Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine

Abstract

Area and spatial distribution information of paddy rice are important for understanding of food security, water use, greenhouse gas emission, and disease transmission. Due to climatic warming and increasing food demand, paddy rice has been expanding rapidly in high latitude areas in the last decade, particularly in northeastern (NE) Asia. Current knowledge about paddy rice fields in these cold regions is limited. The phenology- and pixel-based paddy rice mapping (PPPM) algorithm, which identifies the flooding signals in the rice transplanting phase, has been effectively applied in tropical areas, but has not been tested at large scale of cold regions yet. Despite the effects from more snow/ice, paddy rice mapping in high latitude areas is assumed to be more encouraging due to less clouds, lower cropping intensity, and more observations from Landsat sidelaps. Moreover, the enhanced temporal and geographic coverage from Landsat 8 provides an opportunity to acquire phenology information and map paddy rice. This study evaluated the potential of Landsat 8 images on annual paddy rice mapping in NE Asia which was dominated by single cropping system, including Japan, North Korea, South Korea, and NE China. The cloud computing approach was used to process all the available Landsat 8 imagery in 2014 (143 path/rows, ~3290 scenes) with the Google Earth Engine (GEE) platform. The results indicated that the Landsat 8, GEE, and improved PPPM algorithm can effectively support the yearly mapping of paddy rice in NE Asia. The resultant paddy rice map has a high accuracy with the producer (user) accuracy of 73% (92%), based on the validation using very high resolution images and intensive field photos. Geographic characteristics of paddy rice distribution were analyzed from aspects of country, elevation, latitude, and climate. The resultant 30-m paddy rice map is expected to provide unprecedented details about the area, spatial distribution, and landscape pattern of paddy rice fields in NE Asia, which will contribute to food security assessment, water resource management, estimation of greenhouse gas emissions, and disease control.

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

“…Menarguez [43] tried to improve the performances of the NDWI-or mNDWI-based algorithms by involving vegetation indices (EVI and NDVI), that could help take care of the mixed pixels of water and vegetation. That could be particularly meaningful when used in flood monitoring, e.g., paddy rice transplant monitoring [42,58,59].…”

“…This separated and systematic technique improved the water body mapping accuracy. Another scheme was the application of the land surface water index (LSWI) for water body or flooding identification, based on the relationship between the LSWI and a vegetation greenness index like the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) [42]. Menarguez [43] put forward a new method by combining each of the three water indices (LSWI, mNDWI, and NDWI) with EVI and NDVI, and the results revealed that this integrated method was more sensitive to water bodies, especially the mixed water and vegetation pixels.…”

Accuracy Assessment and Inter-Comparison of Eight Medium Resolution Forest Products on the Loess Plateau, China

“…Secondly, in each strata, random points were generated with circular, radial buffers of 100 m to serve as regions of interest (ROIs). These buffers gained more validation pixels with 30 m resolution, which improved the accuracy assessment [39]. For example, if the size of the ROIs are too small, than the inherent location errors of the satellite images can affect the validation results and produce more uncertainty in landscape heterogeneity [40,41].…”

Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series

“…Before transplanting of the rice planting area at the Chinese-Russian border for five epochs between 1986 and 2010 and reported a twenty-fold increase in paddy rice area. This approach was also shown to work at a larger scale by classifying rice areas in northeast China, North and South Korea and Japan [43].…”