Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data

Abstract

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting.

Comparison of Global Land Cover Datasets for Cropland Monitoring

“…Likewise, in America, the highest agreement occurs in Cuba (i.e., 7.81%), where rice constitutes 50% of agricultural production. Actually, rice production across these environments is characterized by an initial flooded phase and a typical phenology that can be readily identified from remote sensing data, resulting in good discrimination and classification [55]. Similar to previous studies [19], in Africa, the highest agreements are located in the transition zone between the Soudanien and Sahelian bands, running from west Senegal to east Ethiopia.…”

High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data

“…To fulfill the food demand of large population, Asian countries often adopt intensive agriculture to increase grain production on limited cropland extents with heavy capital and labor inputs. Multiple cropping, a common practice of intensive agriculture that grows multiple crops in the same cropland during a single growing season, makes full use of the light-heat resources as well as other natural conditions to increase the agricultural production capacity [4][5][6]. Simultaneously, multiple cropping results in a series of subsequent environment and social issues, such as agricultural land use, water use and nutrient applications, cropland managements, and the reciprocal feedback of climate changes [7][8][9][10].…”

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.…”