"key":"2096_CR112","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/J.COMPAG.2008.03.009","volume":"63","author":"GE Meyer","year":"2008","unstructured":"Meyer, G. E. & Neto, J. C. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture 63, 282\u2013293, https:\/\/doi.org\/10.1016\/J.COMPAG.2008.03.009 (2008).","journal-title":"Computers and Electronics in Agriculture"},{"key":"2096_CR113","unstructured":"Milczarek, M., Robak, A. & Gadawska, A. Sentinel Water Mask (SWM) - New Index for Water Detection On Sentinel-2 Images. Tech. Rep., Crisis Information Centre at Space Research Centre, Polish Academy of Sciences (2017)."},{"key":"2096_CR114","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/J.RSE.2011.10.016","volume":"117","author":"S Mishra","year":"2012","unstructured":"Mishra, S. & Mishra, D. R. Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll-a concentration in turbid productive waters. Remote Sensing of Environment 117, 394\u2013406, https:\/\/doi.org\/10.1016\/J.RSE.2011.10.016 (2012).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR115","doi-asserted-by":"publisher","first-page":"179","DOI":"10.5194\/BG-9-179-2012","volume":"9","author":"ET Mitchard","year":"2012","unstructured":"Mitchard, E. T. et al. Mapping tropical forest biomass with radar and spaceborne LiDAR in Lop\u00e9 National Park, Gabon: Overcoming problems of high biomass and persistent cloud. Biogeosciences 9, 179\u2013191, https:\/\/doi.org\/10.5194\/BG-9-179-2012 (2012).","journal-title":"Biogeosciences"},{"key":"2096_CR116","doi-asserted-by":"publisher","first-page":"655","DOI":"10.3390\/APP9040655","volume":"9","author":"R Nasirzadehdizaji","year":"2019","unstructured":"Nasirzadehdizaji, R. et al. Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage. Applied Sciences 9, 655, https:\/\/doi.org\/10.3390\/APP9040655 (2019).","journal-title":"Applied Sciences"},{"key":"2096_CR117","doi-asserted-by":"publisher","first-page":"904","DOI":"10.3390\/S19040904","volume":"19","author":"N Pasqualotto","year":"2019","unstructured":"Pasqualotto, N., Delegido, J., Van Wittenberghe, S., Rinaldi, M. & Moreno, J. Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI). Sensors 19, 904, https:\/\/doi.org\/10.3390\/S19040904 (2019).","journal-title":"Sensors"},{"key":"2096_CR118","first-page":"221","volume":"31","author":"J Pe\u00f1uelas","year":"1995","unstructured":"Pe\u00f1uelas, J., Baret, F. & Filella, I. Semi-empirical indices to assess carotenoids\/chlorophyll alpha ratio from leaf spectral reflectance. Photosynthetica 31, 221\u2013230 (1995).","journal-title":"Photosynthetica"},{"key":"2096_CR119","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1016\/J.RSE.2018.09.003","volume":"217","author":"S Periasamy","year":"2018","unstructured":"Periasamy, S. Significance of dual polarimetric synthetic aperture radar in biomass retrieval: An attempt on Sentinel-1. Remote Sensing of Environment 217, 537\u2013549, https:\/\/doi.org\/10.1016\/J.RSE.2018.09.003 (2018).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR120","first-page":"495","volume":"65","author":"JE Pinder III","year":"1999","unstructured":"Pinder, J. E. III & McLeod, K. W. Indications of Relative Drought Stress in Longleaf Pine from Thematic Mapper Data. Photogrammetric Engineering and Remote Sensing 65, 495\u2013501 (1999).","journal-title":"Photogrammetric Engineering and Remote Sensing"},{"key":"2096_CR121","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/BF00031911","volume":"101","author":"B Pinty","year":"1992","unstructured":"Pinty, B. & Verstraete, M. M. GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio 101, 15\u201320, https:\/\/doi.org\/10.1007\/BF00031911 (1992).","journal-title":"Vegetatio"},{"key":"2096_CR122","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","volume":"48","author":"J Qi","year":"1994","unstructured":"Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. & Sorooshian, S. A modified soil adjusted vegetation index. Remote Sensing of Environment 48, 119\u2013126, https:\/\/doi.org\/10.1016\/0034-4257(94)90134-1 (1994).