Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images

Abstract

Accurate and up-to-date information on the spatial distribution of paddy rice fields is necessary for the studies of trace gas emissions, water source management, and food security. The phenology-based paddy rice mapping algorithm, which identifies the unique flooding stage of paddy rice, has been widely used. However, identification and mapping of paddy rice in rice-wetland coexistent areas is still a challenging task. In this study, we found that the flooding/transplanting periods of paddy rice and natural wetlands were different. The natural wetlands flood earlier and have a shorter duration than paddy rice in the Panjin Plain, a temperate region in China. We used this asynchronous flooding stage to extract the paddy rice planting area from the rice-wetland coexistent area. MODIS Land Surface Temperature (LST) data was used to derive the temperature-defined plant growing season. Landsat 8 OLI imagery was used to detect the flooding signal and then paddy rice was extracted using the difference in flooding stages between paddy rice and natural wetlands. The resultant paddy rice map was evaluated with in-situ ground-truth data and Google Earth images. The estimated overall accuracy and Kappa coefficient were 95% and 0.90, respectively. The spatial pattern of OLI-derived paddy rice map agrees well with the paddy rice layer from the National Land Cover Dataset from 2010 (NLCD-2010). The differences between RiceLandsat and RiceNLCD are in the range of ±20% for most 1-km grid cell. The results of this study demonstrate the potential of the phenology-based paddy rice mapping algorithm, via integrating MODIS and Landsat 8 OLI images, to map paddy rice fields in complex landscapes of paddy rice and natural wetland in the temperate region.

Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2

“…Landsat and its relevant derivative indices (i.e., LSWI, NDVI, temperature) have spectral bands sensitive to rice paddy conditions (i.e., [16][17][18]); however, phenological differences between scenes and low temporal frequency in the historical archives have limited mapping of rice at moderate scales over large areas. For example, 55 scenes are required for a single time period wall-to-wall mosaic of Myanmar.…”

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

“…Others Gumma et al 2016;Zhang et al 2015;Zhou et al 2016) showed the ability of phenology-based algorithms in paddy rice mapping by using MODIS time-series and/or by integrating MODIS with other higher spatial resolution data such as Landsat 8 OLI images. This research further re-iterates the strength of MODIS time-series data in cropland and/or specific LULC mapping.…”

“…These studies were conducted using data from multiple sensors across many spatial, spectral, radiometric, and temporal resolutions for both irrigated and rainfed crops (Biggs et al 2006;Friedl et al 2010;Funk and Brown 2009;Gumma et al 2011;Loveland et al 2000;Ozdogan and Woodcock 2006;Pervez, Budde, and Rowland 2014;Pittman et al 2010;Teluguntla et al 2015a;Thenkabail et al 2009aThenkabail et al , 2009bWardlow, Egbert, and Kastens 2007;Xiao et al 2006;Yu et al 2013). These studies consider an ensemble of methods that include: (a) decision tree algorithms (De Fries et al 1998;Friedl and Brodley 1997;Ozdogan and Gutman 2008;Waldner, Canto, and Defourny 2015); (b) the random forest algorithm (Gislason, Benediktsson, and Sveinsson 2006;Tatsumi et al 2015); (c) Tassel cap brightness-greenness-wetness (Cohen and Goward 2004;Crist and Cicone 1984;Gutman et al 2008;Masek et al 2008); (d) space-time spiral curves and change vector analysis (Thenkabail, Schull, and Turral 2005); (e) phenological approaches Gumma et al 2011;Loveland et al 2000;Pan et al 2015;Teluguntla et al 2015a;Xiao et al 2006;Zhou et al 2016); (f) Hierarchical Image Segmentation (HSEG) software or HSeG (Tilton et al 2012); (g) support vector machines (Mountrakis, Im, and Ogole 2011;Shao and Lunetta 2012); (h) spectral matching techniques (Thenkabail et al 2007a); (i) pixel-and object-based methods usin...…”

Comparison of Carbon-Use Efficiency Among Different Land-Use Patterns of the Temperate Steppe in the Northern China Pastoral Farming Ecotone

“…Naturally grazed steppe used to be the dominant land-use type in this area but is now being rapidly and extensively replaced by other land uses, particularly steppe cultivation and grazing exclusion [6]. In response to global climate change, the cultivation boundary in the Northern Hemisphere is moving gradually north [7][8][9][10]. Moreover, a large portion of the typical temperate steppe in central and eastern Inner Mongolia is situated in the pastoral farming ecotone in northern China, which is under increasing cultivation pressure driven by growing economic and food demand [11].…”