Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China.

Haifeng Tian,Mingquan Wu,Li Wang,Zheng Niu

doi:10.3390/s18010185

Abstract

Areas and spatial distribution information of paddy rice are important for managing food security, water use, and climate change. However, there are many difficulties in mapping paddy rice, especially mapping multi-season paddy rice in rainy regions, including differences in phenology, the influence of weather, and farmland fragmentation. To resolve these problems, a novel multi-season paddy rice mapping approach based on Sentinel-1A and Landsat-8 data is proposed. First, Sentinel-1A data were enhanced based on the fact that the backscattering coefficient of paddy rice varies according to its growth stage. Second, cropland information was enhanced based on the fact that the NDVI of cropland in winter is lower than that in the growing season. Then, paddy rice and cropland areas were extracted using a K-Means unsupervised classifier with enhanced images. Third, to further improve the paddy rice classification accuracy, cropland information was utilized to optimize distribution of paddy rice by the fact that paddy rice must be planted in cropland. Classification accuracy was validated based on ground-data from 25 field survey quadrats measuring 600 m × 600 m. The results show that: multi-season paddy rice planting areas effectively was extracted by the method and adjusted early rice area of 1630.84 km2, adjusted middle rice area of 556.21 km2, and adjusted late rice area of 3138.37 km2. The overall accuracy was 98.10%, with a kappa coefficient of 0.94.

Highlights

Rice is a staple food for more than three billion people worldwide [1,2]
The paddy rice classification errors are mainly distributing in the boundaries of paddy rice plots and the reason is that there are kappa coefficient of 0.94
This study demonstrates the potential of using Sentinel-1A and Landsat-8 data to accurately map early, middle and late rice cultivation extents

Summary

Introduction

Paddy rice planting areas account for more than 12% of global cropland areas [3] and 40% of the crop yield in China [4]. And accurate estimation of the area and distribution of paddy rice crops is useful information for governments, planners, and decision makers who formulate policies in terms of food security and ecological sustainability [7,8]. Remote sensing-based techniques are a proven and effective method of estimating the area of paddy rice crops, and may be superior to traditional ground-based surveys [3,7,9]. Many efforts have been made to map paddy rice planting areas by using various classification algorithms and data sources, including optical- and microwave-based remotely sensed data. Knowledge- [14] and phenology-based approaches [15] are typical methods used

Results

Discussion

Conclusion