Remote Sensing Irrigation Mapping with Temporal Compositing
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Solution Overview
Problem
Current methods for mapping irrigation at high resolution are limited by data availability and aggregation to large administrative regions, lacking comprehensive field-specific data and timeliness, which hinders field-scale analysis.
Innovation Solution
The development of automated methods using remote sensing imagery, specifically employing a boosted tree approach and ensemble models to predict irrigation practices at medium to high spatial resolutions, enabling pixel-level mapping and field-level decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated remote sensing methods are used to map irrigation at high resolution, then mapping precision and timeliness are improved, but data availability and processing complexity increase
Solution Approach 1:
The patent segments the irrigation mapping process into distinct computational stages: data acquisition from multiple satellites, temporal compositing to handle cloud cover, feature extraction using vegetation and water indices, and machine learning classification. This segmentation allows each stage to be optimized independently, improving overall precision while managing complexity through modular processing.
Solution Approach 2:
The patent transforms raw satellite imagery into multiple derived parameters including NDVI (vegetation index), NDWI (water index), and temporal composites at different resolutions. These parameter transformations convert complex spectral data into meaningful irrigation indicators, enhancing measurement precision while the automated computation manages the complexity of multiple parameter calculations.
2Adaptability or versatility
If field-specific irrigation data is collected at high resolution, then decision-making utility is improved, but data aggregation requirements and storage needs increase
Solution Approach 1:
The patent applies local quality by processing and analyzing satellite data at varying spatial resolutions tailored to different agricultural scales. Field-specific analyses use higher resolution data where needed, while regional summaries use aggregated lower-resolution composites. This approach enables detailed field-scale decision-making without requiring all areas to store and process maximum-resolution data, optimizing data volume requirements.
Solution Approach 2:
The patent adds the temporal dimension by creating time-series composites of irrigation data at multiple resolutions. Instead of storing only high-resolution snapshots, the system generates temporal aggregates that capture irrigation patterns over growing seasons. This dimensional transformation allows field-specific analysis capabilities while reducing spatial data volume through temporal summarization.
3Manufacturing precision
If pixel-level irrigation labeling is performed, then mapping detail and accuracy are improved, but computational time and processing resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing vegetation and water indices from satellite imagery before irrigation classification. Temporal composites are generated in advance to account for cloud cover and seasonal variations. These preparatory computations are performed once and reused across multiple classification iterations, improving label accuracy without proportionally increasing processing time for the actual irrigation detection.
Solution Approach 2:
The patent replaces manual or simple rule-based irrigation detection with machine learning classifiers that automatically interpret multi-temporal satellite data. The system substitutes complex manual analysis with automated algorithms that process pixel-level data efficiently, achieving high labeling accuracy while maintaining practical processing speeds through optimized computational models.
Data Source
AI summary
Detection of field irrigation through remote sensing is provided. In various embodiments, at least one time series of index rasters for a geographic region is read. A time series of weather data for the geographic region is read. The at least one time series of index rasters and the time series of weather data are divided into a plurality of time windows. The at least one time series of index rasters is composited within each of the plurality of time windows, yielding a composite index raster for each of the at least one time series of index rasters in each of the plurality of time windows. The time series of weather data is composited within each of the plurality of time windows, yielding composite weather data in each of the plurality of time windows. The composite index rasters and composite weather data are provided to a trained classifier. A pixel irrigation label for each pixel of the composite index rasters is obtained therefrom. Each pixel irrigation label indicates the presence or absence of irrigation at the associated pixel.