{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:45:53Z","timestamp":1772793953882,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"USDA Choptank River Conservation Effects Assessment Project (CEAP)"},{"name":"USGS Land Change Science Program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012\u20132013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha\u22121, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (\u221225.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.<\/jats:p>","DOI":"10.3390\/s24072339","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T06:04:58Z","timestamp":1712556298000},"page":"2339","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits"],"prefix":"10.3390","volume":"24","author":[{"given":"Alison","family":"Thieme","sequence":"first","affiliation":[{"name":"Sustainable Agricultural Systems Laboratory, U.S. Department of Agriculture-Agricultural Research Service, Bldg 001, BARC-W, 10300 Baltimore Avenue, Beltsville, MD 20705, USA"}]},{"given":"Kusuma","family":"Prabhakara","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, 2181 Samuel J. LeFrak Hall, College Park, MD 20742, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9650-6537","authenticated-orcid":false,"given":"Jyoti","family":"Jennewein","sequence":"additional","affiliation":[{"name":"Sustainable Agricultural Systems Laboratory, U.S. Department of Agriculture-Agricultural Research Service, Bldg 001, BARC-W, 10300 Baltimore Avenue, Beltsville, MD 20705, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7957-5488","authenticated-orcid":false,"given":"Brian T.","family":"Lamb","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, 2045 Route 112, Bldg 4, Coram, NY 11727, USA"}]},{"given":"Greg W.","family":"McCarty","sequence":"additional","affiliation":[{"name":"Hydrology and Remote Sensing Laboratory, U.S. Department of Agriculture-Agricultural Research Service, Bldg 007, BARC-W, 10300 Baltimore Avenue, Beltsville, MD 20705, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5383-8064","authenticated-orcid":false,"given":"Wells Dean","family":"Hively","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, Bldg 001, BARC-W, 10300 Baltimore Avenue, Beltsville, MD 20705, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,6]]},"reference":[{"key":"ref_1","unstructured":"Maryland Department of Agriculture (2017). MACS 2017 Annual Report."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"362","DOI":"10.2489\/jswc.75.3.362","article-title":"Estimating the Effect of Winter Cover Crops on Nitrogen Leaching Using Cost-Share Enrollment Data, Satellite Remote Sensing, and Soil and Water Assessment Tool (SWAT) Modeling","volume":"75","author":"Hively","year":"2020","journal-title":"J. Soil Water Conserv."},{"key":"ref_3","first-page":"793","article-title":"Effects of Cover Crops on Groundwater Quality","volume":"266","author":"Meisinger","year":"1991","journal-title":"Cover Crop. Clean Water Soil Water Conserv. Soc. Ankeny Iowa"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1081\/CSS-100104110","article-title":"Using Winter Cover Crops to Improve Soil and Water Quality","volume":"32","author":"Dabney","year":"2001","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jian, J., Du, X., Reiter, M.S., and Stewart, R.D. (2020). A Meta-Analysis of Global Cropland Soil Carbon Changes Due to Cover Cropping. Soil Biol. Biochem., 143.","DOI":"10.1016\/j.soilbio.2020.107735"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.agee.2014.10.024","article-title":"Carbon Sequestration in Agricultural Soils via Cultivation of Cover Crops\u2013A Meta-Analysis","volume":"200","author":"Poeplau","year":"2015","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.still.2019.04.020","article-title":"Regulation of Soil CO2 and N2O Emissions by Cover Crops: A Meta-Analysis","volume":"192","author":"Muhammad","year":"2019","journal-title":"Soil Tillage Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ator, S.W., and Denver, J.M. (2015). Understanding the Nutrients in the Chesapeake Bay Watershed and Implications for Management and Restoration: The Eastern Shore, Circular 1406.","DOI":"10.3133\/cir1406"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/1353227","article-title":"Relationships between Benthic Community Condition, Water Quality, Sediment Quality, Nutrient Loads, and Land Use Patterns in Chesapeake Bay","volume":"23","author":"Dauer","year":"2000","journal-title":"Estuaries"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.ecolecon.2015.07.033","article-title":"Pay for Performance: Optimizing Public Investments in Agricultural Best Management Practices in the Chesapeake Bay Watershed","volume":"118","author":"Talberth","year":"2015","journal-title":"Ecol. Econ."