Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

摘要: Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization grasslands in ecological agricultural applications. However, current remote sensing data cannot simultaneously provide accurate vegetation changes with fine resolutions. We used a data-fusion approach, namely adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference index (NDVI) from Moderate-Resolution Imaging Spectroradiometer (MODIS) Landsat sets. This provided observations (8-d) medium (30 m) Based on field-sampled aboveground (AGB), NDVI support vector machine (SVM) techniques were integrated develop an AGB estimation (SVM-AGB) Xilinhot Inner Mongolia, China. Compared generated MODIS-NDVI (R2 = 0.73, root-mean-square error (RMSE) 30.61 g/m2), SVM-AGB we developed can not only ensure accuracy 0.77, RMSE 17.22 but also produce higher resolution maps. then time-series detect anomalies regions. found that NDVI-derived estimations contained more details distribution severity compared MODIS estimations. first time have series 30-m 8-d intervals through combined use method model. Our study will be useful near real-time (improved resolutions) conditions, implications arid semi-arid management.