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.
Highlights
Soil moisture (SM) is an important component in global water and energy cycles, and it plays a crucial role in driving hydrological and land surface processes [1]
Only a single vegetation index (VI) and air temperature were used in the above SM retrieval, the results indicated that the machine learning method with synthetic aperture radar (SAR) data, vegetation indexes (VIs), and land surface temperature (LST) as input variables might be effective for downscaling coarse SM data
A method was presented to produce downscaled soil moisture active passive (SMAP) SM data by combining optical/infrared data with SAR data based on the random forest (RF) technique
Summary
Soil moisture (SM) is an important component in global water and energy cycles, and it plays a crucial role in driving hydrological and land surface processes [1]. SM can be obtained from station-based measurements, data assimilation products based on land surface models, and remote sensing monitoring data. In areas with sparsely distributed stations, data assimilation products by means of station-based measurements cannot fully reflect the spatial and temporal variations of surface SM [12,13]. Passive microwaves can generate accurate surface SM estimates because they are less affected by vegetation, soil surface roughness, topography, and water content [14,15]. The soil moisture active passive (SMAP) [14] and soil moisture and ocean salinity (SMOS) satellites [10] based on L-band passive microwaves can provide high-accuracy global daily SM products [16]. Coarse-resolution passive microwave SM data cannot reflect the detailed distribution of surface SM; many researchers have downscaled coarse-resolution passive microwave SM data based on fine-resolution auxiliary data [17,18,19,20,21,22,23]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