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR123","doi-asserted-by":"publisher","first-page":"105030","DOI":"10.1016\/J.ENVSOFT.2021.105030","volume":"140","author":"AM Rad","year":"2021","unstructured":"Rad, A. M., Kreitler, J. & Sadegh, M. Augmented Normalized Difference Water Index for improved surface water monitoring. Environmental Modelling and Software 140, 105030, https:\/\/doi.org\/10.1016\/J.ENVSOFT.2021.105030 (2021).","journal-title":"Environmental Modelling and Software"},{"key":"2096_CR124","first-page":"39","volume":"43","author":"A Rikimaru","year":"2002","unstructured":"Rikimaru, A., Roy, P. S. & Miyatake, S. Tropical forest cover density mapping. Tropical Ecology 43, 39\u201347 (2002).","journal-title":"Tropical Ecology"},{"key":"2096_CR125","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","volume":"55","author":"G Rondeaux","year":"1996","unstructured":"Rondeaux, G., Steven, M. & Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 55, 95\u2013107, https:\/\/doi.org\/10.1016\/0034-4257(95)00186-7 (1996).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR126","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","volume":"51","author":"JL Roujean","year":"1995","unstructured":"Roujean, J. L. & Breon, F. M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment 51, 375\u2013384, https:\/\/doi.org\/10.1016\/0034-4257(94)00114-3 (1995).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR127","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/J.JAG.2014.03.018","volume":"32","author":"MM Saberioon","year":"2014","unstructured":"Saberioon, M. M. et al. Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation 32, 35\u201345, https:\/\/doi.org\/10.1016\/J.JAG.2014.03.018 (2014).","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"2096_CR128","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3178\/JJSHWR.12.28","volume":"12","author":"A Saito","year":"1999","unstructured":"Saito, A. & Yamazaki, E. Characteristics of Spectral Reflectance for Vegetation Ground Surfaces with Snow-cover; Vegetation Indices and Snow Indices. Journal of Japan Society of Hydrology and Water Resources 12, 28\u201338, https:\/\/doi.org\/10.3178\/JJSHWR.12.28 (1999).","journal-title":"Journal of Japan Society of Hydrology and Water Resources"},{"key":"2096_CR129","doi-asserted-by":"publisher","unstructured":"Shen, L. & Li, C. Water body extraction from Landsat ETM+ imagery using adaboost algorithm. In 18th International Conference on Geoinformatics, Geoinformatics 2010, 1\u20134, https:\/\/doi.org\/10.1109\/GEOINFORMATICS.2010.5567762 (2010).","DOI":"10.1109\/GEOINFORMATICS.2010.5567762"},{"key":"2096_CR130","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","volume":"81","author":"DA Sims","year":"2002","unstructured":"Sims, D. A. & Gamon, J. A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 81, 337\u2013354, https:\/\/doi.org\/10.1016\/S0034-4257(02)00010-X (2002).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR131","doi-asserted-by":"publisher","first-page":"1886","DOI":"10.23953\/CLOUD.IJARSG.67","volume":"5","author":"P Sinha","year":"2016","unstructured":"Sinha, P., Verma, N. K. & Ayele, E. Urban Built-up Area Extraction and Change Detection of Adama Municipal Area using Time-Series Landsat Images. International Journal of Advanced Remote Sensing and GIS 5, 1886\u20131895, https:\/\/doi.org\/10.23953\/CLOUD.IJARSG.67 (2016).","journal-title":"International Journal of Advanced Remote Sensing and GIS"},{"key":"2096_CR132","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.2134\/AGRONJ2004.0314","volume":"97","author":"RP Sripada","year":"2005","unstructured":"Sripada, R. P., Heiniger, R. W., White, J. G. & Weisz, R. Aerial Color Infrared Photography for Determining Late-Season Nitrogen Requirements in Corn. Agronomy Journal 97, 1443\u20131451, https:\/\/doi.org\/10.2134\/AGRONJ2004.0314 (2005).","journal-title":"Agronomy Journal"},{"key":"2096_CR133","doi-asserted-by":"publisher","first-page":"6361","DOI":"10.1080\/01431161.2012.687842","volume":"33","author":"D Stathakis","year":"2012","unstructured":"Stathakis, D., Perakis, K. & Savin, I. Efficient segmentation of urban areas by the VIBI. International Journal of Remote Sensing 33, 6361\u20136377, https:\/\/doi.org\/10.1080\/01431161.2012.687842 (2012).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR134","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/J.RSE.2016.06.016","volume":"184","author":"JJ Sulik","year":"2016","unstructured":"Sulik, J. J. & Long, D. S. Spectral considerations for modeling yield of canola. Remote Sensing of Environment 184, 161\u2013174, https:\/\/doi.org\/10.1016\/J.RSE.2016.06.016 (2016).