Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture

Abstract

Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results.

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

“…Soil surface moisture is one of the most challenging land surface parameters to observe accurately in remote sensing quantitative retrieval (Zhao et al, 2003). Therefore, accurate remote sensing-based SM products depend on the ongoing validation, evaluation, and improvement of the retrieval algorithms (Wigneron et al, 2017;Gruber et al, 2020;Wang et al, 2020). Based on the in situ measurement data of the surface SM in Hunan province obtained from two government departments of China, this study conducted a time-series analysis, an authenticity test, and an assessment based on environmental factors on the six mainstream passive microwave remote sensing-based SM products.…”

Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss

“…The normalized difference vegetation index (NDVI) products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) are the most mature and widely used source for monitoring vegetation status [30,31]. In this study, the monthly MODIS NDVI products (MOD13A3, v006) at a 1 km spatial resolution for 2010-2020 were downloaded from the NASA website (https://ladsweb.modaps.eosdis.nasa.gov/).…”

“…Despite of those advantages of the long-term Earth observation dataset and products, there are still some limitations and need to be further improved to achieve better estimates in the future. For example, the FY-3C soil moisture products are likely to overestimate the soil moisture in those areas covered by dense vegetation [30], thus different models should be established and compared to achieve more consistent soil moisture estimates in areas with green plants. There are still omissions errors with the MODIS NDVI images with a 250 m spatial resolution and could be improved using filters to better reflect the regional vegetation status.…”

Reply on RC1

“…In a drought monitoring context, ISMN data have frequently been used in a "convergence of evidence" approach in combination with other drought-related variables or indicators. For example, Scaini et al (2015) (Campo et al, 2011), apparent thermal inertia surface estimates (Notarnicola et al, 2012), data-driven surface and root-zone soil moisture predictions (Kornelsen and Coulibaly, 2014;Manfreda et al, 2014;Wang et al, 2020a), improved satellite albedo products (Liu et al, 2014), and estimates of effective permittivity and brightness temperature of organic soils Park et al (2019).…”