Evaluation of Satellite-Derived Surface Soil Moisture Products over Agricultural Regions of Canada

Abstract

Soil moisture is a critical indicator for climate change and agricultural drought, but its measurement is challenging due to large variability with land cover, soil type, time, space and depth. Satellite estimates of soil moisture are highly desirable and have become more widely available over the past decade. This study investigates and compares the performance of four surface soil moisture satellite datasets over Canada, namely, Soil Moisture and Ocean Salinity Level 3 (SMOS L3), versions 3.3 and 4.2 of European Space Agency Climate Change Initiative (ESA CCI) soil moisture product and a recent product called SMOS-INRA-CESBIO (SMOS-IC) that contains corrections designed to reduce several known sources of uncertainty in SMOS L3. These datasets were evaluated against in situ networks located in mostly agricultural regions of Canada for the period 2012 to 2014. Two statistical comparison methods were used, namely, metrics for mean soil moisture and median of metrics. The results suggest that, while both methods show similar comparisons for regional networks, over large networks, the median of metrics method is more representative of the overall correlation and variability and is therefore a more appropriate method for evaluating the performance of satellite products. Overall, the SMOS products have higher daily temporal correlations, but larger biases, against in situ soil moisture than the ESA CCI products, with SMOS-IC having higher correlations and smaller variability than SMOS L3. The SMOS products capture daily wetting and drying events better than the ESA CCI products, with the SMOS products capturing at least 75% of observed drying as compared to 55% for the ESA CCI products. Overall, for periods during which there are sufficient observations, both SMOS products are more suitable for agricultural applications over Canada than the ESA CCI products, even though SMOS-IC is able to capture soil moisture variability more accurately than SMOS L3.

Microwave soil moisture products in North China Plain based on triple collocation method applicability evaluation

“…At present, the accuracy assessment and applicability evaluation of microwave remote sensing SM products have become a research hotspot for many experts and scholars. Yaasiin Oozeer et al conducted an evaluation and analysis of SMOS L3, ESA CCI, and SMOS-IC remote sensing satellite products in Canada, revealing the high applicability of SMOS L3 products in Canada, with both SMOS products suitable for agricultural SM monitoring [4] . Cui et al performed a detailed assessment of FY-3B and seven other satellite SM products, discovering that FY-3B products demonstrated the best temporal consistency with ground-based observational data in Spain, while SMAP products exhibited the best performance in the United States region [5] .…”

A Machine Learning Framework for Predicting and Understanding the Canadian Drought Monitor

“…Another method uses satellite‐based vegetation parameters to monitor plant response to drought conditions (Klisch & Atzberger, 2016; Liu et al., 2021), sometimes in combination with satellite‐derived land surface temperatures (Karnieli et al., 2010; Son et al., 2012). Other effective earth observation techniques use the Gravity Recovery and Climate Experiment (GRACE) satellites to estimate terrestrial water storage anomalies (Houborg et al., 2012; K. S. Kumar et al., 2021; Thomas et al., 2014; Tian et al., 2021) or satellites operating in the microwave spectrum to estimate soil moisture anomalies (Champagne et al., 2018; Narasimhan & Srinivasan, 2005; Oozeer et al., 2020; Tao et al., 2021).…”

Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events

“…With data from dual networks, correlation is commonly used to show the overall consistency of products and observations. Anomaly-based correlation is also used to check the short-term consistency in capturing soil wetting and drying events [17,18]. Dozens of stations in dense networks can approximate grid-scale SM, based on which bias and unbiased root mean square error (ubRMSE) can be quantified [14,19].…”