Abstract
Remote sensing (RS) soil moisture (SM) products have been widely used in various environmental studies. Understanding the error structure of data is necessary to properly apply RS SM products in trend and variation analysis and data fusion. However, a spatially continuous assessment of RS SM datasets is impeded by the limited spatial distribution of ground-based observations. As an alternative, the RS apparent thermal inertia (ATI) data related to the SM are transformed into SM values to expand the validation space. To obtain error components, the ATI-based SM along with the Soil Moisture Active Passive Mission (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM are applied with the triple-collocation (TC) method to evaluate the RS SM data regarding random errors and amplitude variances at the regional scale. When the ATI-based SM is regarded as the reference data, the amplitude biases of the other two datasets are determined. The mean bias is also estimated by calculating the mean value difference between the ATI-based and validated RS SM. The results show that the ATI-based SM is a reliable source of reference data that, when combined with the TC method, can correctly estimate the error structure of RS SM datasets in wide space, promoting the reasonable application and calibration of RS SM datasets.
Highlights
Soil moisture (SM) plays an important role in the climate system and influences water, energy, and carbon cycles [1,2,3]
E.g., platform design, instrument configuration, and retrieval algorithm, different remote sensing (RS) SM datasets have their own characteristics [7], which are expressed in their systematic and random errors, and the systematic error consists of mean bias and amplitude bias
This study focuses on providing a feasible scheme for obtaining the error structure of RS SM products
Summary
Soil moisture (SM) plays an important role in the climate system and influences water, energy, and carbon cycles [1,2,3]. To capture the spatial heterogeneity of SM within a large-scale grid, RS ancillary data related to SM with a high spatial resolution relative to RS SM products, e.g., terrain data [20] and optical RS data [21,22,23,24,25], are employed; they have the potential to estimate a reliable reference dataset for decomposing the errors of RS SM products. The amplitude bias between RS SM products and the reference data can be estimated by the TC method, which avoids the influences of random errors. The RS ancillary data related to the SM are transformed into SM values by establishing the relationship with in-situ measurements, which expands the ground-based observation information across a wide spatial extent. This study focuses on providing a feasible scheme for obtaining the error structure of RS SM products
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