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR135","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.3390\/RS10101521","volume":"10","author":"Y Tian","year":"2018","unstructured":"Tian, Y., Chen, H., Song, Q. & Zheng, K. A Novel Index for Impervious Surface Area Mapping: Development and Validation. Remote Sensing 10, 1521, https:\/\/doi.org\/10.3390\/RS10101521 (2018).","journal-title":"Remote Sensing"},{"key":"2096_CR136","doi-asserted-by":"publisher","first-page":"2641","DOI":"10.1080\/01431160110053185","volume":"22","author":"S Trigg","year":"2001","unstructured":"Trigg, S. & Flasse, S. An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. International Journal of Remote Sensing 22, 2641\u20132647, https:\/\/doi.org\/10.1080\/01431160110053185 (2001).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR137","doi-asserted-by":"publisher","first-page":"514","DOI":"10.5589\/M12-043","volume":"38","author":"M Trudel","year":"2012","unstructured":"Trudel, M., Charbonneau, F. & Leconte, R. Using RADARSAT-2 polarimetric and ENVISAT-ASAR dual-polarization data for estimating soil moisture over agricultural fields. Canadian Journal of Remote Sensing 38, 514\u2013527, https:\/\/doi.org\/10.5589\/M12-043 (2012).","journal-title":"Canadian Journal of Remote Sensing"},{"key":"2096_CR138","unstructured":"USGS. Landsat Normalized Burn Ratio 2 | U.S. Geological Survey (2022)."},{"key":"2096_CR139","doi-asserted-by":"publisher","first-page":"2702","DOI":"10.1016\/J.RSE.2011.06.010","volume":"115","author":"S Veraverbeke","year":"2011","unstructured":"Veraverbeke, S., Harris, S. & Hook, S. Evaluating spectral indices for burned area discrimination using MODIS\/ASTER (MASTER) airborne simulator data. Remote Sensing of Environment 115, 2702\u20132709, https:\/\/doi.org\/10.1016\/J.RSE.2011.06.010 (2011).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR140","doi-asserted-by":"publisher","first-page":"2186","DOI":"10.3390\/RS11182186","volume":"11","author":"SP Via\u00f1a-Borja","year":"2019","unstructured":"Via\u00f1a-Borja, S. P. & Ortega-S\u00e1nchez, M. Automatic Methodology to Detect the Coastline from Landsat Images with a New Water Index Assessed on Three Different Spanish Mediterranean Deltas. Remote Sensing 11, 2186, https:\/\/doi.org\/10.3390\/RS11182186 (2019).","journal-title":"Remote Sensing"},{"key":"2096_CR141","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/S11119-008-9075-Z\/","volume":"9","author":"M Vincini","year":"2008","unstructured":"Vincini, M., Frazzi, E. & D\u2019Alessio, P. A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agriculture 9, 303\u2013319, https:\/\/doi.org\/10.1007\/S11119-008-9075-Z\/ (2008).","journal-title":"Precision Agriculture"},{"key":"2096_CR142","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/S11119-010-9204-3\/","volume":"12","author":"M Vincini","year":"2011","unstructured":"Vincini, M. & Frazzi, E. Comparing narrow and broad-band vegetation indices to estimate leaf chlorophyll content in planophile crop canopies. Precision Agriculture 12, 334\u2013344, https:\/\/doi.org\/10.1007\/S11119-010-9204-3\/ (2011).","journal-title":"Precision Agriculture"},{"key":"2096_CR143","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/S1672-6308(07)60027-4","volume":"14","author":"F-m Wang","year":"2007","unstructured":"Wang, F.-m, Huang, J.-f, Tang, Y.-l & Wang, X.-z New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice. Rice Science 14, 195\u2013203, https:\/\/doi.org\/10.1016\/S1672-6308(07)60027-4 (2007).","journal-title":"Rice Science"},{"key":"2096_CR144","doi-asserted-by":"publisher","unstructured":"Wang, L. & Qu, J. J. NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophysical Research Letters 34, https:\/\/doi.org\/10.1029\/2007GL031021 (2007).","DOI":"10.1029\/2007GL031021"},{"key":"2096_CR145","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/J.RSE.2017.04.031","volume":"196","author":"C Wang","year":"2017","unstructured":"Wang, C. et al. A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sensing of Environment 196, 1\u201312, https:\/\/doi.org\/10.1016\/J.RSE.2017.04.031 (2017).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR146","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.3390\/RS10101643","volume":"10","author":"Z Wang","year":"2018","unstructured":"Wang, Z., Liu, J., Li, J. & Zhang, D. D. Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2. Remote Sensing 10, 1643, https:\/\/doi.org\/10.3390\/RS10101643 (2018).","journal-title":"Remote Sensing"},{"key":"2096_CR147","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4172\/scientificreports.136","volume":"1","author":"MM Waqar","year":"2012","unstructured":"Waqar, M. M., Mirza, J. F., Mumtaz, R. & Hussain, E. Development of New Indices for Extraction of Built-Up Area and Bare Soil from Landsat. Data. Open Access Scientific Reports 1, 1\u20134, https:\/\/doi.org\/10.4172\/scientificreports.136 (2012).","journal-title":"Data. Open Access Scientific Reports"},{"key":"2096_CR148","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/S0034-4257(01)00318-2","volume":"80","author":"EH Wilson","year":"2002","unstructured":"Wilson, E. H. & Sader, S. A. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment 80, 385\u2013396, https:\/\/doi.org\/10.1016\/S0034-4257(01)00318-2 (2002).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR149","doi-asserted-by":"publisher","first-page":"259","DOI":"10.13031\/2013.27838","volume":"38","author":"DM Woebbecke","year":"1995","unstructured":"Woebbecke, D. M., Meyer, G. E., Von Bargen, K. & Mortensen, D. A. Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions. Transactions of the ASAE 38, 259\u2013269, https:\/\/doi.org\/10.13031\/2013.27838 (1995).","journal-title":"Transactions of the ASAE"},{"key":"2096_CR150","unstructured":"Wolf, A. Using WorldView 2 Vis-NIR MSI Imagery to Support Land Mapping and Feature Extraction Using Normalized Difference Index Ratios. Tech. Rep., Digital Globe (2010)."},{"key":"2096_CR151","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1016\/J.AGRFORMET.2008.03.005","volume":"148","author":"C Wu","year":"2008","unstructured":"Wu, C., Niu, Z., Tang, Q. & Huang, W. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology 148, 1230\u20131241, https:\/\/doi.org\/10.1016\/J.AGRFORMET.2008.03.005 (2008).","journal-title":"Agricultural and Forest Meteorology"},{"key":"2096_CR152","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.3390\/RS6021211","volume":"6","author":"W Wu","year":"2014","unstructured":"Wu, W. The Generalized Difference Vegetation Index (GDVI) for Dryland Characterization. Remote Sensing 6, 1211\u20131233, https:\/\/doi.org\/10.3390\/RS6021211 (2014).","journal-title":"Remote Sensing"},{"key":"2096_CR153","doi-asserted-by":"publisher","first-page":"2479","DOI":"10.1080\/01431160119766","volume":"22","author":"X Xiao","year":"2001","unstructured":"Xiao, X., Shen, Z. & Qin, X. Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: A Normalized Difference Snow and Ice Index. International Journal of Remote Sensing 22, 2479\u20132487, https:\/\/doi.org\/10.1080\/01431160119766 (2001).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR154","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1016\/J.RSE.2003.11.008","volume":"89","author":"X Xiao","year":"2004","unstructured":"Xiao, X. et al. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sensing of Environment 89, 519\u2013534, https:\/\/doi.org\/10.1016\/J.RSE.2003.11.008 (2004).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR155","doi-asserted-by":"publisher","unstructured":"Xing, N. et al. A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index. Remote Sensing 12, 16, https:\/\/doi.org\/10.3390\/RS12010016 (2019).","DOI":"10.3390\/RS12010016"},{"key":"2096_CR156","doi-asserted-by":"publisher","first-page":"4269","DOI":"10.1080\/01431160802039957","volume":"29","author":"H Xu","year":"2008","unstructured":"Xu, H. A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing 29, 4269\u20134276, https:\/\/doi.org\/10.1080\/01431160802039957 (2008).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR157","doi-asserted-by":"publisher","first-page":"557","DOI":"10.14358\/PERS.76.5.557","volume":"76","author":"H Xu","year":"2010","unstructured":"Xu, H. Analysis of impervious surface and its impact on Urban heat environment using the normalized difference impervious surface index (NDISI). Photogrammetric Engineering and Remote Sensing 76, 557\u2013565, https:\/\/doi.org\/10.14358\/PERS.76.5.557 (2010).","journal-title":"Photogrammetric Engineering and Remote Sensing"},{"key":"2096_CR158","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.3390\/W12051339","volume":"12","author":"D Yan","year":"2020","unstructured":"Yan, D., Huang, C., Ma, N. & Zhang, Y. Improved Landsat-Based Water and Snow Indices for Extracting Lake and Snow Cover\/Glacier in the Tibetan Plateau. Water 12, 1339, https:\/\/doi.org\/10.3390\/W12051339 (2020).","journal-title":"Water"},{"key":"2096_CR159","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/J.RSE.2019.03.028","volume":"228","author":"W Yang","year":"2019","unstructured":"Yang, W. et al. A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems. Remote Sensing of Environment 228, 31\u201344, https:\/\/doi.org\/10.1016\/J.RSE.2019.03.028 (2019).