Abstract Root zone soil moisture (RZSM) is a dominant control on crop productivity, land-atmosphere feedbacks, and the hydrologic response of watersheds. Despite its importance, obtaining gap-free daily moisture data remains challenging. For example, remote sensing-based soil moisture products often have gaps arising from limits posed by the presence of clouds and satellite revisit period. Here, we retrieve a proxy of daily RZSM using the actual evapotranspiration (ETa) estimates from Surface Flux Equilibrium Theory (SFET). Our method is calibration-less, parsimonious, and only needs widely available meteorological data and standard land-surface parameters. Evaluation of the retrievals at Oklahoma Mesonet sites shows that our method, overall, matches or outperforms widely available RZSM estimates from three markedly different approaches, viz. remote sensing data based Atmosphere-Land EXchange Inversion (ALEXI) model, the Variable Infiltration Capacity (VIC) model, and the Soil Moisture Active Passive (SMAP) mission RZSM data product. When compared with in-situ observations, unbiased root mean square difference of retrieved RZSM were 0.03 (m 3 m −3 ), 0.06 (m 3 m −3 ), and 0.05 (m 3 m −3 ) for our method, the ALEXI model, and the VIC model, respectively. Better performance of our method is attributed to the use of both SFET for the estimation of ETa and non-parametric kernel-based method used to relate the RZSM with ETa. RZSM from our method may serve as a more accurate and temporally-complete alternative for a variety of applications including mapping of agricultural droughts, assimilation of RZSM for hydrometeorological forecasting, and design of optimal irrigation schedules.
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Rapid Flood Progress Monitoring in Cropland with NASA SMAP
Research in different agricultural sectors, including in crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used in the mapping and monitoring of floods. However, the inability of optical remote sensing to cloud penetration and the scarcity of fine temporal resolution SAR data hinder the application of flood mapping in many cases. Soil Moisture Active Passive (SMAP) level 4 products, which are model-driven soil moisture data derived from SMAP observations and are available at 3-h intervals, can offer an intermediate but effective solution. This study maps flood progress in croplands by incorporating SMAP surface soil moisture, soil physical properties, and national floodplain information. Soil moisture above the effective soil porosity is a direct indication of soil saturation. Soil moisture also increases considerably during a flood event. Therefore, this approach took into account three conditions to map the flooded pixels: a minimum of 0.05 m3m−3 increment in soil moisture from pre-flood to post-flood condition, soil moisture above the effective soil porosity, and the holding of saturation condition for the 72 consecutive hours. Results indicated that the SMAP-derived maps were able to successfully map most of the flooded areas in the reference maps in the majority of the cases, though with some degree of overestimation (due to the coarse spatial resolution of SMAP). Finally, the inundated croplands are extracted from saturated areas by Spatial Hazard Zone areas (SHFA) of Federal Emergency Management Agency (FEMA) and cropland data layer (CDL). The flood maps extracted from SMAP data are validated with FEMA-declared affected counties as well as with flood maps from other sources.
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- Award ID(s):
- 1739705
- NSF-PAR ID:
- 10109819
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 11
- Issue:
- 2
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 191
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs11020191
