Abstract
PROBA-V is a new global vegetation monitoring satellite launched in the second quarter of 2013 that provides data with a 100 m to 1 km spatial resolution and a daily to 10-day temporal resolution in the visible and near infrared (VNIR) bands. A major mission of the PROBA-V satellite is global agriculture monitoring, in which the accuracy of crop mapping plays a key role. In countries such as China, crop fields are typically small, in assorted shapes and with various management approaches, which deem traditional methods of crop identification ineffective, and accuracy is highly dependent on image resolution and acquisition time. The five-day temporal and 100 m spatial resolution PROBA-V data make it possible to automatically identify crops using time series phenological information. This paper takes advantage of the improved spatial and temporal resolution of the PROBA-V data, to map crops at the Yucheng site in Shandong Province and the Hongxing farm in Heilongjiang province of China. First, the Swets filter algorithm was employed to eliminate noisy pixels and fill in data gaps on time series data during the growing season. Then, the crops are classified based on the Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering, the maximum likelihood method (MLC) and similarity analysis. The mapping results were validated using field-collected crop type polygons and high resolution crop maps based on GaoFen-1 satellite (GF-1) data in 16 m resolution. Our study showed that, for the Yucheng site, the cropping system is simple, mainly dominated by winter wheat–maize rotation. The overall accuracy of crop identification was 73.39% which was slightly better than the result derived from MODIS data. For the Hongxing farm, the cropping system is more complex (i.e., more than three types of crops were planted). The overall accuracy of the crop mapping by PROBA-V was 73.29% which was significantly higher than the MODIS product (46.81%). This study demonstrates that time series PROBA-V data can serve as a useful source for reliable crop identification and area estimation. The high revisiting frequency and global coverage of the PROBA-V data show good potential for future global crop mapping and agricultural monitoring.
Highlights
The demand for accurate and reliable satellite-derived estimations and predictions of crop features and output keeps increasing [1,2,3]
This study demonstrates that time series PROBA-V data can serve as a useful source for reliable crop identification and area estimation
To obtain the best classification result, five cluster types were selected by the Iterative Self-Organizing Data Analysis Technique (ISODATA)/MLC clustering of the normalized difference vegetation index (NDVI)
Summary
The demand for accurate and reliable satellite-derived estimations and predictions of crop features and output keeps increasing [1,2,3]. During the last several decades, crop monitoring models and applications using satellite images have been developed [4,5] and improved in five main areas: biomass and yields estimation, vegetation and water stress monitoring, crop acreage estimation, crop type proportion mapping and crop phenological development [6,7]. The accurate cropland mapping and identification of crop types can provide basically essential information for all crop monitoring. Crop phenology monitoring can provide the timing and duration of the cropping cycle, which is essential for yield and biomass estimation [10,11]. Due to the similarity in the spectra of different crops and the diversity in growing stages of one crop type, it is difficult to identify types by improving spatial resolution only. The existing crop classification methods based on high spatial resolution image often requires image acquisition from a certain stage during the growing season [18], which makes it difficult to automate crop identification in a complex cropping system
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