Extracting Planting Area And Growth Information Of Paddy Rice Using Multi-temporal MODIS Data In China

Paddy rice is one of the major staple crops in China. It is essential to get the spatial distribution, planting area and yield information of paddy rice at large scale for guiding rice production, regulating water use, and monitoring environmental changes. However, agricultural production has the characteristics of large coverage, frequent changes seasonally and regionally, and low benefit in per unit area, therefore, it is unfeasible to get the annual information through ground in situ measurement neither in the aspect of technical issues nor in the aspect of economic sustainability. Remote sensing technology is a feasible and efficient way to solve these problems. Compares with the traditional methods, remote sensing has its own advantages for acquiring planting information of crops. For remote sensing has the characteristics of large and frequent coverage, as well as its low costs, then it can get not only the area information, but also the accurate spatial position and the whole process of crop growth information associates with GIS and GPS. In the study, planting and growth information of paddy rice was extracted in all of China using EOS-MODIS data with large coverage and high spatial resolution. The goals of the study were to solve the most important technical problems in rice yield estimation using remote sensing technology, and provide theoretical and experimental bases for large-region rice yield estimation. The main contents of the study including regionalization of paddy rice information acquirement through remote sensing technology in China, detecting major growth stages of paddy rice, extracting the spatial distribution and area information, and getting growth information of paddy rice. The four above mentioned parts are the most important problems for rice yield estimation at large scale using remote sensing technology, so they were studied in detail in the study. Main content of each part is introduced as follows:Firstly, the dissertation gave a brief introduction of the topic, made a summarization of the precious studies, and described the methods in the study. The background, significance, and goals of the study were introduced, and a summary of the precious studies and the issues that need to be solved was made. It is helpful for the realization of the objectives and the potential innovation in the study. Then, detailed methods and the flowcharts were given for each part in the study; an introduction of the data used and an overall design for each step in the study were given as well.Subsequently, detailed introductions of the contents, methods and conclusions for each part in the study were given. In the study of regionalization of paddy rice information acquirement through remote sensing technology, major impacting factors, including rice farming systems, topography, planting structure and atmospheric noises, were analyzed and appropriate indices were chosen. Regionalization was executed by qualitative and quantitative analyses. Rice planting area in the whole country was divided into 4 regions by the difference of rice planting rotation, and subsequently divided 19 sub-regions by the differences of topography, land surface feature structure and atmospheric noise. It might provide useful information to the selection of remote sensing images with appropriate spatial resolution and date, as well as to the accuracy evaluation of the classification.In the study of major growth stages detection of paddy rice, phenological information of paddy rice in the whole country of China was extracted using multi-temporal MODIS data in 2005 as a case study. Firstly, time-series of MODIS-EVI (Enhanced Vegetation Index) was smoothed by Fourier and Wavelet low pass filtering, then the stages of transplanting, beginning of tillering, heading, and maturation were identified by their respective characteristics. The MODIS-derived results were compared with the statistics of meteorological observatories, most of the errors of the MODIS-derived results were within±16 days, and F test indicated that all of the results had significant consistency at the level of 0.05. The detecting methods could be used to extract rice growth stages in other years, and they might be able to extract the growth stage information of other crops according to their characteristics potentially.In the study of spatial distribution and area information extraction, spatial distribution and area information were extracted in all of China using MODIS data with large coverage, high temporal resolution, and low costs. The basis of rice classification in the study was relied on identifying the spectral characteristics of water in the "flooding and transplanting" period. Vegetation and soil moisture sensitive bands were selected by comparing the first 7 bands of MODIS that are sensitive to land surface, then they were used to calculate vegetation and water sensitive indices to amplify the differences between the regions of interest and the background. Spatial distribution and area information of single, early, and late rice in 2000-2007 were extracted by identifying the unique characteristic of rice fields in the "flooding and transplanting" period with the assistance of the growth period information. Areal accuracy of the MODIS-derived results was validated comparing with the agricultural statistics, and four typical test regions were selected to test the spatial matching by overlaying the results derived from MODIS data and the data with medium spatial resolution. Tests showed that the algorithms for rice identification in the study were effective; and the accuracy of the MODIS-derived results was relied on the purity of rice in the mixed pixels, the higher the purity is, the higher the accuracy of the results obtained; cloud contamination had big impacts on the results, therefore, optical sensors were seriously restricted in the regions with frequent clouds.In the study of growth status monitoring, methods of quantitative analyses were studied to break through the traditional methods, for the methods in the past only got the qualitative results of better, even, or worse than the former years or than other regions in the same period. Models were built to inverse the biophysical parameters of rice, including LAI and FPAR, by vegetation indices. Tests showed that EVI was superior to NDVI for getting the biophysical parameters. The growing seasons were identified at pixel level according to the results of the beginning date and end date for single, early and late rice, then LAI and FPAR were inversed by EVI for each period, and NPP was calculated by LUE (Light Use Efficiency) model, eventually, the results of biomass in the whole growing season for single, early, and late rice were calculated. The results could show the growth status of paddy rice, and might provide references for the analysis of the yield in per unit area.Eventually, a summary of the main conclusions and innovations in the study were made, and at the same time gave an expectation of the issues that need to be solved in the future.