## 2 Data and Methods
### Study Area and Validation Scheme  
The study area covers a large region across the Central and Eastern parts of the Popular Republic of China. Figure 4 shows the land cover map (National Institute for Environmental Studies, Tsukuba, Japan, [[http://db.cger.nies.go.jp/dataset/landuse/en/](http://db.cger.nies.go.jp/dataset/landuse/en/)]([http://db.cger.nies.go.jp/dataset/landuse/en/](http://db.cger.nies.go.jp/dataset/landuse/en/))) [60] of the study area where the main land-cover types include, from West to East, bare soil, grassland, and agriculture in the North, and agricultural and woodland, in the South.  
The study area is approximately bounded between latitudes 26\({}^{\circ}\)N and 40\({}^{\circ}\)N, and longitudes 103\({}^{\circ}\)W and 125\({}^{\circ}\)W. The total extension is approximately \(3.6\times 10^{6}\) km\({}^{2}\) (\(1500\times 2400\) km\({}^{2}\)). Due to its large extension, several continental climates are affecting the different regions. For instance, in the North-West region, e.g., Mongolia Plateau, or in the East region, e.g., influenced by the ocean. In general, two climates can be differentiated in our study area, humid subtropical without dry season (Cfa) in the South, and humid subtropical with summer monsoon and dry winter (Cwa) in the North. Humid continental climates at North-East and semi-arid climates at North-West also can be found in the most Northern regions of the study area. However, for SMC assessment and the scope of this  
Figure 4: Landcover map of the study area (available at the National Institute for Environmental Studies, Tsukuba, Japan, [[http://db.cger.nies.go.jp/dataset/landuse/en/](http://db.cger.nies.go.jp/dataset/landuse/en/)]([http://db.cger.nies.go.jp/dataset/landuse/en/](http://db.cger.nies.go.jp/dataset/landuse/en/))).  
Figure 3: SMAP VOD estimates at 500-sample interval for the period 1–13 of April 2019. Units are dimensionless.  
study, these climatic differences are negligible, since we focus on the measurement of the variable itself, including the vegetation influences.  
In Figure 4, natural and agricultural areas can be perfectly identified. For instance, the main agricultural areas can be differentiated in the North-East region, and more naturally vegetated woodland in the South. The main crops cultivated in the agricultural areas are corn and other types of cereals with a typical phenological period, i.e., winter crops, rice in the South and wheat and corn in the northern regions of the study area. Note that during the month of April, these crops are in the growing stage, due to the precipitation regime. This situation is particularity interesting for quantifying the effects of the different mentioned variables on the CYGNSS signal subject to analysis.  
In this study, we employ SMAP SMC estimates to validate our GNSS-R SMC retrieval technique. Figure 5 shows the spatial distribution of SMAP SMC estimates for April 2019, where different subareas can be identified from the day-to-day data collection. Since SMC is sensible even for day-to-night variations and possible precipitations, we classify the data analysis by days and limit our SMC validation at a maximum time difference between SMAP and CYGNSS times of acquisition of about 3 h. We assume that during this \(\pm\)3 h range the SMC state may be considered nearly stable under the absence of precipitation. The information regarding precipitations has been accessed through the China Meteorological Data Service Center ([[https://data.cma.cn/eng](https://data.cma.cn/eng)]([https://data.cma.cn/eng](https://data.cma.cn/eng))), which delivers global precipitation regime maps for the provinces of the People's Republic of China in a 24 h basis, among other options. Finally, in order to select SMAP estimates coincident with CYGNSS SP locations for the validation test, we select the SMAP estimates located in a range inferior of 500 m from each CYGNSS SP location.