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1. Validation of Soil Temperature Sensing Depth Estimates Using High-Temporal Resolution Data from NEON and SMAP Missions

   https://doi.org/10.3390/rs17233845  

   Lv, Shaoning; Ayres, Edward; Hu, Yin (December 2025, Remote Sensing)

   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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2. Rapid Flood Progress Monitoring in Cropland with NASA SMAP

   https://doi.org/10.3390/rs11020191  

   Rahman, Md.; Di, Liping; Yu, Eugene; Lin, Li; Zhang, Chen; Tang, Junmei (January 2019, Remote Sensing)

   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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3. Development and evaluation of low-altitude remote sensing systems for crop production management

   https://doi.org/10.3965/j.ijabe.20160904.2010  

   Huang, Yanbo; Thomson, Steven J.; Brand, Howard J.; Reddy, Krishna N. (January 2016, International journal of agricultural and biological engineering)

   e. Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field. It is built on agricultural mechanization and state-of-the-art technologies of geographical information systems (GIS), global positioning systems (GPS) and remote sensing, and is used to monitor soil, crop growth, weed infestation, insects, diseases, and water status in farm fields to provide data and information to guide agricultural management practices. Precision agriculture began with mapping of crop fields at different scales to support agricultural planning and decision making. With the development of variable-rate technology, precision agriculture focuses more on tactical actions in controlling variable-rate seeding, fertilizer and pesticide application, and irrigation in real-time or within the crop season instead of mapping a field in one crop season to make decisions for the next crop season. With the development of aerial variable-rate systems, low-altitude airborne systems can provide high-resolution data for prescription variable-rate. 

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4. Fiber-optic seismic sensing of vadose zone soil moisture dynamics

   https://doi.org/10.1038/s41467-024-50690-6  

   Shen, Zhichao; Yang, Yan; Fu, Xiaojing; Adams, Kyra H; Biondi, Ettore; Zhan, Zhongwen (December 2024, Nature Communications)

   Abstract Vadose zone soil moisture is often considered a pivotal intermediary water reservoir between surface and groundwater in semi-arid regions. Understanding its dynamics in response to changes in meteorologic forcing patterns is essential to enhance the climate resiliency of our ecological and agricultural system. However, the inability to observe high-resolution vadose zone soil moisture dynamics over large spatiotemporal scales hinders quantitative characterization. Here, utilizing pre-existing fiber-optic cables as seismic sensors, we demonstrate a fiber-optic seismic sensing principle to robustly capture vadose zone soil moisture dynamics. Our observations in Ridgecrest, California reveal sub-seasonal precipitation replenishments and a prolonged drought in the vadose zone, consistent with a zero-dimensional hydrological model. Our results suggest a significant water loss of 0.25 m/year through evapotranspiration at our field side, validated by nearby eddy-covariance based measurements. Yet, detailed discrepancies between our observations and modeling highlight the necessity for complementary in-situ validations. Given the escalated regional drought risk under climate change, our findings underscore the promise of fiber-optic seismic sensing to facilitate water resource management in semi-arid regions. 

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5. Dependence of Soil Moisture and Strength on Topography and Vegetation Varies Within a SMAP Grid Cell

   https://doi.org/10.3390/hydrology12020034  

   Bindner, Joseph R; Proulx, Holly; Wickham, Kevin; Niemann, Jeffrey D; Scalia, Joseph; Green, Timothy R; Grazaitis, Peter J (February 2025, Hydrology)

   Off-road vehicle mobility assessments rely on fine-resolution (~10 m) estimates of soil moisture and strength across the region of interest. Such estimates are often produced by downscaling soil moisture from a microwave satellite like SMAP, then using the soil moisture in a soil strength model. Soil moisture downscaling methods typically assume consistent relationships between the moisture and topographic, vegetation, and soil composition characteristics within the microwave satellite grid cells. The objective of this study is to examine whether soil moisture and strength exhibit heterogenous dependencies on topography, vegetation, and soil composition characteristics within a SMAP grid cell. Soil moisture and strength data were collected at four geographically separated regions within a 9 km SMAP grid cell in the Front Range foothills of northern Colorado. Laboratory methods and pedotransfer functions were used to characterize soil attributes, and remote sensing data were used to determine topographic and vegetation attributes. Pearson correlation analyses were used to quantify the direction, strength, and significance of the relationships of both soil moisture and strength with topography, vegetation, and soil composition. Contrary to the common assumption, spatial variations in the slope and correlation of the relationships are observed for both soil moisture and strength. The findings indicate that improved predictions of soil moisture and soil strength may be achievable by soil moisture downscaling procedures that use spatially variable parameters across the downscaling extent. 

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