Overview
California's Central Valley is one of the most productive agricultural regions in the world, producing over 250 different crops with an annual value of nearly $50 billion. However, the region faces significant challenges related to water scarcity and climate change, making efficient resource management critical for sustainable agriculture.
Satellite Used
Sentinel-2
Time Period
2018-2022
Challenge
A consortium of agricultural cooperatives in California's Central Valley needed to:
- Develop accurate crop yield predictions early in the growing season
- Optimize irrigation scheduling to conserve water while maintaining productivity
- Identify areas of crop stress before they impact yield
- Create a scalable solution that could be applied across diverse crop types and growing conditions
Traditional methods of crop monitoring relied heavily on ground-based observations, which were time-consuming, expensive, and provided limited spatial coverage. The consortium needed a more efficient and comprehensive approach to monitor their crops and make data-driven decisions.
Solution
We implemented a comprehensive crop monitoring and yield prediction system using Sentinel-2 satellite imagery, which offers:
- High spatial resolution (10m) for detailed field-level analysis
- Frequent revisit time (5 days) for regular crop development monitoring
- Multiple spectral bands, including red edge and near-infrared, ideal for vegetation analysis
Key Components of the Solution:
1. Multi-temporal Vegetation Index Analysis
We calculated multiple vegetation indices from Sentinel-2 imagery throughout the growing season, including:
- NDVI (Normalized Difference Vegetation Index) for general crop health
- NDRE (Normalized Difference Red Edge) for early stress detection
- MCARI (Modified Chlorophyll Absorption Ratio Index) for chlorophyll content estimation
- NDWI (Normalized Difference Water Index) for crop water content assessment
2. Machine Learning Yield Prediction Model
We developed a machine learning model that combined:
- Time-series vegetation indices from Sentinel-2
- Historical yield data from the cooperatives
- Weather data (temperature, precipitation, solar radiation)
- Soil characteristics and irrigation records
3. Irrigation Optimization System
Using the NDWI and thermal data, we created an irrigation recommendation system that:
- Identified areas of water stress within fields
- Calculated crop water requirements based on growth stage and environmental conditions
- Generated variable-rate irrigation prescriptions
- Tracked irrigation efficiency over time
Example of NDVI time-series analysis for crop health monitoring
Results
Yield prediction accuracy
Water usage reduction
Increase in profit margin
The implementation of the Sentinel-2 based crop monitoring and yield prediction system delivered significant benefits:
- Early Yield Predictions: Accurate yield forecasts were available 60-90 days before harvest, allowing for better planning of logistics, storage, and marketing.
- Water Conservation: Optimized irrigation scheduling reduced water usage by 22% while maintaining or improving yields.
- Early Stress Detection: The system identified areas of crop stress 2-3 weeks earlier than visual inspection, allowing for timely interventions.
- Improved Resource Allocation: Variable-rate application maps for irrigation and inputs led to more efficient resource use.
- Scalability: The solution was successfully applied across multiple crop types, including almonds, tomatoes, and alfalfa.
"The Sentinel-2 based crop monitoring system has transformed how we manage our operations. We're now able to make data-driven decisions that have significantly improved our water efficiency and profitability. The early yield predictions have been particularly valuable for our planning and marketing efforts."
Conclusion
This case study demonstrates the powerful capabilities of Sentinel-2 satellite imagery for agricultural applications. By leveraging the high spatial resolution, frequent revisit time, and rich spectral information provided by Sentinel-2, we were able to develop a comprehensive crop monitoring and yield prediction system that delivered significant benefits to agricultural producers in California's Central Valley.
The success of this project highlights the value of satellite-based remote sensing for sustainable agriculture, particularly in regions facing water scarcity and climate challenges. The approach developed here can be adapted and applied to other agricultural regions around the world, helping farmers optimize resource use, improve productivity, and enhance resilience to environmental stresses.