Mapping Plastic-Mulched Farmland with C-Band Full Polarization SAR Remote Sensing Data

Remote Sensing

Zhongxin Chen¹  Fei Li²  Hongmei²  Hasituya² 

[1] Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture, P.R. China (AGRIRS)/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, No. 12, Zhongguancun Nan Dajie, Haidian District, Beijing 100081, China;Key Laboratory of Grassland Resources, Ministry of Education, P.R. China/Key Laboratory of Forage Cultivation, Processing and High Efficient Utilization, Ministry of Agriculture, P.R. China, College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, No. 29, Erdos Dongjie, Saihan District, Hohhot 010011, China;

关键词: plastic-mulched farmland;    mapping;    Radarsat-2 data;    backscattering intensity;    polarimetric decomposition;    machine learning algorithm;   

DOI  :  10.3390/rs9121264

来源: DOAJ

【 摘 要 】

Plastic mulching is an important technology in agricultural production both in China and the rest of the world. In spite of its benefit of increasing crop yields, the booming expansion of the plastic mulching area has been changing the landscape patterns and affecting the environment. Accurate and effective mapping of Plastic-Mulched Farmland (PMF) can provide useful information for leveraging its advantages and disadvantages. However, mapping the PMF with remote sensing is still challenging owing to its varying spectral characteristics with the crop growth and geographic spatial division. In this paper, we investigated the potential of Radarsat-2 data for mapping PMF. We obtained the backscattering intensity of different polarizations and multiple polarimetric decomposition descriptors. These remotely-sensed information was used as input features for Random Forest (RF) and Support Vector Machine (SVM) classifiers. The results indicated that the features from Radarsat-2 data have great potential for mapping PMF. The overall accuracies of PMF mapping with Radarsat-2 data were close to 75%. Although the classification accuracy with the back-scattering intensity information alone was relatively lower owing to the inherent speckle noise in SAR data, it has been improved significantly by introducing the polarimetric decomposition descriptors. The accuracy was nearly 75%. In addition, the features derived from the Entropy/Anisotropy/Alpha (H/A/Alpha) polarimetric decomposition, such as Alpha, entropy, and so on, made a greater contribution to PMF mapping than the Freeman decomposition, Krogager decomposition and the Yamaguchi4 decomposition. The performances of different classifiers were also compared. In this study, the RF classifier performed better than the SVM classifier. However, it is expected that the classification accuracy of PMF with SAR remote sensing data can be improved by combining SAR remote sensing data with optical remote sensing data.

【 授权许可】

Unknown