CropWatch has established a partitioned, hierarchical, and multi-resolution remote sensing monitoring framework to tailored the characteristics of global agricultural production. This framework comprises 54 remote sensing monitoring indicators and 15 foundational datasets spanning agroclimatic, cropping systems, agronomic, crop conditions, yield, production, disasters, and early warning. By leveraging technologies such as Earth big data and cloud computing, a unique and dynamically updated fundamental cropland resources dataset has been developed. Furthermore, using global agro-ecological zones as the basic monitoring units, CropWatch has constructed a results-oriented monitoring unit–model mapping table. This enables the adaptive selection and self-calibration of parameters for remote sensing models that monitor regional crop area, yield, and crop conditions, effectively addressing the regional adaptability of agricultural monitoring indicators and models.

Integrating global climate zones, crop planting patterns, and crop type information, CropWatch classifies the world into 105 primary and 665 secondary agro-ecological zones, covering 173 countries along with their provincial and county-level administrative units. At the global scale, CropWatch monitors and analyzes trends in agro-meteorological variables--- such as radiation, temperature, and precipitation---focus on anomalies and their potential impacts on agricultural production. CropWatch also prioritizes monitoring and assessing cropping intensity and crop stress, analyzing their effects on agricultural production intensity and stress conditions at the continental main production zone scale. For major agricultural countries, provincial/state levels, and tertiary agro-ecological zones, CropWatch conducts detailed monitoring of crop conditions, crop yield and production, delivering a comprehensive diagnostic assessment of the production situation in these key regions and ecological areas..

1. CropWatch Indicators

The CropWatch indicators are used to assess agro-environmental conditions and monitor crop condition and its dynamics (Table B.1).

Table B. 1 CropWatch indicators

2. Remote Sensing Monitoring of Crop Planting Area

CropWatch comprehensively considers regional differences in agroecological system, cropping structure, farmland management, and ground observation data, and has established an operational crop area monitoring system based on agroecological zones as the basic unit. The system adopts microservices architecture, encapsulates multiple crop identification and classification techniques—such as the mapping index of soybean, maize and rapeseed, random forest classification, sample-free and small-sample crop classification, spatiotemporal transfer classification, and a two-step method for rice potential area identification and fine extraction—into APIs for modular and scalable services.

By integrating semantic segmentation and artificial intelligence models, the system enables efficient data cleaning and high precision crop type identification. Meanwhile, the combination of large-scale crowdsourced data and multiple classification algorithms supports crop mapping in major grain and oilseed producing and exporting countries. Furthermore, by integrating high resolution field parcel extraction results with cropland distribution data, non-cropland coefficients are quantitatively assessed and combined with crop mapping results to achieve high-precision dynamic monitoring of crop planting areas.

3. Methods for Crop Yield Monitoring and Forcasting

CropWatch employs multiple approaches for crop yield estimation, including the yield–remote sensing index model, the biomass–harvest index model, and various data-driven methods. By coupling crop spatial distribution, phenological information, and the biomass–harvest index model, CropWatch has developed an interactive and visualized crop yield prediction system, enabling dynamic monitoring and assessment of yield for target crops.

For more details, please visit: http://cloud.cropwatch.com.cn/web/method

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