Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China

Cui, Jin; Xiao, Xiangming; Dong, Jinwei; Qin, Yuanwei; Wang, Zongming

doi:10.1007/s11707-015-0518-3

Cited by 45 publications

(29 citation statements)

References 42 publications

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“…Our maps are able to show detailed shifts in rice area on the local to county scale, which could not be deduced from the publicly-available agricultural statistics. The biggest observable change during the time period studied is the large increase in rice area in Heilongjiang, which has also been reported by previous studies [36,38,40,42]. From 2005 onward, paddy rice area in Heilongjiang increased at an unprecedented rate.…”

Section: Discussionsupporting

confidence: 82%

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

2016

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Abstract:Rice is an important food crop and a large producer of green-house relevant methane. Accurate and timely maps of paddy fields are most important in the context of food security and greenhouse gas emission modelling. During their life-cycle, rice plants undergo a phenological development that influences their interaction with waves in the visible light and infrared spectrum. Rice growth has a distinctive signature in time series of remotely-sensed data. We used time series of MODIS (Moderate Resolution Imaging Spectroradiometer) products MOD13Q1 and MYD13Q1 and a one-class support vector machine to detect these signatures and classify paddy rice areas in continental China. Based on these classifications, we present a novel product for continental

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Section: Discussionsupporting

confidence: 82%

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

2016

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“…With rapid socio-economic development in this area, more land is required for housing, infrastructure and industry, and rice cultivation is increasingly becoming prominent in northeast China [33]. Even though rice fields have been shifted to the peri-urban, apart from competition from residential and industrial land-uses, they are also seriously contested by such lucrative farming activities as aquaculture, horticulture, and other high income earning and shorter duration crops.…”

Section: Study Areamentioning

confidence: 99%

Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets

et al. 2017

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Sentinel-1A and Landsat 8 images have been combined in this study to map rice fields in urban Shanghai, southeast China, during the 2015 growing season. Rice grown in paddies in this area is characterized by wide inter-field variability in addition to being fragmented by other land-uses. Improving rice classification accuracy requires the use of multi-source and multi-temporal high resolution data for operational purposes. In this regard, we first exploited the temporal backscatter of rice fields and background land-cover types at the vertical transmitted and vertical received (VV) and vertical transmitted and horizontal received (VH) polarizations of Sentinel-1A. We observed that the temporal backscatter of rice increased sharply at the early stages of growth, as opposed to the relatively uniform temporal backscatter of the other land-cover classes. However, the increase in rice backscatter is more sustained at the VH polarization, and two-class separability measures further indicated the superiority of VH over VV in discriminating rice fields. We have therefore combined the temporal VH images of Sentinel-1A with the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI) derived from a single-date cloud-free Landsat 8 image. The integration of these optical indices with temporal backscatter eliminated all commission errors in the Rice class and increased overall accuracy by 5.3%, demonstrating the complimentary role of optical indices to microwave data in mapping rice fields in subtropical and urban landscapes such as Shanghai.

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“…Previous studies has used the thematic map of wetland to deal with this problem and suggested the need to develop a MODIS- or Landsat-based natural wetland mask (Jin et al, 2015; Zhang et al, 2015). A more detailed analysis of the dynamics of paddy rice and natural wetland might give us more clues to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Xiao

Qin

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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117

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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.

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Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China

Cited by 45 publications

References 42 publications

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

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

Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets

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

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