1.Crop Classification Method

The Global Agricultural Monitoring system comprehensively considers ecological, planting structure, field management, and regional differences in ground data to construct a business monitoring system based on agricultural ecological zones as fundamental units. It employs a microservices architecture to encapsulate various technologies such as soybean index, maize index, random forest, sample-free/small-sample crop classification, spatiotemporal transfer learning classification, and two-step approach for rice potential area identification and fine spatial extraction into APIs. By integrating semantic segmentation and artificial intelligence models, it rapidly cleans survey data and accurately identifies crop types. Combining massive multi-source data with various classification models and techniques, it achieves crop mapping for major grain and oil crop-producing countries as well as exporting countries. It also quantifies the non-cultivated land coefficient by integrating high-resolution parcel extraction results and cultivated land distribution data, and combines this with crop mapping results to achieve high-precision monitoring of planted areas.

2.Crop Yield Estimation Method

CropWatch develops an interactive crop yield prediction system by coupling crop spatial distribution, phenological information, and the "Biomass-Harvest Index" model, achieving crop yield monitoring.

3.CropWatch indicators

The CropWatch indicators are designed to assess the condition of crops and the environment in which they grow and develop; the indicators—RAIN (for rainfall), TEMP (temperature), and RADPAR (photosynthetically active radiation, PAR)—are not identical to the weather variables, but instead are value-added indicators computed only over crop growing areas (thus for example excluding deserts and rangelands) and spatially weighted according to the agricultural production potential, with marginal areas receiving less weight than productive ones. The indicators are expressed using the usual physical units (e.g., mm for rainfall) and were thoroughly tested for their coherence over space and time. CWSU are the CropWatch Spatial Units, including MRUs, MPZ, and countries (including first-level administrative districts in select large countries). For all indicators, high values indicate "good" or "positive."

For more details, please refer to the webpage: http://cloud.cropwatch.com.cn/web/method

References

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Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Yueran Hu et.al A Shape-attention Pivot-Net for Identifying Central Pivot Irrigation Systems from Satellite Images using a Cloud Computing Platform: An application in the contiguous US, GIScience & Remote Sensing, 2023

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