},{"key":"ref_11","unstructured":"(2023, October 01). USDA NRCS. Environmental Quality Incentives Program (EQIP) Fact Sheet, Available online: https:\/\/www.nrcs.usda.gov\/sites\/default\/files\/2022-10\/EQIP-fact-sheet.pdf."},{"key":"ref_12","first-page":"1","article-title":"Government Programs That Support Farmer Adoption of Soil Health Practices","volume":"34","author":"Bowman","year":"2019","journal-title":"Choices"},{"key":"ref_13","unstructured":"Wallander, S., Smith, D., Bowman, M., and Claassen, R. (2021). Cover Crop Trends, Programs, and Practices in the United States, Economic Information Bulletin 222."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"126278","DOI":"10.1016\/j.eja.2021.126278","article-title":"Field-Scale Assessment of Belgian Winter Cover Crops Biomass Based on Sentinel-2 Data","volume":"126","author":"Goffart","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"303","DOI":"10.2489\/jswc.64.5.303","article-title":"Using Satellite Remote Sensing to Estimate Winter Cover Crop Nutrient Uptake Efficiency","volume":"64","author":"Hively","year":"2009","journal-title":"J. Soil Water Conserv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jennewein, J., Lamb, B.T., Hively, W.D., Thieme, A., Thapa, R., Goldsmith, A., and Mirsky, S.B. (2022). Integration of Satellite-Based Optical and Synthetic Aperture Radar Imagery to Estimate Winter Cover Crop Performance in Cereal Grasses. Remote Sens., 14.","DOI":"10.3390\/rs14092077"},{"key":"ref_17","first-page":"88","article-title":"Evaluating the Relationship between Biomass, Percent Groundcover and Remote Sensing Indices across Six Winter Cover Crop Fields in Maryland, United States","volume":"39","author":"Prabhakara","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111943","DOI":"10.1016\/j.rse.2020.111943","article-title":"Using NASA Earth Observations and Google Earth Engine to Map Winter Cover Crop Conservation Performance in the Chesapeake Bay Watershed","volume":"248","author":"Thieme","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_19","unstructured":"Thieme, A. (2022). Multispectral Satellite Remote Sensing Approaches for Estimating Cover Crop Performance in Maryland and Delaware. [Ph.D. Thesis, University of Maryland]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"682","DOI":"10.2489\/jswc.73.6.682","article-title":"The Feasibility of Satellite Remote Sensing and Spatial Interpolation to Estimate Cover Crop Biomass and Nitrogen Uptake in a Small Watershed","volume":"73","author":"Xu","year":"2018","journal-title":"J. Soil Water Conserv."},{"key":"ref_21","unstructured":"Prabhakara, K. (2016). Factors Influencing Remote Sensing Measurements of Winter Cover Crops. [Ph.D. Thesis, University of Maryland]. Available online: http:\/\/hdl.handle.net\/1903\/18970."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"350","DOI":"10.2489\/jswc.74.4.350","article-title":"Unmanned Aerial Vehicle\u2013Based Assessment of Cover Crop Biomass and Nitrogen Uptake Variability","volume":"74","author":"Yuan","year":"2019","journal-title":"J. Soil Water Conserv."},{"key":"ref_23","unstructured":"(2023, October 05). USDA NASS. Quick Stats Database, Available online: https:\/\/www.nass.usda.gov\/Quick_Stats\/."