Exploring the Potential of Mapping Cropping Patterns on Smallholder Scale Croplands Using Sentinel-1 SAR Data

作     者：Juliana USEYA CHEN Shengbo 

作者机构：College of GeoExploration Science and Information Engineering Jilin University 

出 版 物：《Chinese Geographical Science》 (中国地理科学（英文版）)

年 卷 期：2019年第29卷第4期

页      面：626-639页

核心收录：

学科分类：0303[法学-社会学] 07[理学] 0708[理学-地球物理学] 0705[理学-地理学] 0813[工学-建筑学] 0704[理学-天文学] 0833[工学-城乡规划学] 

基　　金：Under the auspices of Fundamental Research Funds for the Central Universities,China(No.2017TD-26) the Plan for Changbai Mountain Scholars of Jilin Province,China(No.JJLZ54) 

主　　题：cropping patterns polarized backscatter time series Sentinel-1 SAR Zimbabwe 

摘      要：It is of paramount importance to have sustainable agriculture since agriculture is the backbone of many nations’ economic development. Majority of agricultural professionals rarely capture the cropping patterns necessary to promote Good Agricultural Practises.Objective of this research is to explore the potential of mapping cropping patterns occurring on different field parcels on small-scale farmlands in Zimbabwe. The first study location under investigation are the International Maize and Wheat Improvement Center(CIMMYT) research station and a few neighboring fields, the second is Middle Sabi Estate. Fourier time series modeling was implemented to determine the trends befalling on the two study sites. Results reveal that Sentinel-1 synthetic aperture radar(SAR) time series allow detection of subtle changes that occur to the crops and fields respectively, hence can be utilized to detect cropping patterns on small-scale farmlands. Discrimination of the main crops(maize and soybean) grown at CIMMYT was possible, and crop rotation was synthesized where sowing starts in November. A single cropping of early and late crops was observed, there were no winter crops planted during the investigation period. At Middle Sabi Estate, single cropping on perennial sugarcane fields and triple cropping of fields growing leafy vegetables, tomatoes and onions were observed. Classification of stacked images was used to derive the crop rotation maps representing what is practised at the farming lands. Random forest classification of the multi-temporal image stacks achieved overall accuracies of 99% and 95% on the respective study sites. In conclusion, Sentinel-1 time series can be implemented effectively to map the cropping patterns and crop rotations occurring on small-scale farming land. We recommend the use of Sentinel-1 SAR multi-temporal data to spatially explicitly map cropping patterns of single-, double-and triple-cropping systems on both small-scale and large-scale farming areas to ensure food security.