Publication Date:
2019
Description:
Abstract
High‐quality soil moisture (SM) datasets are in great demand for climate, hydrology and other fields, but detailed evaluation of SM products from various sources is scarce. Thus, using 670 SM stations worldwide, we evaluated and compared SM products from microwave remote sensing (AMSR‐E (C‐ and X‐band) and ESA CCI), land surface model (GLDAS), and reanalysis data (ERA‐Interim and NCEP) under different time scales and various climates and land covers. We find that: 1) ESA CCI and GLDAS have the closest values to the in‐situ SM on the annual scale, whereas others overestimate the SM; ERA‐Interim (averaged R = 0.58) and ESA CCI (averaged R = 0.54) correlate best with the in‐situ data, while GLDAS performs worst. 2) Overall, the deviations of each product vary in seasons. ESA CCI and ERA‐Interim products are closer to the in‐situ SM at seasonal scales, and AMSR‐E and NCEP perform worst in December to February and June to August, respectively. 3) Except for NCEP and ERA‐Interim, the others can well reflect the inter‐monthly variation of the in‐situ SM. 4) Under various climates and land covers. AMSR‐E products are less effective in cold climates, whereas GLDAS and NCEP products perform poorly in arid or temperate and dry climates. Moreover, the bias and R of each SM product differ obviously under different forest types, especially AMSR‐E products. In summary, SM from ESA CCI is the best, followed by ERA‐Interim product, and precipitation is an important auxiliary data for selecting high‐quality SM stations and improving the accuracy of SM from GLDAS. These results can provide a reference for improving the accuracy of the above SM products.
Print ISSN:
0885-6087
Electronic ISSN:
1099-1085
Topics:
Architecture, Civil Engineering, Surveying
,
Geography
Permalink