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR160","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/J.ISPRSJPRS.2019.06.012","volume":"154","author":"J Yue","year":"2019","unstructured":"Yue, J., Tian, J., Tian, Q., Xu, K. & Xu, N. Development of soil moisture indices from differences in water absorption between shortwave-infrared bands. ISPRS Journal of Photogrammetry and Remote Sensing 154, 216\u2013230, https:\/\/doi.org\/10.1016\/J.ISPRSJPRS.2019.06.012 (2019).","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"2096_CR161","first-page":"53","volume":"38","author":"R Zhang","year":"1996","unstructured":"Zhang, R., Rao, N. & Liao, K. Approach for a Vegetation Index Resistant to Atmospheric Effect. Acta Botanica Sinica 38, 53\u201362 (1996).","journal-title":"Acta Botanica Sinica"},{"key":"2096_CR162","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1109\/IGARSS.2005.1526319","volume":"3","author":"H Zhao","year":"2005","unstructured":"Zhao, H. & Chen, X. Use of normalized difference bareness index in quickly mapping bare areas from TM\/ETM+. International Geoscience and Remote Sensing Symposium (IGARSS) 3, 1666\u20131668, https:\/\/doi.org\/10.1109\/IGARSS.2005.1526319 (2005).","journal-title":"International Geoscience and Remote Sensing Symposium (IGARSS)"},{"key":"2096_CR163","doi-asserted-by":"publisher","first-page":"868","DOI":"10.3390\/S18030868","volume":"18","author":"Q Zheng","year":"2018","unstructured":"Zheng, Q., Huang, W., Cui, X., Shi, Y. & Liu, L. New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery. Sensors 18, 868, https:\/\/doi.org\/10.3390\/S18030868 (2018).","journal-title":"Sensors"},{"key":"2096_CR164","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/978-3-662-45737-5_51","volume":"482","author":"W Zhou","year":"2015","unstructured":"Zhou, W., Li, Z., Ji, S., Hua, C. & Fan, W. A New Index Model NDVI-MNDWI for Water Object Extraction in Hybrid Area. Communications in Computer and Information Science 482, 513\u2013519, https:\/\/doi.org\/10.1007\/978-3-662-45737-5_51 (2015).","journal-title":"Communications in Computer and Information Science"},{"key":"2096_CR165","doi-asserted-by":"publisher","first-page":"5289","DOI":"10.3390\/RS14215289","volume":"14","author":"L Niu","year":"2022","unstructured":"Niu, L. et al. Triangle Water Index (TWI): An Advanced Approach for More Accurate Detection and Delineation of Water Surfaces in Sentinel-2 Data. Remote Sensing 14, 5289, https:\/\/doi.org\/10.3390\/RS14215289 (2022).","journal-title":"Remote Sensing"},{"key":"2096_CR166","doi-asserted-by":"publisher","first-page":"13087","DOI":"10.1073\/PNAS.1606162113\/SUPPL_FILE\/PNAS.201606162SI.PDF","volume":"113","author":"JA Gamon","year":"2016","unstructured":"Gamon, J. A. et al. A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers. Proceedings of the National Academy of Sciences of the United States of America 113, 13087\u201313092, https:\/\/doi.org\/10.1073\/PNAS.1606162113\/SUPPL_FILE\/PNAS.201606162SI.PDF (2016).","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"2096_CR167","unstructured":"Griffith, C. Box: Python dictionaries with advanced dot notation access (2022)."},{"key":"2096_CR168","doi-asserted-by":"publisher","author":"D Montero","year":"2022","unstructured":"Montero, D. et al. Awesome Spectral Indices. Zenodo https:\/\/doi.org\/10.5281\/zenodo.7424467 (2022).","DOI":"10.5281\/zenodo.7424467"},{"key":"2096_CR169","unstructured":"Colvin, S. pydantic: Data parsing and validation using Python type hints (2021)."