Evaluation of Six Satellite-Based Soil Moisture Products Based on in situ Measurements in Hunan Province, Central China

Abstract

Long-term near-surface soil moisture (SM) data can be obtained on a regional scale through microwave remote sensing. Therefore, to quantitatively analyze the accuracy of multisource remote sensing–based observation products, improve the retrieval algorithm, and effectively use in terminal environments, a standardized comprehensive evaluation is imperative. The SM data obtained by the China Meteorological Administration and Ministry of Water Resources were used as reference data to verify the performance of six passive microwave remote sensing–based SM products from the SMOS, SMAP, GCOM-W, FY-3B, and FY-3C satellites in Hunan province, China. These data were also used to analyze the effects of topographical, land cover, and meteorological factors on SM retrieval accuracy. Results show that SMAP shows the best overall performance in Hunan province; furthermore, it exhibits stable accuracy and is not easily affected by environmental factors. The FY series of satellite products shows the worst performance, and some grid remote sensing data are negatively correlated with the ground measurement data. AMSR2 possesses the largest amount of data and the largest deviation, and only this product exhibits significant differences with the fluctuation trend of the measured SM and precipitation. Passive microwave detection technology presents the best performance in the central part of Hunan province and the largest bias in the Dongting Lake area. SMOS-L3 and SMOS-IC, two products of the same satellite, show the lowest bias but present differences in the SM fluctuation range, orbital accuracy, as well as dry or wet bias. Furthermore, FY-3B and FY-3C, two satellites belonging to the same series, exhibit excellent consistency in performance. The evaluation results and accuracy variation between products as well as other factors identified in the study provide a baseline reference for improving the retrieval algorithm. This study provides a quantitative basis for developing improved applications of passive microwave SM products.

Modeling shallow soil moisture dynamics in mountainous landslide active regions

“…During droughts, on the other hand, predictions of shallow soil moisture content are critical for agricultural production (Fan et al, 2022). In Yufeng, dry seasons account for about 6 months in a year.…”

“…Under serious concerns about climate change, multi-dimensional projections of the tendency and frequency of extremes in temperature and precipitations (IPCC, 2014;Deshmukh and Singh, 2016) all demonstrate higher risks of storm-induced soil erosions or landslides (Brocca et al, 2007;Strobbia and Cassiani, 2007;Zhou et al, 2021) and drought-related issues. The increasing risks bring great challenges to landslide hazard mitigation, water scarcity preparation, and agriculture adaptations (Fan et al, 2022;Tan et al, 2022), particularly in mountain regions that are highly vulnerable to climate change.…”

What Is the Impact of the Establishment of Natural Reserves on Rural Residential Land? An Empirical Study From Hunan Province, China

“…China's Hunan Province governs 14 prefecture-level cities (including autonomous prefectures), with a total of 122 county-level administrative regions (including county-level cities, districts, and autonomous counties, uniformly called counties), and is located in the middle reaches of the Yangtze River (Figure 1). Superior natural conditions have created the characteristics of rich animals and plant biodiversity and a beautiful natural landscape in Hunan Province (Ding et al, 2022;Fan et al, 2022). Provincial or national natural reserves in Hunan Province were distributed in 27, 42, and 44 counties in 2000, 2010, and 2020, respectively.…”

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

“…Since the early 1980s, when the Scanning Multichannel Microwave Radiometer, carried by Nimbus‐8, began providing SM products, there have been developments to improve the spatial and temporal accuracy and resolution of products (Fan et al., 2022). As such, the market presents numerous remote sensing‐based SM projects retrieved from various satellites using various algorithms, for example, the Soil Moisture and Ocean Salinity of the European Space Agency with a spatial resolution of 35–50 km (Xiao et al., 2017), the Soil Moisture Active and Passive of the National Aeronautics and Space Administration with a spatial resolution of 36 km (Fan et al., 2022), and the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR‐E/2), of Japan Aerospace Exploration Agency's; with a spatial resolution of 25 km (Yan & Bai, 2020). The challenge with these SM products is their coarse spatial resolution; hence, an alternative method to estimate SM at a local scale is required.…”