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Crop growth means the growth of cereal crop seedlings, as well as the status and trend of their growth. It has been one of the most important aspects of agricultural remote sensing in the last twenty years. Evaluation of crop growing condition in large scope before harvesting could be helpful for field management, as well as provide important information for early crop production estimation. For a country with a population of 1.2 billion it is important to know not only the domestic crop growth, but also the crop condition in other big agricultural countries that have food trade with China. This paper introduced the design, methods used and implementation of a global crop growth monitoring system, which satisfies the need of the global crop monitoring in the world. The system uses two methods of monitoring, which are real-time crop growth monitoring and crop growing process monitoring. Real-time crop growth monitoring could get the crop growing status for certain period by comparing the remote sensed data (NDVI, for example) of the period with the data of the period in the history (last year, mostly). The differential result was classified into several categories to reflect the condition at difference level of crop growing. This analysis result allows users to quickly assess how much and where conditions have either deteriorated, remained unchanged or improved. The crop growing process monitoring is the contrast between year and year for crop growth profile, which reflects the crop growing continuance at time during crop growing season. Time series of NDVI during the crop season are used and crop growth profiles are formed by getting statistical average of time series NDVI image for plowland in certain regions such as a state. Eigenvalues such as growing rate, peak value, average and so on were gotten from the crop growing profile to estimate crop growing status which concerns the whole growing process. Based on the above monitoring methods, we developed a crop growth monitoring system based on remote sensing, which provides a monitoring and analyzing environment for real-time crop growth monitoring and crop growing process monitoring to all the users. The system was developed in C/S pattern and includes five main functional modules, which are real-time crop growth monitoring module, crop growing process monitoring module, results visualization module, business management module and system configuring module. The structure and the function are particularly described in the paper. In the system, both real-time crop growth monitoring and crop growing process monitoring are carried out at three scales, which are state (province) scale, country scale and continent scale. While in most of the countries in the world the monitoring was carried out at country scale, monitoring in large agricultural countries such as USA, Canada, India, etc., was carried out at both state (province) scale and country scale. This can provide more detailed crop growth information in these countries. Much more macro crop growth information was supplied by running the system at continent scale such as North America, Europe, Africa and South East Asia, etc. Taken 10-day (16-day for MODIS) composite NDVI products of NOAA/AVHRR, SPOT/VEGETATION and MODIS as data source, the system has been run successfully for more than a year, which provides decision-supporting information on crop condition to more than a dozen of ministries and commissions in China.

Crop acreage estimation is essential to forecast crop production using remote sensing. The different spatial resolution of remotely sensed data directly affects the accuracy of crop acreage estimation. It is necessary and valuable to study the effect of resolution on crop acreage estimation, from both qualitative and quantitative points of view. Therefore, this paper analysed the resolution effect on the accuracy of acreage estimation by using CBERS-02B imagery. Spatial statistics methods and manifold accuracy evaluation indices were used respectively to analyse the data with different spatial resolutions and crop proportion statistics. The study results indicate that decreased spatial resolution will lead to reduced regional accuracy in addition to increased standard deviation, RMSE and bias due to the augmentation of mixed pixels. A replacement of higher resolution data by lower resolution data will have an important impact on the derived crop proportions. The regional accuracy of crop statistics can remain higher than 88%, when the crop proportion is higher than 40%. In summary, the higher resolution of the imagery can lead to increased average regional accuracy. The results of this paper also provide academic and experimental reference to resolve the problem of data selection in crop acreage estimation by remote sensing.