Details:

Journal Title:
Remote Sensing of Environment
Personal Author:
Vergopolan, Noemi ; Chaney, Nathaniel W. ; Beck, Hylke E. ; Pan, Ming ; Sheffield, Justin ; ... More +
Vergopolan, Noemi ; Chaney, Nathaniel W. ; Beck, Hylke E. ; Pan, Ming ; Sheffield, Justin ; Chan, Steven ; Wood, Eric F. Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research)
Description:
Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Recent satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). The framework was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m soil moisture. We validated the downscaled soil moisture estimates at four experimental watersheds with dense in-situ soil moisture networks in the United States and obtained overall high correlations (> 0.81) and good mean KGE score (0.56). The downscaled product captures the spatial and temporal soil moisture dynamics better than SMAP L3 and L4 product alone at both field and watershed scales. Our results highlight the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications.
Keywords:
[+]
Computers In Earth Sciences Geology Soil Science
Source:
Remote Sensing of Environment, 242, 111740
DOI:
https://doi.org/10.1016/j.rse.2020.111740
ISSN:
0034-4257
Format:
PDF
Publisher:
Elsevier BV
Document Type:
Journal Article
Funding:
Grant no. NA19OAR4590199
Rights Information:
Accepted Manuscript
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:3ecd7952eb5deaa06674a22453c2b0be22a3377431c01be92c1b4237d886247e
Download URL:
https://repository.library.noaa.gov/view/noaa/57263/noaa_57263_DS1.pdf
File Type:

Supporting Files

• No Additional Files

More +