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Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, the CropWatch system has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The approach adopts a hierarchical system covering four spatial levels of detail: global, regional, national (thirty-one key countries including China) and “sub-countries” (for the nine largest countries). The thirty-one countries encompass more that 80% of both production and exports of maize, rice, soybean and wheat. The methodology resorts to climatic and remote sensing indicators at different scales. The global patterns of crop environmental growing conditions are first analyzed with indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass. At the regional scale, the indicators pay more attention to crops and include Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Cropped Arable Land Fraction (CALF) as well as Cropping Intensity (CI). Together, they characterize crop situation, farming intensity and stress. CropWatch carries out detailed crop condition analyses at the national scale with a comprehensive array of variables and indicators. The Normalized Difference Vegetation Index (NDVI), cropped areas and crop conditions are integrated to derive food production estimates. For the nine largest countries, CropWatch zooms into the sub-national units to acquire detailed information on crop condition and production by including new indicators (e.g., Crop type proportion). Based on trend analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. The hierarchical approach adopted by CropWatch is the basis of the analyses of climatic and crop conditions assessments published in the quarterly “CropWatch bulletin” which provides accurate and timely information essential to food producers, traders and consumers.

Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics (based on crop condition profiles). Since this type of method will generate false information if there are changes in crop rotation, cropping area or crop phenology, information on cropped/uncropped arable land is integrated to improve the accuracy of crop condition monitoring. The study proposes a new method to retrieve adjusted NDVI for cropped arable land during the growing season of winter crops by integrating 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data at 250-m resolution with a cropped and uncropped arable land map derived from the multi-temporal China Environmental Satellite (Huan Jing Satellite) charge-coupled device (HJ-1 CCD) images at 30-m resolution. Using the land map’s data on cropped and uncropped arable land, a pixel-based uncropped arable land ratio (UALR) at 250-m resolution was generated. Next, the UALR-adjusted NDVI was produced by assuming that the MODIS reflectance value for each pixel is a linear mixed signal composed of the proportional reflectance of cropped and uncropped arable land. When UALR-adjusted NDVI data are used for crop condition assessment, results are expected to be more accurate, because: (i) pixels with only uncropped arable land are not included in the assessment; and (ii) the adjusted NDVI corrects for interannual variation in cropping area. On the provincial level, crop growing profiles based on the two kinds of NDVI data illustrate the difference between the regular and the adjusted NDVI, with the difference depending on the total area of uncropped arable land in the region. The results suggested that the proposed method can be used to improve the assessment of early crop condition, but additional evaluation in other major crop producing regions is needed to better assess the method’s application in other regions and agricultural systems.