Location: Hydrology and Remote Sensing LaboratoryTitle: Assessment and combination of SMAP and Sentinel-1A/B derived soil moisture estimates with land surfacemodel outputs in the Mid-Atlantic coastal plain, U.S.A.
|KIM, H. - University Of Virginia|
|LEE, S. - University Of Maryland|
|LAKSHMI, V - University Of Virginia|
|KWON, Y - University Of Maryland|
Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/18/2019
Publication Date: 5/19/2020
Citation: Kim, H., Lee, S., Cosh, M.H., Lakshmi, V., Kwon, Y., McCarty, G.W. 2020. Assessment and combination of SMAP and Sentinel-1A/B derived soil moisture estimates with land surfacemodel outputs in the Mid-Atlantic coastal plain, U.S.A.. IEEE Transactions on Geoscience and Remote Sensing. 59(2):991-1011. https://doi.org/doi:10.1109/TGRS.2020.2991665.
Interpretive Summary: Soil moisture (SM) is a key indicator of conditions for crop growth as well as landscape biogeochemical processes involving fluxes and storage of soil nutrients and carbon. This has led to the need for developing robust SM monitoring technologies involving space-based sensors. The SMAP (Soil Moisture Active Passive) satellite has proven to be an effective method of monitoring SM content at fairly coarse scale resolutions (36 km grid). In this study, we tested the ability to increase spatial resolution of the SM product to as high as 1 km resolution by combining the passive coarse scale SMAP data with finer scale active radar data from other satellites (Sentinel-1 series) that are less sensitive to SM status. This study demonstrated the ability to generate fine scale SM maps from combined SMAP/Sentinel data enabling improved monitoring of SM condition in agricultural landscapes and improved management of watersheds for irrigation water use and other water quality/quantity outcomes.
Technical Abstract: Prediction of large-scale water-related natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks can benefit from the high-spatial-resolution soil moisture (SM) data of satellite and modeled products since antecedent SM conditions in the topsoil layer govern the partitioning of precipitation into infiltration and runoff. SM data retrieved from Soil Moisture Active Passive (SMAP) have proved an effective method of monitoring SM content at different spatial resolutions: 1) Radiometer-based product gridded at 36-km, 2) Radiometer-only enhanced posting product gridded at 9-km, and 3) SMAP/Sentinel-1A/B products at 3- and 1-km. In this study, we focused on 9-, 3-, and 1-km SM products: three products were validated against in-situ data using conventional metrics and triple collocation analysis (TCA) and were then merged with a Noah-Multiparameterization version-3.6 (NoahMP36) land surface model (LSM). An exponential filter and a cumulative density function (CDF) were applied for further evaluation of the three SM products, and the maximize-R method was applied to combine SMAP and NoahMP36 SM data. CDF-matched 9-, 3-, and 1-km SMAP SM data showed reliable performance: R and ubRMSD values of the CDF-matched SMAP products were 0.562, 0.609, 0.548; and 0.060-, 0.053-, and 0.056-m3/m3, respectively. When SMAP and NoahMP36 were combined, the R-values for 9-, 3-, and 1-km SMAP SM data were greatly improved: R-values were 0.771, 0.778, and 0.763; and ubRMSDs were 0.044-, 0.036-, and 0.039-m3/m3, respectively. These results indicate the potential uses of SMAP/Sentinel data for improving regional-scale SM estimates and for