{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T03:29:57Z","timestamp":1775100597831,"version":"3.50.1"},"reference-count":115,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bavarian Ministry of Science and the Arts"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km2) for the year 2019. For WW and OSR, the synthetic products\u2019 high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R2 = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt\/ha for WW, R2 = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt\/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world.<\/jats:p>","DOI":"10.3390\/rs15071830","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T01:05:26Z","timestamp":1680138326000},"page":"1830","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI 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 W\u00fcrzburg, 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":"Thorsten","family":"Dahms","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of W\u00fcrzburg, 97074 W\u00fcrzburg, Germany"},{"name":"Gauss Center for Geodesy and Geoinformation, Federal Agency for Cartography and Geodesy, 60598 Frankfurt am Main, 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 Garmisch-Partenkirchen, Germany"}]},{"given":"Ingolf","family":"Steffan-Dewenter","sequence":"additional","affiliation":[{"name":"Department of Animal Ecology and Tropical Biology, University of W\u00fcrzburg, 97074 W\u00fcrzburg, 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 W\u00fcrzburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"ref_1","unstructured":"FAO (2017). 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