},{"key":"2096_CR170","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris, C. R. et al. Array programming with NumPy. Nature 585, 357\u2013362, https:\/\/doi.org\/10.1038\/s41586-020-2649-2 (2020).","journal-title":"Nature"},{"key":"2096_CR171","doi-asserted-by":"publisher","author":"J Reback","year":"2022","unstructured":"Reback, J. et al. pandas-dev\/pandas: Pandas 1.4.1. Zenodo https:\/\/doi.org\/10.5281\/ZENODO.6053272 (2022).","DOI":"10.5281\/ZENODO.6053272"},{"key":"2096_CR172","doi-asserted-by":"publisher","author":"K Jordahl","year":"2021","unstructured":"Jordahl, K. et al. geopandas\/geopandas: v0.10.2. Zenodo\u00a0https:\/\/doi.org\/10.5281\/ZENODO.5573592 (2021).","DOI":"10.5281\/ZENODO.5573592"},{"key":"2096_CR173","doi-asserted-by":"publisher","first-page":"3168","DOI":"10.21105\/joss.03168","volume":"6","author":"D Montero","year":"2021","unstructured":"Montero, D. eemont: A Python package that extends Google Earth Engine. Journal of Open Source Software 6, 3168, https:\/\/doi.org\/10.21105\/joss.03168 (2021).","journal-title":"Journal of Open Source Software"},{"key":"2096_CR174","doi-asserted-by":"publisher","first-page":"301","DOI":"10.5194\/ISPRS-ARCHIVES-XLVIII-4-W1-2022-301-2022","volume":"XLVIII-4-W","author":"D Montero","year":"2022","unstructured":"Montero, D., Aybar, C., Mahecha, M. D. & Wieneke, S. spectral: Awesome Spectral Indices deployed via the Google Earth Engine JavaScript API. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4-W, 301\u2013306, https:\/\/doi.org\/10.5194\/ISPRS-ARCHIVES-XLVIII-4-W1-2022-301-2022 (2022).","journal-title":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"2096_CR175","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/S0168-1923(03)00115-1","volume":"118","author":"A Knohl","year":"2003","unstructured":"Knohl, A., Schulze, E. D., Kolle, O. & Buchmann, N. Large carbon uptake by an unmanaged 250-year-old deciduous forest in Central Germany. Agricultural and Forest Meteorology 118, 151\u2013167, https:\/\/doi.org\/10.1016\/S0168-1923(03)00115-1 (2003).","journal-title":"Agricultural and Forest Meteorology"},{"key":"2096_CR176","doi-asserted-by":"publisher","author":"A Knohl","year":"2021","unstructured":"Knohl, A. et al. FLUXNET2015 DE-Hai Hainich. FluxNet; University of Goettingen, Bioclimatology https:\/\/doi.org\/10.18140\/FLX\/1440148 (2012).","DOI":"10.18140\/FLX\/1440148"},{"key":"2096_CR177","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.rse.2016.01.023","volume":"176","author":"DP Roy","year":"2016","unstructured":"Roy, D. P. et al. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sensing of Environment 176, 255\u2013271, https:\/\/doi.org\/10.1016\/j.rse.2016.01.023 (2016).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR178","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.rse.2017.06.019","volume":"199","author":"DP Roy","year":"2017","unstructured":"Roy, D. P. et al. Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance. Remote Sensing of Environment 199, 25\u201338, https:\/\/doi.org\/10.1016\/j.rse.2017.06.019 (2017).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR179","doi-asserted-by":"publisher","unstructured":"Roy, D. P., Li, Z. & Zhang, H. K. Adjustment of sentinel-2 multi-spectral instrument (MSI) red-edge band reflectance to nadir BRDF adjusted reflectance (NBAR) and quantification of red-edge band BRDF effects. Remote Sensing 9, https:\/\/doi.org\/10.3390\/rs9121325 (2017).","DOI":"10.3390\/rs9121325"},{"key":"2096_CR180","doi-asserted-by":"publisher","first-page":"4100","DOI":"10.3390\/RS13204100","volume":"13","author":"M Domnich","year":"2021","unstructured":"Domnich, M. et al. KappaMask: AI-Based Cloudmask Processor for Sentinel-2. Remote Sensing 13, 4100, https:\/\/doi.org\/10.3390\/RS13204100 (2021).","journal-title":"Remote Sensing"},{"key":"2096_CR181","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","volume":"118","author":"Z Zhu","year":"2012","unstructured":"Zhu, Z. & Woodcock, C. E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment 118, 83\u201394, https:\/\/doi.org\/10.1016\/j.rse.2011.10.028 (2012).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR182","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","volume":"159","author":"Z Zhu","year":"2015","unstructured":"Zhu, Z., Wang, S. & Woodcock, C. E. Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sensing of Environment 159, 269\u2013277, https:\/\/doi.org\/10.1016\/j.rse.2014.12.014 (2015).