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Crop identification is a critical component for grain production prediction. Identifying crop type using remote sensing techniques has been investigated for many decades. A number of different supervised methods have been developed to discriminate different crops. However, most of these methods were applied to areas with relatively large cultivated fields. In China, the cultivation policy leads to extreme complexity in the agricultural landscape, especially in summer and autumn seasons. The objective of this study was to investigate the capability of multi-temporal Landsat images for crop identification in a region with high farmland fragmentation. The study area is located in Taigu, Shanxi province, where the crop planting structure is very complicated. A total of 7 Landsat Enhanced Thematic Mapper Plus (ETM+) images were acquired from 14 October 2003 to 26 June 2004 for classification. Two most favorable classifiers, support vector machine (SVM) and maximum likelihood classifier (MLC) were selected for classification with training samples using different combinations of multi-temporal Landsat images. The overall classification accuracy and Kappa statistics estimated from the confusion matrix using validation samples were selected for evaluating all classification results. Accuracy assessment results indicated that multi-temporal ETM+ data achieved satisfactory classification accuracy (best overall accuracy 89.61%) in the study area. SVM classifier performed better than MLC when three or less Landsat images were used. The addition of the temporal dimension further increased the overall classification accuracy for both SVM and MLC, but the accuracy increased slightly for SVM classifier. The time of data acquisitions are of great importance for crop classification. Results in this paper indicated that multitemporal Landsat ETM+ data are capable for crop discrimination in regions with high farmland fragmentation. In the future, the use of China Environment Satellite HJ-1A/B data for this application should be investigated in the future for the higher temporal resolution and greater spatial coverage.

Remote sensing for the monitoring of agricultural crops has been widely used in the past. Synthetic aperture radar (SAR) system, for its characteristics of all-weather, all-day image obtain capacity, is an attractive source of information for agriculture crop classification applications, particularly in regions where cloud cover is a problem. The accuracy with which crops can be classified is dependent on a range of sensor properties, including the SAR operating configuration. This paper focuses on the effect of integrating C- and X-band SAR data on the improvement of classification accuracy. The study was carried out on Yucheng Ecological Experimental Station (China). Radarsat-2 and TerraSAR-X data were acquired, and during the satellite overpass, the ground investigation was implemented. Support vector machine classifier was used to classify the image based on the backscattering coefficients and texture features. The classification was conducted separately on Radarsat-2, TerraSAR-X and the integrating of the two. The performance of single band SAR was not acceptly good, but the integrating of the two had a great increase on classification accuracy (more than 10%). With different frequency we would get more information about the earth surface. Integrating of multiband of SAR data was a dependable way to improve classification accuracy.

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.

In this study, three Landsat ETM images obtained at different period of the growth of seed rape and winter wheat are used in a study of analyzing the potential of multi-date ETM in crop discrimination for an area near Kaifeng city in Henan province, China. We compared the spectrum development between winter wheat and seed rape before, during and after the flowering stage of seed rape. Then, we provide two different classification schemas to discriminate the two crops depend on different image combination. The first one using images between the jointing stage and flowering stage of seed rape, the other one using images between the jointing stage and leguminous stage. At last, we selected two ETM images, on March 8 2001 and April 1 in 2001, to build a decision tree to discriminate seed rape and winter wheat. And the result was compared with that from IKONOS image classification and proved that the discrimination accuracy was 87% high. And the results also told us that discrimination errors were happened in two kinds of regions. One is the regions that crop growing status were very poor, another is that the planting patches were very thin or small

The process of soil erosion is complex. Among these factors that influence the process of water and soil erosion, vegetation cover and slope steepness are selected. The vegetation cover data in the Upper Basin of Miyun Reservoir have been estimated from normalized difference vegetation index derived from Landsat-7 ETM+ images. Based on the pixel of DEM slope, steepness is computed. Vegetation cover and topographic factor can be combined as a crosstab model. A soil erosion risk map with six grades can be drawn. Using the method the probability and the extent of soil loss can be measured. This information is very useful to watershed manager. This case study describes and assesses soil erosion in the Upper Basin of Miyun Reservoir