{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T15:37:33Z","timestamp":1772897853058,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["WA 2135\/4-1"],"award-info":[{"award-number":["WA 2135\/4-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001407","name":"Department of Biotechnology, Ministry of Science and Technology, India","doi-asserted-by":"publisher","award":["BT\/IN\/German\/DFG\/14\/BVCR\/2016"],"award-info":[{"award-number":["BT\/IN\/German\/DFG\/14\/BVCR\/2016"]}],"id":[{"id":"10.13039\/501100001407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study\u2019s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.<\/jats:p>","DOI":"10.3390\/s21082886","type":"journal-article","created":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T13:58:04Z","timestamp":1618927084000},"page":"2886","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2574-6303","authenticated-orcid":false,"given":"Jayan","family":"Wijesingha","sequence":"first","affiliation":[{"name":"Grassland Science and Renewable Plant Resources, Universit\u00e4t Kassel, Steinstra\u00dfe 19, D-37213 Witzenhausen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Supriya","family":"Dayananda","sequence":"additional","affiliation":[{"name":"Grassland Science and Renewable Plant Resources, Universit\u00e4t Kassel, Steinstra\u00dfe 19, D-37213 Witzenhausen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2840-7086","authenticated-orcid":false,"given":"Michael","family":"Wachendorf","sequence":"additional","affiliation":[{"name":"Grassland Science and Renewable Plant Resources, Universit\u00e4t Kassel, Steinstra\u00dfe 19, D-37213 Witzenhausen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Astor","sequence":"additional","affiliation":[{"name":"Grassland Science and Renewable Plant Resources, Universit\u00e4t Kassel, Steinstra\u00dfe 19, D-37213 Witzenhausen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8939","DOI":"10.1073\/pnas.1606036114","article-title":"Future urban land expansion and implications for global croplands","volume":"114","author":"Reitsma","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1080\/17535069.2017.1347810","article-title":"Megacity governance and the state","volume":"11","year":"2018","journal-title":"Urban Res. Pract."},{"key":"ref_3","first-page":"71","article-title":"Urbanisation and new agroecologies","volume":"LIII","author":"Patil","year":"2018","journal-title":"Econ. Polit. Wkly."},{"key":"ref_4","unstructured":"Directorate of Economics and Statistics (2012). Report on Area, Production, Productivity and Prices of Agriculture Crops in Karnataka, 2009\u20132010."},{"key":"ref_5","unstructured":"Food and Agriculture Organization of the United Nations (2017). The Future of Food and Agriculture: Trends and Challenges, FAO."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Sishodia, R.P., Ray, R.L., and Singh, S.K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sens., 12.","DOI":"10.3390\/rs12193136"},{"key":"ref_8","unstructured":"Jones, H.G., and Vaughan, R.A. (2010). Remote Sensing of Vegetation\u2014Principles, Techniques, and Applications, Oxford University Press."},{"key":"ref_9","unstructured":"Prasad, S.T., John, G.L., and Alfredo, H. (2011). Hyperspectral Remote Sensing of Vegetation, CRC Press."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mananze, S., P\u00f4\u00e7as, I., and Cunha, M. (2018). Retrieval of maize leaf area index using hyperspectral and multispectral data. Remote Sens., 10.","DOI":"10.3390\/rs10121942"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s10712-018-9478-y","article-title":"Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods","volume":"40","author":"Verrelst","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4927","DOI":"10.3390\/rs6064927","article-title":"On the semi-automatic retrieval of biophysical parameters based on spectral index optimization","volume":"6","author":"Rivera","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.isprsjprs.2015.05.005","article-title":"Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties\u2014A review","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","unstructured":"Caicedo, J.P.R. (2014). Optimized and Automated Estimation of Vegetation Properties: Opportunities for Sentinel-2. [Ph.D. Thesis, Universitat De Val\u00e8ncia]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1111\/j.1365-3040.1992.tb00992.x","article-title":"Defining leaf area index for non-flat leaves","volume":"15","author":"Chen","year":"1992","journal-title":"Plant. Cell Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, J., Quackenbush, L.J., Volk, T.A., and Im, J. (2020). Forest and crop leaf area index estimation using remote sensing: Research trends and future directions. Remote Sens., 12.","DOI":"10.3390\/rs12182934"},{"key":"ref_17","first-page":"160","article-title":"Leaf chlorophyll constraint on model simulated gross primary productivity in