Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images.

Jie Wang,Cui Jin,Geli Zhang,Yuanwei Qin,Weili Kou,Xiangming Xiao,Jinwei Dong,Yuting Zhou,Yao Zhang

doi:10.1038/srep10088

Jie Wang, Cui Jin + Show 7 more

Open Access

https://doi.org/10.1038/srep10088

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Abstract

As farmland systems vary over space and time (season and year), accurate and updated maps of paddy rice are needed for studies of food security and environmental problems. We selected a wheat-rice double-cropped area from fragmented landscapes along the rural–urban complex (Jiangsu Province, China) and explored the potential utility of integrating time series optical images (Landsat-8, MODIS) and radar images (PALSAR) in mapping paddy rice planting areas. We first identified several main types of non-cropland land cover and then identified paddy rice fields by selecting pixels that were inundated only during paddy rice flooding periods. These key temporal windows were determined based on MODIS Land Surface Temperature and vegetation indices. The resultant paddy rice map was evaluated using regions of interest (ROIs) drawn from multiple high-resolution images, Google Earth, and in-situ cropland photos. The estimated overall accuracy and Kappa coefficient were 89.8% and 0.79, respectively. In comparison with the National Land Cover Data (China) from 2010, the resultant map better detected changes in the paddy rice fields and revealed more details about their distribution. These results demonstrate the efficacy of using images from multiple sources to generate paddy rice maps for two-crop rotation systems.

Highlights

The early 1990s and in the year 2000 were created at a spatial resolution of 5 arc minutes (~10 km)[13,14]
Different characteristics of vegetation indices are the basis for distinguishing flooded paddies from other land cover types
Paddy rice fields are in the midst of the flooding periods, covered by a mixture of water and plants

Summary

Introduction

The early 1990s and in the year 2000 were created at a spatial resolution of 5 arc minutes (~10 km)[13,14]. Reduced Landsat data availability caused by cloud cover or other problems may result in the failure of Landsat time series with 16-day intervals to distinguish the crops and trees This problem is obvious in regions with double or multiple rotation agricultural systems, where cropland tends to be covered by plants year round, resulting in unavoidable confusion with natural evergreen forest. The current study, which involves mapping paddy rice planting areas at a 30-m spatial resolution, has two aims: (1) to develop a pixel- and phenology-based algorithm by integrating time series optical images (Landsat-8, MODIS) and radar images (PALSAR) to map paddy rice planting areas in wheat-rice agricultural systems; and (2) to evaluate the potential utility of Landsat-8 OLI data in identifying fragmented paddy rice fields in complex agricultural landscapes. The case study area is located at the Yangzi-Huaihe Plain, China, which is characterized by a two-crop rotation (wheat and rice) agricultural system and intermixture of rural and urban landscapes (Figure S1)

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