Irrigation Kc Forecasting with Multi-Satellite Data Fusion
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Solution Overview
Problem
Current irrigation management systems in precision agriculture face challenges in obtaining frequent and accurate crop coefficient (Kc) values due to limitations in satellite imagery frequency and high costs, which hinders efficient water resource management.
Innovation Solution
The system utilizes a multiplicity of remote sensors providing high and low-resolution imagery data to generate near-real-time and forecast Kc values, enabling frequent updates and precise irrigation planning with variable spatial resolution, including recommendations for irrigation time, quantity, and water quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite imagery is used to obtain crop coefficient values, then measurement precision is improved, but productivity deteriorates due to infrequent revisit time (7-16 days)
Solution Approach 1:
The patent combines data from multiple satellite platforms (Landsat, Sentinel-2, MODIS) with different revisit frequencies into a unified processing system. By merging these data sources, the system achieves frequent updates (every 3-5 days) while maintaining measurement precision through multi-source validation and data fusion techniques.
Solution Approach 2:
The system performs preliminary processing of satellite imagery data including atmospheric correction, cloud masking, and quality assessment before generating crop coefficient values. This preliminary action ensures that only high-quality data are used, maintaining measurement precision while enabling more frequent processing cycles.
2Measurement precision
If high-resolution satellite imagery is used, then measurement precision is improved, but cost increases significantly
Solution Approach 1:
The patent applies different spatial resolutions to different areas within a field based on local requirements. High-resolution imagery is used only where needed for precise irrigation management, while lower-resolution data are used in other areas. This local quality approach maintains measurement precision where required while reducing overall data acquisition costs.
Solution Approach 2:
The system processes multiple types of satellite imagery (optical, thermal, radar) from different sources using a universal processing framework. This multi-functionality allows the system to achieve high measurement precision by selecting the most appropriate data source for each condition, reducing reliance on expensive high-resolution imagery alone.
3Productivity
If frequent satellite imagery acquisition is performed, then productivity is improved, but cost increases
Solution Approach 1:
The system acquires and processes only the necessary portion of satellite imagery data required for crop coefficient calculation. Rather than processing all available imagery, it selectively processes data that meet quality criteria and are relevant to the current growth stage, achieving frequent updates while controlling costs through partial action.
Solution Approach 2:
The patent utilizes freely available or low-cost satellite imagery from multiple public sources (Landsat, Sentinel-2, MODIS) to achieve frequent updates. By relying on these cost-effective data sources and processing them through an efficient algorithm, the system maintains high productivity without incurring significant data acquisition costs.
Data Source
AI summary
The present invention relates to the field of precision agriculture, particularly to systems and methods for providing crop coefficient (Kc) values including near real time and forecast values for the management of precise agricultural irrigation.