Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets

Lamin Mansaray,Dongdong Zhang,Weijiao Huang,Jun Li,Jingfeng Huang

doi:10.3390/rs9030257

Abstract

Sentinel-1A and Landsat 8 images have been combined in this study to map rice fields in urban Shanghai, southeast China, during the 2015 growing season. Rice grown in paddies in this area is characterized by wide inter-field variability in addition to being fragmented by other landuses. Improving rice classification accuracy requires the use of multi-source and multi-temporal high resolution data for operational purposes. In this regard, we first exploited the temporal backscatter of rice fields and background land-cover types at the vertical transmitted and vertical received (VV) and vertical transmitted and horizontal received (VH) polarizations of Sentinel-1A. We observed that the temporal backscatter of rice increased sharply at the early stages of growth, as opposed to the relatively uniform temporal backscatter of the other land-cover classes. However, the increase in rice backscatter is more sustained at the VH polarization, and two-class separability measures further indicated the superiority of VH over VV in discriminating rice fields. We have therefore combined the temporal VH images of Sentinel-1A with the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI) derived from a single-date cloud-free Landsat 8 image. The integration of these optical indices with temporal backscatter eliminated all commission errors in the Rice class and increased overall accuracy by 5.3%, demonstrating the complimentary role of optical indices to microwave data in mapping rice fields in subtropical and urban landscapes such as Shanghai.

Highlights

Urban and peri-urban agriculture (UPA) has gained much recognition as a major source of livelihoods for inhabitants within cities and those along the rural–urban fringe, especially in developing countries [1,2,3]
Based on knowledge acquired during the 2016 field campaigns, we have proposed a simplified land-cover classification scheme for the area under study with five broad classes: Rice refers to paddy rice fields; Water includes rivers, lakes, streams and ponds; Built includes residential and industrial estates; Tress include forests and plants with considerable height and density; and Others include transitional areas, paved roads, bare land surfaces, grassland, other low biomass crops, and in the final map could include any or a combination of the other classes due to misclassification
In determining the optimal polarization based on the temporal increase in paddy rice backscatter, In determining optimalpolygons polarization on the temporalatincrease inand paddy backscatter, backscatter profiles ofthe training werebased critically examined both vertical received (VV)

Summary

Introduction

Urban and peri-urban agriculture (UPA) has gained much recognition as a major source of livelihoods for inhabitants within cities and those along the rural–urban fringe, especially in developing countries [1,2,3]. The increasing trends of rural–urban migration have triggered a population explosion in cities. Migrated rural surplus labor is mostly unskilled. Migrant populations mainly engage in primary economic activities, prominent among which is farming [4]. The concept of UPA is fraught with definitional challenges as it involves a diverse range of agricultural activities involving crops, livestock, and aquaculture at scales ranging from roof-top gardens to larger cultivated open spaces [5]. The boundary between the urban and Remote Sens. The boundary between the urban and Remote Sens. 2017, 9, 257; doi:10.3390/rs9030257 www.mdpi.com/journal/remotesensing

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Conclusion