Abstract
In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.
Highlights
The reference data can be chosen from remote sensing (RS) soil moisture (SM) products, modeled datasets, representative SM transformed by an ancillary variable related to SM, e.g., the apparent thermal inertia (ATI) [12], the soil evaporation efficiency (SEE) [13] and terrain data [14], and ground-based observations
To determine the parameters of dry and wet edges, the range of normalized difference vegetation index product (NDVI) values (x-axis in Figure 3) is first split into several equal intervals, and the minimum and maximum land surface temperature (LST) values in each interval are extracted from the LST-NDVI space
Though the traditional ordinary least square (OLS) regression method can build the relationship of the ground-based observation and the upscaled SM, random error is fitted leading to the regression model with poor prediction
Summary
As the demand for long-term and large-scale SM data in recent years has increased [9], numerous microwave remote sensing (RS) SM products at the global scale have been produced based on different instruments and retrieval algorithms [10]. These differences cause various performance characteristics, which may lead to inconsistent trends and variability in SM. The reference data can be chosen from RS SM products, modeled datasets, representative SM transformed by an ancillary variable related to SM, e.g., the apparent thermal inertia (ATI) [12], the soil evaporation efficiency (SEE) [13] and terrain data [14], and ground-based observations
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