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR183","doi-asserted-by":"publisher","first-page":"4409412","DOI":"10.1109\/TGRS.2022.3152272","volume":"60","author":"DE Pabon-Moreno","year":"2022","unstructured":"Pabon-Moreno, D. E., Migliavacca, M., Reichstein, M. & Mahecha, M. D. On the potential of Sentinel-2 for estimating Gross Primary Production. IEEE Transactions on Geoscience and Remote Sensing 60, 4409412, https:\/\/doi.org\/10.1109\/TGRS.2022.3152272 (2022).","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"2096_CR184","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1080\/01431169308953986","volume":"14","author":"JE Vogelmann","year":"1993","unstructured":"Vogelmann, J. E., Rock, B. N. & Moss, D. M. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14, 1563\u20131575, https:\/\/doi.org\/10.1080\/01431169308953986 (1993).","journal-title":"International Journal of Remote Sensing"},{"key":"2096_CR185","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/0034-4257(94)90125-2","volume":"47","author":"JE McMurtrey","year":"1994","unstructured":"McMurtrey, J. E., Chappelle, E. W., Kim, M. S., Meisinger, J. J. & Corp, L. A. Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sensing of Environment 47, 36\u201344, https:\/\/doi.org\/10.1016\/0034-4257(94)90125-2 (1994).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR186","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/S0034-4257(02)00127-X","volume":"84","author":"LC Rowan","year":"2003","unstructured":"Rowan, L. C. & Mars, J. C. Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Remote Sensing of Environment 84, 350\u2013366, https:\/\/doi.org\/10.1016\/S0034-4257(02)00127-X (2003).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR187","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1016\/j.rse.2015.07.015","volume":"168","author":"G Tramontana","year":"2015","unstructured":"Tramontana, G., Ichii, K., Camps-Valls, G., Tomelleri, E. & Papale, D. Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data. Remote Sensing of Environment 168, 360\u2013373, https:\/\/doi.org\/10.1016\/j.rse.2015.07.015 (2015).","journal-title":"Remote Sensing of Environment"},{"key":"2096_CR188","doi-asserted-by":"publisher","first-page":"4291","DOI":"10.5194\/bg-13-4291-2016","volume":"13","author":"G Tramontana","year":"2016","unstructured":"Tramontana, G. et al. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13, 4291\u20134313, https:\/\/doi.org\/10.5194\/bg-13-4291-2016 (2016).","journal-title":"Biogeosciences"},{"key":"2096_CR189","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.5194\/bg-17-1343-2020","volume":"17","author":"M Jung","year":"2020","unstructured":"Jung, M. et al. Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences 17, 1343\u20131365, https:\/\/doi.org\/10.5194\/bg-17-1343-2020 (2020).","journal-title":"Biogeosciences"},{"key":"2096_CR190","doi-asserted-by":"publisher","unstructured":"Emmanuel Johnson, J., Laparra, V., P\u00e9rez-Suay, A., Mahecha, M. D. & Camps-Valls, G. Kernel methods and their derivatives: Concept and perspectives for the earth system sciences. PLoS ONE 15, https:\/\/doi.org\/10.1371\/journal.pone.0235885 (2020).","DOI":"10.1371\/journal.pone.0235885"},{"key":"2096_CR191","doi-asserted-by":"publisher","first-page":"054034","DOI":"10.1088\/1748-9326\/AC6762","volume":"17","author":"J Cort\u00e9s-Andr\u00e9s","year":"2022","unstructured":"Cort\u00e9s-Andr\u00e9s, J. et al. Physics-aware nonparametric regression models for Earth data analysis. Environmental Research Letters 17, 054034, https:\/\/doi.org\/10.1088\/1748-9326\/AC6762 (2022).","journal-title":"Environmental Research Letters"},{"key":"2096_CR192","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825\u20132830 (2011).","journal-title":"Journal of Machine Learning Research"},{"issue":"July","key":"2096_CR193","doi-asserted-by":"publisher","first-page":"7640","DOI":"10.1109\/IGARSS.2018.8518007","volume":"2018","author":"M Reichstein","year":"2018","unstructured":"Reichstein, M. et al. Modelling landsurface time-series with recurrent neural nets. International Geoscience and Remote Sensing Symposium (IGARSS) 2018(July), 7640\u20137643, https:\/\/doi.org\/10.1109\/IGARSS.2018.8518007 (2018).","journal-title":"International Geoscience and Remote Sensing Symposium (IGARSS)"},{"issue":"Janua","key":"2096_CR194","first-page":"802","volume":"2015","author":"X Shi","year":"2015","unstructured":"Shi, X. et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems 2015(Janua), 802\u2013810 (2015).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2096_CR195","unstructured":"Abadi, M. et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. Tech. Rep., Google Research (2015)."