Soil moisture is a critical component of the Earth's water cycle, essential for various environmental and agricultural processes, and its significance is further underscored by the impacts of climate change. The change in soil moisture patterns can have profound implications for hydrological dynamics, agricultural productivity, and ecosystem sustainability. To understand these changes, an initial study was conducted to examine the long-term spatiotemporal evolution of soil moisture and its interactions with key hydrometeorological parameters using coarse-resolution data. Over a 40-year period, it was found that approximately 50% of the global vegetated surface layer (0-7 [cm] depth) experienced significant drying. Conversely, only 9% of the global vegetated area showed an upward trend in soil moisture, largely attributed to increasing precipitation levels. While these results provide valuable insights into broad-scale soil moisture trends and their primary drivers, and highlight the limitations of coarse-resolution data, which fail to capture the finer-scale processes and anthropogenic influences that are critical for understanding micro-scale feedback mechanisms. Therefore, high-resolution soil moisture data is required, but currently, none of the operational missions provide data at such a high resolution. However, the retrieval of high-resolution soil moisture products at a global scale can be achieved in this “Golden Age of SAR”. Among the upcoming L-band SAR missions, NISAR is in the final stages of preparation for launch. Therefore, taking advantage of the upcoming NISAR mission, an algorithm for high-resolution soil moisture retrieval is proposed i.e., “multi-scale” soil moisture retrieval algorithm. This algorithm is based on the disaggregation approach which combines the coarse-resolution (9 [km]) soil moisture data with fine-scale co-polarization and cross-polarization backscatter measurements to retrieve high-resolution soil moisture. The algorithm can take input of coarse resolution soil moisture either from satellite radiometer-based or climate model data. In this study, European Center for Medium Weather Range Forecast (ECMWF) ERA5-Land reanalysis data were used as an input coarse resolution soil moisture data. The ECMWF assimilates a large number of satellite and in-situ information to produce overall very reliable datasets. The major advantage of choosing the input dataset from climate model reduces dependency on satellite mission lifetimes. The end goal of the algorithm is to remove dependencies on any complex modeling, tedious retrieval steps, or multiple ancillary data needs, and subsequently decrease the degrees of freedom to achieve optimal accuracy in soil moisture retrievals. The proposed algorithm targets a spatial resolution of 200 [m], a specific spatial resolution determined based on the user requirements. However, currently due to the unavailability of NISAR data, similar L-band data from UAVSAR acquired during the SMAPVEX-12 campaign and ALOS-2 SAR were utilized for algorithm calibration and validation. The algorithm has been initially tested on selected agricultural sites. The retrieved high-resolution soil moisture was validated with in-situ measurements, and the ubRMSE was below 0.06 [m3/m3], meeting the NISAR mission accuracy goals. Additionally, given the SAR's ability to provide fine-resolution backscatter measurements at 10 [m] spatial resolution. The analysis was conducted at spatial resolutions of 100 [m] and 200 [m] across various hydrometeorological settings globally. This includes sites from polar to arid regions and diverse land use. This retrieval and validation were performed using the ALOS-2 L-band SAR time-series data. The retrieved soil moisture at both spatial resolutions showed consistent patterns, with the finer 100 [m] resolution have more detailed information. The validation statistics show that the algorithm consistently maintained an ubRMSE below 0.06 [m3/m3] at both 100 [m] and 200 [m] spatial resolutions. The performance of the algorithm, even in forested regions with dense canopies, presents the robustness of the algorithm. This is attributed to the L-band SAR frequency's higher penetration capability. However, since these validation statistics are based on limited sites, there is a need to calculate the error in the soil moisture retrieval for each grid to ensure comprehensive accuracy. Recognizing the limitations of in-situ measurements, which are sparse and geographically constrained, an analytical approach to estimate uncertainty in high-resolution soil moisture retrievals for the NISAR mission is also proposed. This approach accounts for errors in the input datasets and algorithm parameters. The approach was applied on the UAVSAR datasets from the SMAPVEX-12 campaign and compared with the ubRMSE for different crop types. The uncertainty estimates closely matches the ubRMSE, demonstrating the robustness of the analytical approach. Overall, this study demonstrates the effectiveness of the proposed algorithm for high-resolution soil moisture retrieval for the NISAR mission and future SAR missions, with the potential to achieve spatial resolutions finer than 100 [m].
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