Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany
نویسندگان
چکیده
Rapid and accurate yield estimates at both field regional levels remain the goal of sustainable agriculture food security. Hereby, identification consistent reliable methodologies providing predictions is one hot topics in agricultural research. This study investigated relationship spatiotemporal fusion modelling using STRAFM on crop prediction for winter wheat (WW) oil-seed rape (OSR) a semi-empirical light use efficiency (LUE) model Free State Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated validated by fusing high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 2012), 7 Enhanced Plus (ETM+) (2012), 8 Operational Land Imager (OLI) (2013 2019) with coarse MOD13Q1 (250 Except some temporal periods (i.e., 2001, 2002, obtained an R2 more than 0.65 RMSE less 0.11, which proves that OLI fused products are higher accuracy TM products. Moreover, accuracies NDVI data have been found correlate total number available scenes every year (N), correlation coefficient (R) +0.83 (between yearly NDVIs N) ?0.84 RMSEs N). For prediction, climate elements (such as minimum temperature, maximum relative humidity, evaporation, transpiration, solar radiation) inputted LUE model, resulting average 0.75 0.73 (OSR), 4.33 dt/ha 2.19 dt/ha. The results prove consistency stability estimation. Using were WW (R2 = 0.88) OSR 0.74). Lastly, observed positive R 0.81 0.77 between modelled OSR, respectively.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs15061651