},{"key":"2096_CR196","doi-asserted-by":"publisher","unstructured":"Paszke, A. et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32, https:\/\/doi.org\/10.48550\/arxiv.1912.01703 (2019).","DOI":"10.48550\/arxiv.1912.01703"},{"key":"2096_CR197","doi-asserted-by":"crossref","unstructured":"Lovelace, R., Nowosad, J. & Muenchow, J. Geocomputation with R (Chapman and Hall\/CRC, 2019).","DOI":"10.1201\/9780203730058"},{"key":"2096_CR198","doi-asserted-by":"publisher","first-page":"65","DOI":"10.48550\/arxiv.1411.1607","volume":"59","author":"J Bezanson","year":"2014","unstructured":"Bezanson, J., Edelman, A., Karpinski, S. & Shah, V. B. Julia: A Fresh Approach to Numerical Computing. SIAM Review 59, 65\u201398, https:\/\/doi.org\/10.48550\/arxiv.1411.1607 (2014).","journal-title":"SIAM Review"},{"key":"2096_CR199","doi-asserted-by":"publisher","first-page":"692","DOI":"10.21105\/JOSS.00692","volume":"3","author":"J Hoffimann","year":"2018","unstructured":"Hoffimann, J. GeoStats.jl\u2013High-performance geostatistics in Julia. Journal of Open Source Software 3, 692, https:\/\/doi.org\/10.21105\/JOSS.00692 (2018).","journal-title":"Journal of Open Source Software"},{"key":"2096_CR200","doi-asserted-by":"publisher","first-page":"602","DOI":"10.21105\/JOSS.00602","volume":"3","author":"M Innes","year":"2018","unstructured":"Innes, M. Flux: Elegant machine learning with Julia. Journal of Open Source Software 3, 602, https:\/\/doi.org\/10.21105\/JOSS.00602 (2018).","journal-title":"Journal of Open Source Software"},{"key":"2096_CR201","doi-asserted-by":"publisher","author":"MJ Innes","year":"2018","unstructured":"Innes, M. J. et al. Fashionable Modelling with Flux. CoRR https:\/\/doi.org\/10.48550\/arxiv.1811.01457 (2018).","journal-title":"CoRR","DOI":"10.48550\/arxiv.1811.01457"},{"key":"2096_CR202","doi-asserted-by":"publisher","author":"C Rackauckas","year":"2019","unstructured":"Rackauckas, C. et al. DiffEqFlux.jl - A Julia Library for Neural Differential Equations. CoRR https:\/\/doi.org\/10.48550\/arxiv.1902.02376 (2019).","journal-title":"CoRR","DOI":"10.48550\/arxiv.1902.02376"},{"key":"2096_CR203","doi-asserted-by":"publisher","author":"K Zubov","year":"2021","unstructured":"Zubov, K. et al. NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations. CoRR https:\/\/doi.org\/10.48550\/arxiv.2107.09443 (2021).","journal-title":"CoRR","DOI":"10.48550\/arxiv.2107.09443"},{"key":"2096_CR204","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arxiv.2204.05117","volume":"23","author":"F Martinuzzi","year":"2022","unstructured":"Martinuzzi, F., Rackauckas, C., Abdelrehim, A., Mahecha, M. D. & Mora, K. ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models. Journal of Machine Learning Research 23, 1\u20138, https:\/\/doi.org\/10.48550\/arxiv.2204.05117 (2022).","journal-title":"Journal of Machine Learning Research"},{"key":"2096_CR205","doi-asserted-by":"publisher","author":"D Montero","year":"2022","unstructured":"Montero, D. et al. Awesome Spectral Indices. Zenodo\u00a0https:\/\/doi.org\/10.5281\/zenodo.5508090 (2022).","DOI":"10.5281\/zenodo.5508090"}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41597-023-02096-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-023-02096-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-023-02096-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,8]],"date-time":"2023-04-08T13:08:10Z","timestamp":1680959290000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41597-023-02096-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,8]]},"references-count":205,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["2096"],"URL":"http:\/\/dx.doi.org\/10.1038\/s41597-023-02096-0","relation":{"references":[{"id-type":"doi","id":"10.5281\/zenodo.7424467","asserted-by":"subject"},{"id-type":"doi","id":"10.5281\/ZENODO.6053272","asserted-by":"subject"},{"id-type":"doi","id":"10.5281\/ZENODO.5573592","asserted-by":"subject"},{"id-type":"doi","id":"10.18140\/FLX\/1440148","asserted-by":"subject"},{"id-type":"doi","id":"10.5281\/zenodo.5508090","asserted-by":"subject"}]},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":["Library and Information Sciences","Statistics, Probability and Uncertainty","Computer Science Applications","Education","Information Systems","Statistics and Probability"],"published":{"date-parts":[[2023,4,8]]},"assertion":[{"value":"13 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"197"}}