Abstract
Satellite-based retrieval of forest soil moisture (SM) and vegetation optical depth (VOD) are two long-standing unresolved issues hindering advances in hydrology, ecology, and Earth system science. A key obstacle is the lack of adequate reference data in forested regions. NASA's Soil Moisture Active Passive (SMAP) mission, with its partners, conducted the SMAP Validation Experiment 2019–2022 (SMAPVEX19–22) to improve the SMAP SM and VOD retrievals in temperate forests of the northeastern USA. The scope and scale of the campaign exceeded anything done thus far to develop forest satellite-based SM and VOD retrieval algorithms. The field campaign measured SM, surface conditions, and vegetation properties, with results demonstrating the value of tree sensors with SM measurements and destructive sampling of the vegetation water content of branches and leaves to capture the water distribution in soil and trees. Using low-cost zenith-pointing cameras proved effective in tracking vegetation phenology, aiding the interpretation of brightness temperature (TB). Airborne and mobile terrestrial laser scanning measurements captured the three-dimensional forest structure necessary for microwave measurement interpretation. Challenges included characterizing SM in organic forest soils and determining volumetric SM due to spatially variable soil bulk density. Comparisons of the field measurements with SMAP data revealed its ability to retrieve the soil permittivity (correlation of 0.68 and 0.75 for the two experiment sites) alongside VOD, including the frozen conditions. The findings indicated that L-band scattering albedo is temporally variable, and L-band TB is sensitive to deciduous forest leaves, influencing the development of SM and VOD retrieval algorithms.
| Original language | English |
|---|---|
| Pages (from-to) | 10749-10771 |
| Number of pages | 23 |
| Journal | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Volume | 18 |
| DOIs | |
| State | Published - 2025 |
Keywords
- laser radar
- passive microwave remote sensing
- remote sensing
- Soil moisture
- vegetation