{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:32:13Z","timestamp":1774366333455,"version":"3.50.1"},"reference-count":98,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T00:00:00Z","timestamp":1679097600000},"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>Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and \u22120.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt\/ha and 2.19 dt\/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.<\/jats:p>","DOI":"10.3390\/rs15061651","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T03:09:37Z","timestamp":1679281777000},"page":"1651","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany"],"prefix":"10.3390","volume":"15","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 W\u00fcrzburg, 97074 Wurzburg, Germany"}]},{"given":"Thorsten","family":"Dahms","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of W\u00fcrzburg, 97074 Wurzburg, Germany"},{"name":"Gauss Center for Geodesy and Geoinformation, Federal Agency for Cartography and Geodesy, 60598 Frankfurt am Main, 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"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0136-8584","authenticated-orcid":false,"given":"Adomas","family":"Liepa","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of W\u00fcrzburg, 97074 Wurzburg, Germany"}]},{"given":"Thomas","family":"Rummler","sequence":"additional","affiliation":[{"name":"Department of Applied Computer Science, Institute of Geography, University of Augsburg, 86159 Augsburg, Germany"}]},{"given":"Joel","family":"Arnault","sequence":"additional","affiliation":[{"name":"Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, 82467 Garmish-Partenkirchen, Germany"}]},{"given":"Ingolf","family":"Steffan-Dewenter","sequence":"additional","affiliation":[{"name":"Department of Animal Ecology and Tropical Biology, University of W\u00fcrzburg, 97074 Wurzburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6626-3052","authenticated-orcid":false,"given":"Tobias","family":"Ullmann","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of W\u00fcrzburg, 97074 Wurzburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1038\/nature11420","article-title":"Closing yield gaps through nutrient and water management","volume":"490","author":"Mueller","year":"2012","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bian, C., Shi, H., Wu, S., Zhang, K., Wei, M., Zhao, Y., Sun, Y., Zhuang, H., Zhang, X., and Chen, S. 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