agricultural systems","volume":"43","author":"Houborg","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Croft, H., Chen, J.M., Wang, R., Mo, G., Luo, S., Luo, X., He, L., Gonsamo, A., Arabian, J., and Zhang, Y. (2020). The global distribution of leaf chlorophyll content. Remote Sens. Environ., 236.","DOI":"10.1016\/j.rse.2019.111479"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1109\/JSTARS.2014.2298752","article-title":"Toward a semiautomatic machine learning retrieval of biophysical parameters","volume":"7","author":"Caicedo","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1080\/01431161.2016.1186850","article-title":"Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method","volume":"37","author":"Liang","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1080\/01431169308954010","article-title":"The reflectance at the 950\u2013970 nm region as an indicator of plant water status","volume":"14","author":"Penuelas","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","first-page":"119","article-title":"Estimating canopy water content using hyperspectral remote sensing data","volume":"12","author":"Clevers","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1080\/014311697217396","article-title":"Estimation of plant water concentration by the reflectance Water Index WI (R900\/R970)","volume":"18","author":"Penuelas","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12898-019-0233-0","article-title":"Estimation of vegetation water content using hyperspectral vegetation indices: A comparison of crop water indicators in response to water stress treatments for summer maize","volume":"19","author":"Zhang","year":"2019","journal-title":"BMC Ecol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15203","DOI":"10.3390\/rs71115203","article-title":"Estimation of canopy water content by means of hyperspectral indices based on drought stress gradient experiments of maize in the north plain China","volume":"7","author":"Zhang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tsouros, D.C., Bibi, S., and Sarigiannidis, P.G. (2019). A Review on UAV-Based Applications for Precision Agriculture. Information, 10.","DOI":"10.3390\/info10110349"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1109\/JSTARS.2019.2910558","article-title":"Comparing the performance of multispectral and hyperspectral images for estimating vegetation properties","volume":"12","author":"Lu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dayananda, S., Astor, T., Wijesingha, J., Chickadibburahalli Thimappa, S., Dimba Chowdappa, H., Nidamanuri, R.R., Nautiyal, S., and Wachendorf, M. (2019). Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging. Remote Sens., 11.","DOI":"10.3390\/rs11151771"},{"key":"ref_30","unstructured":"Danner, M., Locherer, M., Hank, T., and Richter, K. (2015). Measuring Leaf Area Index (LAI) with the LI-Cor LAI 2200C or LAI-2200, EnMAP Field Guide Technical Report; GFZ Data Services."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1111\/j.1399-3054.2012.01639.x","article-title":"A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids","volume":"146","author":"Cerovic","year":"2012","journal-title":"Physiol. Plant."},{"key":"ref_32","unstructured":"Kuester, M. (2016). Radiometric Use of WorldView-3 Imagery, Digital Globe."},{"key":"ref_33","unstructured":"Digital Globe (2014). WorldView-3, Digital Globe."},{"key":"ref_34","unstructured":"Davaadorj, A. (2019). Evaluating Atmospheric Correction Methods Using Worldview-3 Image. [Master\u2019s Thesis, University of Twente]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/S2095-3119(15)61073-5","article-title":"Estimating the crop leaf area index using hyperspectral remote sensing","volume":"15","author":"Liu","year":"2016","journal-title":"J. Integr. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0034-4257(98)00046-7","article-title":"Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b, and Total Carotenoid Content in Eucalyptus Leaves","volume":"66","author":"Datt","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_37","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Prog. Rep. RSC 1978-1, 112."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS- MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hill, J., and M\u00e9gier, J. (1994). Imaging Spectrometry in Agriculture\u2014Plant Vitality And Yield Indicators. Imaging Spectrometry\u2014A Tool for Environmental Observations, Springer.","DOI":"10.1007\/978-0-585-33173-7"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2015.08.002","article-title":"Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance","volume":"108","author":"Aasen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","unstructured":"Cubert GmbH (2016). Cubert S185, Cubert GmbH."