{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:42:16Z","timestamp":1775119336528,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T00:00:00Z","timestamp":1643587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region\u2019s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5\u20136 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions\u2019 cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R2 = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R2 = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R2 = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R2 = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R2 = 0.60, RMSE = 0.05) and S-MOD13Q1 (R2 = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution.<\/jats:p>","DOI":"10.3390\/rs14030677","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T22:16:18Z","timestamp":1643753778000},"page":"677","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2351-9492","authenticated-orcid":false,"given":"Maninder Singh","family":"Dhillon","sequence":"first","affiliation":[{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, 97074 Wuerzburg, Germany"}]},{"given":"Thorsten","family":"Dahms","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, 97074 Wuerzburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0708-5863","authenticated-orcid":false,"given":"Carina","family":"K\u00fcbert-Flock","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Hessian State Agency for Nature Conservation, Environment and Geology (HLNUG), 65203 Wiesbaden, Germany"}]},{"given":"Ingolf","family":"Steffan-Dewenter","sequence":"additional","affiliation":[{"name":"Department of Animal Ecology and Tropical Biology, University of Wuerzburg, 97074 Wuerzburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2599-5983","authenticated-orcid":false,"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Animal Ecology and Tropical Biology, University of Wuerzburg, 97074 Wuerzburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6626-3052","authenticated-orcid":false,"given":"Tobias","family":"Ullmann","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Institute of Geography and Geology, University of Wuerzburg, 97074 Wuerzburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"619818","DOI":"10.3389\/frsen.2021.619818","article-title":"Grand Challenges in Satellite Remote Sensing","volume":"2","author":"Dubovik","year":"2021","journal-title":"Front. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dhillon, M.S., Dahms, T., Kuebert-Flock, C., Borg, E., Conrad, C., and Ullmann, T. (2020). Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany. Remote Sens., 12.","DOI":"10.3390\/rs12111819"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1016\/j.rse.2008.03.009","article-title":"Multi-temporal MODIS\u2013Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data","volume":"112","author":"Roy","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2014.09.012","article-title":"A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion","volume":"156","author":"Gevaert","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.3390\/rs4061856","article-title":"Preparing Landsat Image Time Series (LITS) for Monitoring Changes in Vegetation Phenology in Queensland, Australia","volume":"4","author":"Bhandari","year":"2012","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.3390\/rs3091943","article-title":"A Multi-Resolution Multi-Temporal Technique for Detecting and Mapping Deforestation in the Brazilian Amazon Rainforest","volume":"3","author":"Arai","year":"2011","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2017.05.042","article-title":"Investigating spatiotemporal snow cover variability via cloud-free MODIS snow cover product in Central Alborz Region","volume":"202","author":"Dariane","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.jhydrol.2015.12.065","article-title":"Improving the accuracy of MODIS 8-day snow products with in situ temperature and precipitation data","volume":"534","author":"Dong","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Parajka, J., and Bloschl, G. (2008). Spatio-temporal combination of MODIS images\u2014Potential for snow cover mapping. Water Resour. Res., 44.","DOI":"10.1029\/2007WR006204"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xie, D., Zhang, J., Zhu, X., Pan, Y., Liu, H., Yuan, Z., and Yun, Y. (2016). An Improved STARFM with Help of an Unmixing-Based Method to Generate High Spatial and Temporal Resolution Remote Sensing Data in Complex Heterogeneous Regions. Sensors, 16.","DOI":"10.3390\/s16020207"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cui, J., Zhang, X., and Luo, M. (2018). Combining Linear Pixel Unmixing and STARFM for Spatiotemporal Fusion of Gaofen-1 Wide Field of View Imagery and MODIS Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071047"},{"key":"ref_13","first-page":"861","article-title":"Cloud Detection and Restoration of Landsat-8 using STARFM","volume":"35","author":"Lee","year":"2019","journal-title":"Korean J. Remote Sens."},{"key":"ref_14","first-page":"1","article-title":"Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data","volume":"58","author":"Zhu","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/19479830903561035","article-title":"Multi-source remote sensing data fusion: Status and trends","volume":"1","author":"Zhang","year":"2010","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Belgiu, M., and Stein, A. (2019). Spatiotemporal Image Fusion in Remote Sensing. Remote Sens., 11.","DOI":"10.3390\/rs11070818"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"30","article-title":"Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland","volume":"30","author":"Olexa","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"63507","DOI":"10.1117\/1.JRS.6.063507","article-title":"Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model","volume":"6","author":"Wu","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1016\/j.rse.2009.03.007","article-title":"A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS","volume":"113","author":"Hilker","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1109\/TGRS.2012.2186638","article-title":"Spatiotemporal Reflectance Fusion via Sparse Representation","volume":"50","author":"Huang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.rse.2018.04.042","article-title":"STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-\/gap-free surface reflectance product","volume":"214","author":"Luo","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2013.02.007","article-title":"Assessing the accuracy of blending Landsat\u2013MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection","volume":"133","author":"Emelyanova","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhu, X., Cai, F., Tian, J., and Williams, T.K.-A. (2018). Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions. Remote Sens., 10.","DOI":"10.3390\/rs10040527"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1080\/07038992.2020.1865141","article-title":"Deep Learning-Based Spatiotemporal Fusion Approach for Producing High-Resolution NDVI Time-Series Datasets","volume":"47","author":"Htitiou","year":"2021","journal-title":"Can. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"65","DOI":"10.14358\/PERS.84.2.65","article-title":"\u201cBlend-then-Index\u201d or \u201cIndex-then-Blend\u201d: A Theoretical Analysis for Generating High-resolution NDVI Time Series by STARFM","volume":"84","author":"Chen","year":"2018","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9213","DOI":"10.3390\/rs6109213","article-title":"Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of \u201cIndex-then-Blend\u201d and \u201cBlend-then-Index\u201d Approaches","volume":"6","author":"Jarihani","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7865","DOI":"10.3390\/rs70607865","article-title":"An Improved Method for Producing High Spatial-Resolution NDVI Time Series Datasets with Multi-Temporal MODIS NDVI Data and Landsat TM\/ETM+ Images","volume":"7","author":"Rao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liao, C., Wang, J., Pritchard, I., Liu, J., and Shang, J. (2017). A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions. Remote Sens., 9.","DOI":"10.3390\/rs9111125"},{"key":"ref_30","first-page":"102333","article-title":"Spatiotemporal fusion method to simultaneously generate full-length normalized difference vegetation index time series (SSFIT)","volume":"100","author":"Qiu","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kloos, S., Yuan, Y., Castelli, M., and Menzel, A. (2021). Agricultural Drought Detection with MODIS Based Vegetation Health Indices in Southeast Germany. Remote Sens., 13.","DOI":"10.3390\/rs13193907"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_33","unstructured":"Kuebert, C. (2018). Fernerkundung f\u00fcr das Ph\u00e4nologiemonitoring: Optimierung und Analyse des Ergr\u00fcnungsbeginns mittels MODIS-Zeitreihen f\u00fcr Deutschland, University of Wuerzburg."},{"key":"ref_34","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C. (1974). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, Texas A&M University. NASA\/GSFC Type III Final Report."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1016\/j.rse.2011.05.010","article-title":"Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain","volume":"115","author":"Hwang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.rse.2011.10.014","article-title":"Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology","volume":"117","author":"Walker","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1007\/s41976-019-00023-9","article-title":"The Performance of Random Forest Classification Based on Phenological Metrics Derived from Sentinel-2 and Landsat 8 to Map Crop Cover in an Irrigated Semi-arid Region","volume":"2","author":"Htitiou","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.1080\/10106049.2019.1695960","article-title":"Monitoring spatial variability and trends of wheat grain yield over the main cereal regions in Morocco: A remote-based tool for planning and adjusting policies","volume":"36","author":"Benabdelouahab","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-020-05789-7","article-title":"Remote monitoring of agricultural systems using NDVI time series and machine learning methods: A tool for an adaptive agricultural policy","volume":"13","author":"Lebrini","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2013.04.002","article-title":"Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data","volume":"135","author":"Xin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"223","DOI":"10.5194\/hess-15-223-2011","article-title":"Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery","volume":"15","author":"Anderson","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"063554","DOI":"10.1117\/1.JRS.6.063554","article-title":"Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference","volume":"6","author":"Gao","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_44","first-page":"59","article-title":"Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data","volume":"13","author":"Singh","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_45","first-page":"63","article-title":"Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data","volume":"49","author":"Dong","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","first-page":"221","article-title":"Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity","volume":"64","author":"Quintano","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.rse.2018.10.027","article-title":"Sentinel-2\/Landsat-8 product consistency and implications for monitoring aquatic systems","volume":"220","author":"Pahlevan","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"112716","DOI":"10.1016\/j.rse.2021.112716","article-title":"Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery","volume":"266","author":"Moon","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112470","DOI":"10.1016\/j.rse.2021.112470","article-title":"Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record","volume":"261","author":"Zhang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"111685","DOI":"10.1016\/j.rse.2020.111685","article-title":"Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery","volume":"240","author":"Bolton","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","first-page":"821","article-title":"North American landscape characterization dataset development and data fusion issues","volume":"64","author":"Lunetta","year":"1998","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/014311698215748","article-title":"Multisensor image fusion in remote sensing: Concepts, methods and applications","volume":"19","author":"Pohl","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/LGRS.2007.907971","article-title":"An Algorithm to Produce Temporally and Spatially Continuous MODIS-LAI Time Series","volume":"5","author":"Gao","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.agrformet.2008.08.017","article-title":"Effective interpolation of incomplete satellite-derived leaf-area index time series for the continental United States","volume":"149","author":"Borak","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.5194\/bg-10-4055-2013","article-title":"A comparison of methods for smoothing and gap filling time series of remote sensing observations\u2014Application to MODIS LAI products","volume":"10","author":"Kandasamy","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1016\/j.rse.2011.05.006","article-title":"A multisensor fusion approach to improve LAI time series","volume":"115","author":"Verger","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Robinson, N.P., Allred, B.W., Jones, M.O., Moreno, A., Kimball, J.S., Naugle, D.E., Erickson, T.A., and Richardson, A.D. (2017). A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States. Remote Sens., 9.","DOI":"10.3390\/rs9080863"},{"key":"ref_59","unstructured":"Didan, K., Munoz, A.B., Solano, R., and Huete, A. (2015). MODIS Vegetation Index User\u2019s Guide (MOD13 Series), Vegetation Index and Phenology Lab, University of Arizona."},{"key":"ref_60","unstructured":"Solano, R., Didan, K., Jacobson, A., and Huete, A. (2010). MODIS Vegetation Index User\u2019s Guide (MOD13 Series), Terrestrial Biophysics and Remote Sensing Lab, University of Arizona."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2943","DOI":"10.5194\/bg-7-2943-2010","article-title":"A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling","volume":"7","author":"Chen","year":"2010","journal-title":"Biogeosciences"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Thorsten, D., Christopher, C., Babu, D.K., Marco, S., and Erik, B. (2017, January 23\u201328). Derivation of biophysical parameters from fused remote sensing data. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127970"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/677\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:11:59Z","timestamp":1760134319000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/677"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,31]]},"references-count":64,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030677"],"URL":"https:\/\/doi.org\/10.3390\/rs14030677","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,31]]}}}