An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

Abstract

A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1-RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1-RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active-passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical-vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions. 2 of 20 salinity (SMOS) satellites [10] based on L-band passive microwaves can provide high-accuracy global daily SM products [16]. However, coarse-resolution passive microwave SM data cannot reflect the detailed distribution of surface SM; therefore, many researchers have downscaled coarse-resolution passive microwave SM data based on fine-resolution auxiliary data [17][18][19][20][21][22][23].Some downscaling methods are based on active or passive microwave data including downscaling coarse-resolution microwave brightness temperature (TB) data or coarse-resolution SM data based on microwave backscatter data [22,[24][25][26][27][28][29][30], and downscaling low-frequency passive microwave data based on high-frequency passive microwave data [31][32][33]. Active microwave techniques offer higher spatial resolutions than passive microwave techniques. Downscaling methods based on active microwaves usually leverage the linear or near-linear relationship between microwave TB or SM data and microwave backscatter data in a time series. Vegetation and surface roughness are generally considered time-invariant during long-time series [26]. However, active microwaves are greatly affected by vegetation and surface roughness [34]; therefore, their applications in most areas are limited [35]. Another assumption is that invariance of vegetation and surface roughness occurs in adjacent observations (several days); thus, these techniques req...

A New Approach for Soil Moisture Downscaling in the Presence of Seasonal Difference

“…In the following research, the most suitable downscaling model for each season can be determined according to the importance of explanatory variables. In addition to the auxiliary data selected in this study, we can also consider the infrared data [24,48] and SAR [7,16,18]. Additionally, we chose the seasonal scale for research, since the climate in the study area has four distinct seasons.…”

“…The ground data stations record soil relative humidity. Due to the lack of SM monitoring measurements at 5 cm, we chose soil moisture relative humidity data at a depth of 10 cm, as in past research [16,19,40]. Although the measurement depths are inconsistent, there is a strong correlation between the SM values of the two continuous soil layers [16,41].…”

“…The idea behind this method in downscaling research is to establish the regression relationship between soil moisture and auxiliary variables [15]. Machine learning methods can express well this regression correlation [16]. The machine learning algorithms used for downscaling include random forest (RF), support vector machines (SVM), K-nearest neighbor (KNN) [16][17][18][19][20].…”

“…Machine learning methods can express well this regression correlation [16]. The machine learning algorithms used for downscaling include random forest (RF), support vector machines (SVM), K-nearest neighbor (KNN) [16][17][18][19][20]. These algorithms have their own characteristics, so it is necessary to choose the most suitable algorithm for the research area [19].…”

Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China

“…In a regression, the mean predicted values of all independent decision trees are regarded as the RF model outputs. The adaptive, randomized, and decorrelated features make RF suitable for complex and highly non-linear relationship models [54]. RF is simple and flexible; it does not significantly improve the computation while improving the prediction accuracy.…”