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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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Validation of Soil Temperature Sensing Depth Estimates Using High-Temporal Resolution Data from NEON and SMAP Missions
Passive microwave remote sensing of soil moisture is crucial for monitoring the Earth’s water cycle and surface dynamics. The penetration depth during this process is significant, as it influences the accuracy of retrieved soil moisture data. Within L-band remote sensing, tools such as the τ-z model interpret microwave emissions to estimate soil moisture, taking into account the complex interactions between soil and radiation. However, in validating these models against high-temporal-resolution, ground-based measurements, especially from extensive networks like the Terrestrial National Ecological Observatory Network (NEON), further research and validation efforts are needed. This study comprehensively validates the τ-z model’s ability to estimate the soil temperature sensing depth (zTeff) using data from the NEON and Soil Moisture Active Passive (SMAP) satellite missions. A harmonization process was conducted to align the spatial and temporal scales of the two datasets, enabling rigorous validation. We compared soil optical depth (τ)—a parameter capable of theoretically unifying sensing depth representations across wet soil (~0.05 m) to extreme dry/frozen conditions (e.g., up to ~1500 m in ice-equivalent scenarios)—and geometric depth (z) frameworks against outputs from the τ-z model and NEON’s in situ profiles. The results show that: (1) for the profiles that satisfy the monotonic assumption by the τ-z model, zTeff fits the prediction well at about 0.2 τ for the average; (2) Combining SMAP’s soil moisture, the τ-z model achieves high accuracy in estimating zTeff, with RMSD (0.05 m) and unRMSD (0.03 m), and correlations (0.67) between estimated and observed values. The findings are expected to advance remote sensing techniques in various fields, including agriculture, hydrology, and climate change research.
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- Award ID(s):
- 2217817
- PAR ID:
- 10660640
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 17
- Issue:
- 23
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 3845
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/rs17233845
