Identification and mapping of winter wheat by integrating temporal change information and Kullback–Leibler divergence |
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| Institution: | 1. Dept. of Earth System Science and Center on Food Security and the Environment, Stanford University, CA, USA;2. Google, Mountain View, CA, USA;3. Center for Agribusiness Excellence, Tarleton State University, Stephenville, TX, USA;1. Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China;2. Centre for Biodiversity and Conservation Science, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, Queensland 4072, Australia;3. Remote Sensing Research Centre, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, Queensland 4072, Australia;4. School of Life Sciences, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia;5. Terrestrial Ecosystem Research Network, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia;6. Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Kaifeng 475004, China |
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| Abstract: | Crop acreage and its spatial distribution are a base for agriculture related works. Current research combining medium and low spatial resolution images focuses on data fusion and unmixing methods. The purpose of the former is to generate synthetic fine spatial resolution data instead of directly solving the problem. In the latter, high-resolution data is only used to provide endmembers and the result is usually an abundance map rather than the true spatial distribution data. To solve this problem, this paper designs a conceptual model which divides the study area into different types of pixels at a MODIS 250 m scale. Only three types of pixels contain winter wheat, i.e., pure winter wheat pixels (PA), the mixed pixels comprising winter wheat and other vegetation (MA) and the mixed pixels comprising winter wheat and other crops (MB). Different strategies are used in processing them. (1) Within the pure cultivated land pixels, the Kullback–Leibler (KL) divergence is employed to analyze the similarity between unknown pixels and the pure winter wheat samples on the temporal change characteristics of NDVI. Further PA is identified. (2) For MA, a proposed reverse unmixing method is firstly used to extract the temporal change information of cultivated land components, after which winter wheat is identified from the cultivated land components as previously described. (3) For MB which only appears on the border of PA, a mask is created by expanding the PA and temporal difference is utilized to identify winter wheat under the mask. Finally, these three results are integrated at a TM scale with the aid of 25 m resolution land use data. We applied the proposed solution and obtained a good result in the main agricultural area of the Yiluo River Basin. The identified winter wheat planting acreage is 161,050.00 hm2. The result is validated based on the five-hundred random validation points. Overall accuracy is 94.80% and Kappa coefficient is 0.85. This demonstrates that the temporal information reflecting crop growth is also an important indicator, and the KL divergence makes it more convenient in identifying winter wheat. This research provided a new perspective for the combination of low and medium spatial resolution remote sensing images. The proposed solution can also be effectively applied in other places and countries for the crop which has a clear temporal change characteristic that is different from others. |
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| Keywords: | KL divergence Temporal change information Remote sensing Winter wheat |
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