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-Based Cloud and Cloud Shadow Detection in Landsat Imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112990","DOI":"10.1016\/j.rse.2022.112990","article-title":"Cloud Mask Intercomparison eXercise (CMIX): An Evaluation of Cloud Masking Algorithms for Landsat 8 and Sentinel-2","volume":"274","author":"Skakun","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1126\/science.1183899","article-title":"Precision Agriculture and Food Security","volume":"327","author":"Gebbers","year":"2010","journal-title":"Science"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/jsfa.6734","article-title":"The Role of Precision Agriculture for Improved Nutrient Management on Farms","volume":"95","author":"Hedley","year":"2015","journal-title":"J. Sci. Food Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty Five Years of Remote Sensing in Precision Agriculture: Key Advances and Remaining Knowledge Gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2920","DOI":"10.3390\/s150202920","article-title":"Estimation of Biomass and Canopy Height in Bermudagrass, Alfalfa, and Wheat Using Ultrasonic, Laser, and Spectral Sensors","volume":"15","author":"Pittman","year":"2015","journal-title":"Sensors"},{"key":"ref_30","unstructured":"(2023, May 10). Holland Scientific. Forage Sensor Box. Precision Sustainable Agriculture. Available online: https:\/\/www.precisionsustainableag.org\/forage-sensor-box."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.isprsjprs.2020.08.003","article-title":"Estimating Crop Biomass Using Leaf Area Index Derived from Landsat 8 and Sentinel-2 Data","volume":"168","author":"Dong","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dehghan-Shoar, M.H., Pullanagari, R.R., Kereszturi, G., Orsi, A.A., Yule, I.J., and Hanly, J. (2023). A Unified Physically Based Method for Monitoring Grassland Nitrogen Concentration with Landsat 7, Landsat 8, and Sentinel-2 Satellite Data. Remote Sens., 15.","DOI":"10.3390\/rs15102491"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mandanici, E., and Bitelli, G. (2016). Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sens., 8.","DOI":"10.3390\/rs8121014"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.fcr.2011.06.007","article-title":"Comparison of Active and Passive Spectral Sensors in Discriminating Biomass Parameters and Nitrogen Status in Wheat Cultivars","volume":"124","author":"Erdle","year":"2011","journal-title":"Field Crop. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4335","DOI":"10.1080\/01431160903258217","article-title":"Characterizing Vegetation Indices Derived from Active and Passive Sensors","volume":"31","author":"Fitzgerald","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","first-page":"391","article-title":"Comparison between Vegetation Index Obtained by Active and Passive Proximal Sensors","volume":"9","author":"Prudente","year":"2022","journal-title":"J. Agric. Stud."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3109","DOI":"10.3390\/s130303109","article-title":"Comparison and Intercalibration of Vegetation Indices from Different Sensors for Monitoring Above-Ground Plant Nitrogen Uptake in Winter Wheat","volume":"13","author":"Yao","year":"2013","journal-title":"Sensors"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.fcr.2013.09.006","article-title":"Evaluation of Active and Passive Sensor Systems in the Field to Phenotype Maize Hybrids with High-Throughput","volume":"154","author":"Winterhalter","year":"2013","journal-title":"Field Crop. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Biney, J.K.M., Saberioon, M., Bor\u016fvka, L., Hou\u0161ka, J., Va\u0161\u00e1t, R., Chapman Agyeman, P., Coblinski, J.A., and Klement, A. (2021). Exploring the Suitability of Uas-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13020308"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wijesingha, J., Dayananda, S., Wachendorf, M., and Astor, T. (2021). Comparison of Spaceborne and Uav-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters. Sensors, 21.","DOI":"10.3390\/s21082886"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.rse.2004.05.017","article-title":"Crop Condition and Yield Simulations Using Landsat and MODIS","volume":"92","author":"Doraiswamy","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Moravec, D., Kom\u00e1rek, J., L\u00f3pez-Cuervo Medina, S., and Molina, I. (2021). Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors. Remote Sens., 13.","DOI":"10.3390\/rs13183550"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2021.01.009","article-title":"Sentinel-2 and WorldView-3 Atmospheric Correction and Signal Normalization Based on Ground-Truth Spectroradiometric Measurements","volume":"173","author":"Pancorbo","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","unstructured":"CROPSCAN, Inc. (2023, September 17). CROPSCAN. Available online: http:\/\/www.cropscan.com\/."