With the availability of high frequent satellite data, crop phenology could be accurately mapped using time series spatial data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop seasonality parameters using higher spatial resolution images (e.g., Landsat TM) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering for time-series data, and interpolation for daily NDVI images then the NDVI time-series could present a complete and smooth phenological cycle. To demonstrate its application, TIMESAT program was employed to extract the seasonality parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop seasonality parameters derived from HJ-1 A/B NDVI time-series were considerably accurate compared with local agro-metrological observation. Further study on technical issues regarding to time-series processing, and potential applications were discussed.
Agricultural land use and cropping patterns are closely related to food production, soil degradation, water resource management, greenhouse gas emission, and regional climate alterations. Methods for reliable and cost-efficient mapping of cropping pattern, as well as their changes over space and time, are therefore urgently needed. To cope with this need, we developed a phenology-based method to map cropping patterns based on time-series of vegetation index data. The proposed method builds on the well-known ‘threshold model’ to retrieve phenological metrics. Values of four phenological parameters are used to identify crop seasons. Using a set of rules, the crop season information is translated into cropping pattern. To illustrate the method, cropping patterns were determined for three consecutive years (2008–2010) in the Henan province of China, where reliable validation data was available. Cropping patterns were derived using eight-day composite MODIS Enhanced Vegetation Index (EVI) data. Results show that the proposed method can achieve a satisfactory overall accuracy (~84%) in extracting cropping patterns. Interestingly, the accuracy obtained with our method based on MODIS EVI data was comparable with that from Landsat-5 TM image classification. We conclude that the proposed method for cropland and cropping pattern identification based on MODIS data offers a simple, yet reliable way to derive important land use information over large areas.
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