Remote Spectral Time-Series Mapping for Field-Scale Soil and Vegetation
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Solution Overview
Problem
Current remote sensing methods fail to accurately estimate soil properties and vegetation indices at high resolution and field scale, requiring extensive data processing and are not cost-effective, which hinders precise agricultural and forestry management.
Innovation Solution
A method combining satellite-derived soil maps with crop and soil models, using coefficients derived from remote data across multiple spectral bands and time frames, and incorporating weather and field data to determine soil and vegetation properties, including texture, moisture, and biomass, with mechanistic growth models for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NDVI is used to estimate vegetation properties, then vegetation parameters can be approximated, but soil properties cannot be estimated
Solution Approach 1:
The patent segments the spectral information into different components by analyzing multiple spectral bands separately. It processes visible, near-infrared, and thermal infrared bands independently to extract different property information, allowing simultaneous estimation of both vegetation and soil properties from the segmented spectral data
Solution Approach 2:
The patent transitions from analyzing single-time-point NDVI values to analyzing temporal derivatives of spectral indices across multiple time frames. By examining the rate of change in spectral bands over time, the system extracts additional dimensional information that reveals both vegetation dynamics and soil properties simultaneously
2Measurement precision
If high resolution vegetation and soil maps are created, then field scale properties can be determined, but it becomes costly and time consuming
Solution Approach 1:
The patent performs preliminary processing of satellite data by calculating temporal derivatives and spectral indices before detailed analysis. It pre-processes multiple spectral bands to extract key features, reducing the computational burden during final property determination and enabling efficient high-resolution mapping
Solution Approach 2:
The patent transforms the analysis from direct property estimation to analyzing rates of change of spectral parameters over time. By using temporal derivatives of spectral indices as input parameters, the system achieves higher resolution property determination with reduced processing requirements compared to traditional methods
3Reliability
If long term NDVI series are analyzed to detect water stress patterns, then Vegetation Condition Index can be calculated, but high data amount and processing work are required
Solution Approach 1:
The patent extracts only the essential temporal derivative information from long-term NDVI series, taking out the critical water stress signal while discarding redundant data. By focusing on the rate of change rather than the complete time series, it maintains detection reliability with significantly reduced processing requirements
Solution Approach 2:
The patent performs preliminary calculation of temporal derivatives and spectral indices from the raw NDVI time series before conducting water stress analysis. This pre-processing step condenses the information content, enabling reliable water stress detection with reduced computational complexity
Data Source
AI summary
A computer-implemented method for determining soil and/or vegetation properties of an agricultural field, wherein the method includes the steps of: receiving remote data over a plurality of time frames, wherein the remote data includes data from at least one determined location including a plurality of spectral bands or optical domains of different wavelengths; and processing the remote data, wherein processing the remote data includes the steps of: generating at least one coefficient derived from the remote data; determining a rate of change of the at least one coefficient at the plurality of time frames; and determining at least one soil and/or vegetation property value based on the rate of change of the at least one generated coefficient for at least one of the plurality of time frames.