},{"key":"ref_45","unstructured":"(2023, May 10). Holland Scientific. Crop Circle ACS-470 Multi-Spectral Crop Canopy Sensor. Available online: http:\/\/hollandscientific.com\/crop-circle-acs-470-multi-spectral-crop-canopy-sensor\/."},{"key":"ref_46","unstructured":"Holben, B. (2023, September 05). AERONET, Available online: http:\/\/aeronet.gsfc.nasa.gov\/."},{"key":"ref_47","unstructured":"USGS ESPA (2023, August 04). EROS Science Processing Architecture. EROS Science Processing Architecture On Demand Interface, Available online: https:\/\/espa.cr.usgs.gov\/."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat Surface Reflectance Dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","unstructured":"U.S. Geological Survey (2024, February 04). EarthExplorer, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_50","unstructured":"USGS (2023, August 04). Landsat 7 SLC-Off Products. Landsat 7 SLC-Off Products, Available online: https:\/\/www.usgs.gov\/landsat-missions\/landsat-7."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5449","DOI":"10.1080\/01431160903369642","article-title":"Automated Masking of Cloud and Cloud Shadow for Forest Change Analysis Using Landsat Images","volume":"31","author":"Huang","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","unstructured":"NV5 Geospatial Software (2012). Exelis Visual Information Solutions, ENVI. V. 4.8, NV5 Geospatial Software."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., and van den Bosch, J. (2014, January 24\u201327). MODTRAN\u00ae 6: A Major Upgrade of the MODTRAN\u00ae Radiative Transfer Code. Proceedings of the 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland.","DOI":"10.1109\/WHISPERS.2014.8077573"},{"key":"ref_54","unstructured":"Spectral Sciences Inc. (2023, September 04). MODTRAN5. Available online: http:\/\/modtran5.com\/."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lamb, B.T., Hively, W.D., Jennewein, J., Thieme, A., and Soroka, A. (2023). Atmospheric Correction Intercomparison of Hyperspectral and Multispectral Imagery over Agricultural Study Sites, IEEE.","DOI":"10.1109\/IGARSS52108.2023.10281710"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1364\/AO.47.002215","article-title":"Radiative Transfer Codes for Atmospheric Correction and Aerosol Retrieval: Intercomparison Study","volume":"47","author":"Kotchenova","year":"2008","journal-title":"Appl. Opt."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/36.581987","article-title":"Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An Overview","volume":"35","author":"Vermote","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","unstructured":"Environment Canada (2023, March 16). Ozone Map Archive. Available online: http:\/\/exp-studies.tor.ec.gc.ca\/cgi-bin\/clf2\/selectMap?lang=e&printerversion=false&printfullpage=false&accessible=off\/."},{"key":"ref_59","unstructured":"(2023, March 16). NOAA ERSL. Radiosonde Database, Available online: http:\/\/www.esrl.noaa.gov\/raobs\/."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.2135\/cropsci2013.04.0217","article-title":"Diurnal Variability in Reflectance Measurements from Cotton","volume":"54","author":"Oliveira","year":"2014","journal-title":"Crop. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Darra, N., Psomiadis, E., Kasimati, A., Anastasiou, A., Anastasiou, E., and Fountas, S. (2021). Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards. Agronomy, 11.","DOI":"10.3390\/agronomy11040741"},{"key":"ref_62","unstructured":"Devadas, R. (2009). Analysis of the Interaction of Nitrogen Application and Stripe Rust Infection in Wheat Using in Situ Proximal and Remote Sensing Techniques, School of Science and Technology, University of New England."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.compag.2013.10.007","article-title":"The Performance of Active Spectral Reflectance Sensors as Influenced by Measuring Distance, Device Temperature and Light Intensity","volume":"100","author":"Kipp","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s10661-005-9164-7","article-title":"Point Sampling Digital Imagery with \u2018Samplepoint\u2019","volume":"123","author":"Booth","year":"2006","journal-title":"Environ. Monit. Assess."