Root Zone Soil Moisture Estimation Using Hybrid Remote Sensing
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Solution Overview
Problem
Estimating soil moisture under agricultural vegetation at different growth stages is challenging due to limited acquisition frequency of L-band SAR satellite data and the lack of consideration for initial bare surface soil moisture conditions in existing systems, leading to improper irrigation practices.
Innovation Solution
A method and system for root zone soil moisture estimation using remote sensing that combines bare surface soil moisture estimation with satellite-derived indexes like NDVI, LAI, and RVI, and a soil water balance model to compute root zone water balance, facilitating accurate spatial distribution of soil moisture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If L-band SAR satellite data is used for soil moisture estimation under vegetation, then penetration capability through dense vegetation is improved, but acquisition frequency is reduced
Solution Approach 1:
The patent combines L-band SAR data with optical satellite data (NDVI, LAI, RVI) and soil water balance models to create a hybrid estimation system. This merging allows the system to leverage the penetration capability of L-band while compensating for its low acquisition frequency using optical data and process-based models, thereby resolving the contradiction between reliability and productivity.
Solution Approach 2:
The patent introduces intermediate variables such as vegetation indices (NDVI, LAI, RVI) and soil water balance parameters as mediators between remote sensing data and soil moisture estimation. These intermediaries translate optical satellite data and L-band SAR data into meaningful soil moisture estimates, enabling frequent monitoring while maintaining accuracy through the mediation of multiple data sources and computational models.
2Device complexity
If existing soil moisture estimation models are used, then computation is simplified, but initial bare surface soil moisture conditions are not considered
Solution Approach 1:
The patent performs preliminary estimation of bare surface soil moisture conditions using L-band SAR data and optical satellite data before calculating vegetation surface soil moisture. This preliminary action establishes the baseline soil moisture state, which is then used to compute changes under vegetation cover. By performing this preliminary action, the system improves measurement precision without excessive complexity, as the preliminary estimation uses straightforward remote sensing data processing.
Solution Approach 2:
The patent segments the soil moisture estimation process into distinct components: bare surface soil moisture estimation, vegetation surface soil moisture estimation, and root zone soil moisture estimation. Each segment uses appropriate data and models for that specific layer, allowing the system to maintain manageable complexity while improving overall precision by addressing each component separately with specialized methods.
3Device complexity
If point-based soil water balance model is used, then computation is simplified, but spatial distribution capability is limited
Solution Approach 1:
The patent transitions from point-based soil water balance calculations to spatially distributed estimation by incorporating satellite-derived spatial variables (NDVI, LAI, RVI) and applying the model across multiple pixels or grid cells. This dimensionality change allows the system to maintain the computational simplicity of process-based models while extending their spatial distribution capability to cover entire agricultural areas, resolving the contradiction between complexity and coverage.
Data Source
AI summary
This disclosure relates generally to root zone moisture estimation for vegetation cover using remote sensing. Conventionally, it is challenging to estimate root zone soil moisture using only satellite data. Moreover, estimation of soil moisture under vegetation cover based on bare surface soil moisture and vegetation parameters is not available. The disclosed method and system facilitate estimation of an ensemble of soil moisture under vegetation cover and root zone soil moisture using process based soil water balance for spatial estimation of root zone soil moisture. The system estimates bare surface soil moisture for different soil types/textures using the baseline bare surface model and soil properties derived from satellite data and in-situ sensors. The method further provides temporal spatially distributed soil moisture inputs to an intelligent irrigation management/information system which is very important to reduce and regulate water consumption.