Near‐real‐time one‐kilometre Soil Moisture Active Passive soil moisture data product

Abstract

The coarse resolution soil moisture (SM) data from NASA SMAP mission have been steadily produced with the expected performance since April 2015. These coarse resolution observations could be downscaled to fine resolution using fine scale observations of SM sensitive quantities from existing satellite sensors. For operational users who need near‐real‐time (NRT) high resolution SM data, the downscaling approach should be feasible for operational implementation, requiring limited ancillary information and primarily depending on readily available satellite observations. Based on these principles, nine potential candidate downscaling schemes were selected for developing an optimal downscaling strategy. Using remotely sensed land surface temperature (LST) and enhanced vegetation index (EVI) observations, the optimal downscaling approach was tested for operational producing a NRT 1 km SM data product from SMAP. Comprehensive assessments on the 1 km SM product were conducted based on agreement statistics with in‐situ SM measurements. Statistical results show that the accuracy of the original coarse spatial resolution SMAP SM product can be significantly improved by 8% by the downscaled 1 km SM. With respect to the in‐situ measurements, the 1 km SM mapping capability developed here presents a clear advantage over the SMAP/Sentinel SM data product; and it also provides better data availability for users. This study suggests that a NRT 1 km SMAP SM data product could be routinely generated from SMAP at the centre for Satellite Applications and Research of NOAA NESDIS for operational users.

Enhancing Spatial Resolution of SMAP Soil Moisture Products through Spatial Downscaling over a Large Watershed: A Case Study for the Susquehanna River Basin in the Northeastern United States

“…In general, the application of aforementioned downscaling techniques enhances the spatial resolution of passive microwave SM with varying degrees of accuracy. However, most of these techniques are predominantly applied to the arid and semi-arid climatic regions, and their performances are often site-specific (e.g., [22,25,28,53]), requiring further investigation across diverse geographical and climatic regions with a range of land cover types.…”

Unraveling the Relationship Between Tropical Storms and Agricultural Drought

“…Soil moisture is known as a key variable for agricultural drought monitoring in a multitude of studies (Berg & Sheffield, 2018; Gavahi et al., 2020; Hao & AghaKouchak, 2013; Martínez‐Fernández et al., 2016; L. Xu et al., 2020, 2019; Y. Xu et al., 2018; Yin et al., 2020). Moreover, soil moisture is an important component in the hydrological cycle, which is estimated through in situ stations, satellite remote sensing, or hydrologic modeling.…”

“…Spatial resolution of the soil moisture data derived by satellite are still coarse while hydrological‐ and agricultural applications require finer‐scale (kilometer or sub‐kilometer) soil moisture data due to spatial heterogeneousness (i.e., land use, vegetation, and topography) in regional or local areas (Hssaine et al., 2021; Tian et al., 2020). Different downscaling approaches are developed based on the need of high‐resolution soil moisture data (Guevara & Vargas, 2019; Montzka et al., 2018; Yin et al., 2020). Moreover, recent studies have utilized 1 km scale soil moisture data for drought monitoring and revealed finer resolution data provide more detailed information in heterogeneous areas (Abbaszadeh et al., 2021, 2019; Fang et al., 2021; Gavahi et al., 2020; Yin et al., 2020).…”

“…Different downscaling approaches are developed based on the need of high‐resolution soil moisture data (Guevara & Vargas, 2019; Montzka et al., 2018; Yin et al., 2020). Moreover, recent studies have utilized 1 km scale soil moisture data for drought monitoring and revealed finer resolution data provide more detailed information in heterogeneous areas (Abbaszadeh et al., 2021, 2019; Fang et al., 2021; Gavahi et al., 2020; Yin et al., 2020). Literature indicates the inefficacy of previous studies in identifying the TS‐drought relationship at regional or local level as they did not use the soil moisture data at kilometer or sub‐kilometer scales which are crucial for effective agricultural practices and farm‐level studies (Brun & Barros, 2014; Kam et al., 2013; Maxwell et al., 2013).…”

Retrieval of Sub-Kilometric Relative Surface Soil Moisture With Sentinel-1 Utilizing Different Backscatter Normalization Factors

“…Infiltration crucially depends on near-surface water content; hence having accurate observations and estimates of global soil moisture is vitally important [11]. Unfortunately, current remote sensing observations of soil moisture are on the kilometric spatial scale or greater [12], [13], whereas some NFM practices (such as land-and soil-based management) occur on sub-kilometric spatial scales (i.e., individual field scale) [14]. In order to better understand the variability of soil moisture across different agricultural practices (such as the comparison of different fields and farms) within the context of NFM practices, finer resolution observations of soil moisture are required.…”