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"043532","DOI":"10.1117\/1.3449090","article-title":"Vegetation Water Content Mapping in a Diverse Agricultural Landscape: National Airborne Field Experiment 2006","volume":"4","author":"Cosh","year":"2010","journal-title":"J. Appl. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"113665","DOI":"10.1016\/j.rse.2023.113665","article-title":"Evaluating the Saturation Effect of Vegetation Indices in Forests Using 3D Radiative Transfer Simulations and Satellite Observations","volume":"295","author":"Gao","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1080\/01431160110107806","article-title":"Vegetation and Soil Lines in Visible Spectral Space: A Concept and Technique for Remote Estimation of Vegetation Fraction","volume":"23","author":"Gitelson","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a Two-Band Enhanced Vegetation Index without a Blue Band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_71","unstructured":"R Core Team (2023, March 16). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_72","unstructured":"Lenth, R., Singmann, H., Love, J., Buerkner, P., and Herve, M. (2019). Package \u2018Emmeans\u2019. R Package Version, 1."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0034-4257(01)00328-5","article-title":"Effects of Spectral Response Function on Surface Reflectance and NDVI Measured with Moderate Resolution Satellite Sensors","volume":"81","author":"Trishchenko","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1080\/01431160110075622","article-title":"Analysis of in Situ Hyperspectral Data for Nutrient Estimation of Giant Sequoia","volume":"23","author":"Gong","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1080\/01431168308948546","article-title":"The Red Edge of Plant Leaf Reflectance","volume":"4","author":"Horler","year":"1983","journal-title":"Int. J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1002\/agj2.21207","article-title":"Remote Sensing Evaluation of Winter Cover Crop Springtime Performance and the Impact of Delayed Termination","volume":"115","author":"Thieme","year":"2023","journal-title":"Agron. J."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and Expansion of the Fmask Algorithm: Cloud, Cloud Shadow, and Snow Detection for Landsats 4\u20137, 8, and Sentinel 2 Images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary Analysis of the Performance of the Landsat 8\/OLI Land Surface Reflectance Product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_79","first-page":"102253","article-title":"An Experimental Sky-Image-Derived Cloud Validation Dataset for Sentinel-2 and Landsat 8 Satellites over NASA GSFC","volume":"95","author":"Skakun","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/36.673680","article-title":"A Procedure for the Detection and Removal of Cloud Shadow from AVHRR Data over Land","volume":"36","author":"Simpson","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.rse.2016.02.052","article-title":"An Analysis of Landsat 7 and Landsat 8 Underflight Data and the Implications for Time Series Investigations","volume":"185","author":"Holden","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2408","DOI":"10.1109\/LGRS.2017.2766448","article-title":"Multispectral Misregistration of Sentinel-2A Images: Analysis and Implications for Potential Applications","volume":"14","author":"Skakun","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_83","unstructured":"Kaufman, Y.J., Tanr\u00e9, D., Holben, B.N., Markham, B.L., and Gitelson, A.A. (1992). School of Natural Resources: Faculty Publications, IEEE."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2015.02.010","article-title":"Atmospheric Correction for Global Mapping Spectroscopy: ATREM Advances for the HyspIRI Preparatory Campaign","volume":"167","author":"Thompson","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2339\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:24:16Z","timestamp":1760106256000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,6]]},"references-count":84,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072339"],"URL":"https:\/\/doi.org\/10.3390\/s24072339","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,6]]}}}