Publications

Geng, XZ; Li, H; Yao, ZY; Chen, X; Yang, ZK; Li, SE; Wu, LF; Cui, YK (2022). Potential of ANN for Prolonging Remote Sensing-Based Soil Moisture Products for Long-term Time Series Analysis. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 19, 2503205.

Abstract
Soil moisture (SM) plays an important role in the water-heat-energy exchange and water cycle of the land ecosystem. Long-term SM products are vital in the time series study of ecology and hydrology. Therefore, it is vital to extend the time span with limited SM monitoring sensors, since there is no single long-term SM product currently. In this study, an SM product prolonging method based on an artificial neural network (ANN) and moderate-resolution imaging spectroradiometer (MODIS) optical products was proposed. The prolonging results of Soil Moisture Active Passive (SMAP) and Fenyun-3B (FY3B) products were validated in Tibetan Plateau to present the feasibility of this method. The result shows this method is feasible in areas under medium vegetation cover (0.2 < NDVI < 0.6), but it still needs to be improved in some areas (especially in areas with very high or very low NDVI). The prolonging data fits well (R = 0.84, RMSE < 0.06 cm(3)cm(-3)) with in situ measurements for both SMAP and FY-3B products. The generated long-term SM will benefit the global water cycle study.

DOI:
10.1109/LGRS.2021.3140113

ISSN:
1558-0571