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wijesingha, J., Astor, T., Schulze-Br\u00fcninghoff, D., Wengert, M., and Wachendorf, M. (2020). Predicting forage quality of grasslands using UAV-borne imaging spectroscopy. Remote Sens., 12.","DOI":"10.3390\/rs12010126"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, G., Li, C., Wang, Y., Yuan, H., Feng, H., Xu, B., and Yang, X. (2017). The DOM generation and precise radiometric calibration of a UAV-mounted miniature snapshot hyperspectral imager. Remote Sens., 9.","DOI":"10.3390\/rs9070642"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","unstructured":"Soukhavong, D. (2020, November 19). (Laurae) Ensembles of tree-based models: Why correlated features do not trip them\u2014And why NA matters. Available online: https:\/\/medium.com\/data-design\/ensembles-of-tree-based-models-why-correlated-features-do-not-trip-them-and-why-na-matters-7658f4752e1b."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature selection with the boruta package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3711","DOI":"10.1093\/bioinformatics\/bty373","article-title":"The revival of the Gini importance?","volume":"34","author":"Nembrini","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.21105\/joss.01903","article-title":"mlr3: A modern object-oriented machine learning framework in R","volume":"4","author":"Lang","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_51","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wright, M.N., and Ziegler, A. (2017). Ranger: A fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw., 77.","DOI":"10.18637\/jss.v077.i01"},{"key":"ref_53","first-page":"279","article-title":"Cautionary note about R2","volume":"39","author":"Kvalseth","year":"1985","journal-title":"Am. Stat."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Afrasiabian, Y., Noory, H., Mokhtari, A., Nikoo, M.R., Pourshakouri, F., and Haghighatmehr, P. (2020). Effects of spatial, temporal, and spectral resolutions on the estimation of wheat and barley leaf area index using multi- and hyper-spectral data (case study: Karaj, Iran). Precis. Agric.","DOI":"10.1007\/s11119-020-09749-9"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.rse.2018.06.036","article-title":"Estimating smallholder crops production at village level from Sentinel-2 time series in Mali\u2019s cotton belt","volume":"216","author":"Lambert","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Taylor, J.R.N., and Kruger, J. (2015). Millets. Encycl. Food Heal., 748\u2013757.","DOI":"10.1016\/B978-0-12-384947-2.00466-9"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Shafian, S., Rajan, N., Schnell, R., Bagavathiannan, M., Valasek, J., Shi, Y., and Olsenholler, J. (2018). Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196605"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Bhadra, S., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Newcomb, M., Shakoor, N., and Mockler, T.C. (2020). Quantifying leaf chlorophyll concentration of sorghum from hyperspectral data using derivative calculus and machine learning. Remote Sens., 12.","DOI":"10.3390\/rs12132082"},{"key":"ref_59","first-page":"1","article-title":"Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system","volume":"9","author":"Li","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_60","first-page":"74","article-title":"Legumes","volume":"3\u20134","author":"Allen","year":"2012","journal-title":"Encycl. Hum. Nutr."},{"key":"ref_61","first-page":"108","article-title":"Estimation of leaf area index using ground spectral measurements over agriculture crops: Prediction capability assessment of optical indices","volume":"35","author":"Haboudane","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_62","first-page":"47","article-title":"Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels","volume":"25","author":"Schlemmera","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/0076-6879(87)48036-1","article-title":"Chlorophylls and Carotenoids: Pigments of Photosynthetic Biomembranes","volume":"148","author":"Lichtenthaler","year":"1987","journal-title":"Methods Enzymol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"105786","DOI":"10.1016\/j.compag.2020.105786","article-title":"Estimating leaf chlorophyll content of crops via optimal unmanned aerial vehicle hyperspectral data at multi-scales","volume":"178","author":"Zhu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1080\/014311698214910","article-title":"Technical note A new technique for interpolating the reflectance red edge position","volume":"19","author":"Dawson","year":"1998","journal-title":"Int. J. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2886\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:50:20Z","timestamp":1760161820000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/8\/2886"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,20]]},"references-count":65,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21082886"],"URL":"https:\/\/doi.org\/10.3390\/s21082886","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,20]]}}}