Comprehensive Evaluation of Using TechDemoSat-1 and CYGNSS Data to Estimate Soil Moisture over Mainland China

Abstract

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a new opportunity for land observation. This study is the first to compare and evaluate the performance of the only two spaceborne GNSS-R satellite missions whose data are publicly available, i.e., the UK’s TechdemoSat-1 (TDS-1) and the US’s Cyclone Global Navigation Satellite System (CYGNSS), for sensitivity analysis with SMAP SM on a daily basis and soil moisture (SM) estimates on a monthly basis over Mainland China. For daily sensitivity analysis, the two data were matched up and compared for the period (i.e., May 2017 through April 2018) when they coexisted (R = 0.561 vs R = 0.613). For monthly SM estimates, a back-propagation artificial neural network (BP-ANN) was used to construct a model using data from more than two years. The model was subsequently used to derive long-term and continuous SM maps over Mainland China. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China (R = 0.676, MAE = 0.052 m3m-3, and ubRMSE = 0.060 m3m-3 for TDS-1; R = 0.798, MAE = 0.040 m3m-3, and ubRMSE = 0.062 m3m-3 for CYGNSS). The retrieved results were further validated using monthly in situ SM data from dense sites across Mainland China. It was found that the SM derived from the TDS-1/ CYGNSS also correlated well with in situ SM (R = 0.687, MAE = 0.066 m3m-3, and ubRMSE = 0.056 m3m-3 for TDS-1; R = 0.724, MAE = 0.052 m3m-3, and ubRMSE = 0.053 m3m-3 for CYGNSS). The results in this study suggested that TDS-1/CYGNSS and the upcoming spaceborne GNSS-R mission could be new and powerful data sources to produce SM data set at a large scale and with relatively high precision.

Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

“…After correcting for the GPS transmitting power, antenna gain, signal travel distance, and wavelength, the derived reflectivity can be representative of the soil water content level and land surface roughness and vegetation conditions. Following [21,22,[24][25][26][27][28][29], the CYGNSS reflectivity is calculated assuming a coherent reflection over land. This reflectivity derived using the coherent reflection assumption has demonstrated a satisfactory performance for estimating surface SM in various previous studies.…”

“…To achieve this, CYGNSS uses eight small satellites that orbit the tropics (within ±38 • latitudes). The considerable amount of CYGNSS land observation data also measured in this region has greatly contributed to the development of new SM retrieval approaches from spaceborne GNSS-R [21][22][23][24][25][26][27][28][29].…”

Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression With a Preclassification Approach

“…The quality of CYGNSS and SMAP data was needed to be evaluated before modeling since the quality of data directly related to the performance of the ML predictions. Hence, the obtained CYGNSS observables calculated for each specular point (SP) acquisition and SMAP data gridded daily over the EASE-grid were first filtered according to the following rules: (1) The CYGNSS reflectivity had to be positive and smaller than 0.1 to remove the anomalies [33]; (2) the incident angle of CYGNSS data was above than 60°are commonly disregarded (a degradation in data quality often occurs at larger incidence angles [39]) [30], [37]; (3) the negative antenna gain in the direction of the specular point (corresponding to uncertainties reported in the measured antenna gain patterns) was removed [28], [34], [35], [37]; (4) to ensure that the error in the CYGNSS SP location estimation is within a reasonable range, only the BRCS data with a Delay Doppler Map (DDM) peak position between the 5th and 11th bins in the delay axis were preserved [35], [40]; (5) the SMAP "retrieval successful/unsuccessful" quality flag was used as well to filter the SMAP data to ensure the quality of SM estimation [31], [41].…”

“…The previous study [32]- [37] has tried to add alternative ancillary data to improve the accuracy of SM estimates. We found that most of the added ancillary data are related to terrain, such as topography and soil texture [32]- [37].…”

“…The number of ancillary data that are from other sources was not reduced [35], [36]. Yang et al [37] also used backpropagation (BP)-ANN to compare and evaluate the SM estimation performance of the two spaceborne GNSS-R satellite missions (TDS and CYGNSS), which has quite a few (six) ancillary variables. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China.…”