Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

Xiao, Xiangming; Boles, Stephen; Frolking, Steve; Li, Changsheng; Babu, Jagadeesh Y.; Salas, William; Moore, Berrien

doi:10.1016/j.rse.2005.10.004

Cited by 655 publications

(564 citation statements)

References 33 publications

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“…3C), or from remote sensing (Fig. 3D), was very strong especially if one considers that the three data sets concerned originate from three entirely different sources: (i) the X-ray door-to-door survey data on the poultry population census in late 2004; (ii) the official current Thai agriculture statistics on rice; (iii) a rice detection algorithm developed in an independent study seeking to map rice agriculture across Southeast Asia (Xiao et al, 2006). In particular, there was a strong visual match between the contours or demarcation of the high density free grazing duck areas, and areas where two or more rice crops were produced (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Three vegetation indices, normalized different vegetation index (NDVI), enhanced vegetation index (EVI) and land surface water index (LSWI), are sensitive to changes in leaf area index, leaf chlorophyll content, and leaf water content and soil moisture, respectively. A satellite-based mapping algorithm, based on temporal profile analysis of these three vegetation indices, was developed to identify and track those image pixels that experienced flooding and rice transplanting over time (Xiao et al, 2005(Xiao et al, , 2006. In this study 46 8-day composites of MODIS surface reflectance product (MOD09A1) in 2004 were used as input data for the mapping algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, substantial progress has been achieved in both satellite sensor capability and data analysis methods application to predict rice crop distributions. For example, it is nowadays possible to routinely map and monitor rice paddy agriculture (Xiao et al, 2005(Xiao et al, , 2006 and cropping intensity in Asia, using images from the moderate resolution imaging spectroradiometer (MODIS) sensor onboard the NASA Terra satellite. The satellite-based algorithms permit the production of maps and monitoring of cropping intensity, the crop calendar (planting and harvesting dates) and irrigation practices at moderate spatial resolution (250-500 m) and in near-real time fashion.…”

Section: Introductionmentioning

confidence: 99%

“…First, it aimed to characterise the association between duck numbers and the availability of harvested rice paddy fields, using duck census and contemporal rice statistics in Thailand. Second, the study aimed to quantify the geospatial association between free grazing duck census and rice production intensity as predicted by Xiao et al (2006) rice detection algorithm. This information could be applied to predict the areas at risk of HPAI in Thailand.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Avian influenza, domestic ducks and rice agriculture in Thailand

Gilbert

Xiao

Chaitaweesub

et al. 2007

Agriculture, Ecosystems & Environment

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118

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Highly pathogenic avian influenza (HPAI) caused by H5N1 viruses has become a global scale problem which first emerged in southern China and from there spread to other countries in Southeast and East Asia, where it was first confirmed in end 2003. In previous work, geospatial analyses demonstrated that free grazing ducks played critical role in the epidemiology of the disease in Thailand in the winter 2004/2005, both in terms of HPAI emergence and spread. This study explored the geographic association between free grazing duck census counts and current statistics on the spatial distribution of rice crops in Thailand, in particular the crop calendar of rice production. The analysis was carried out using both district level rice statistics and rice distribution data predicted with the aid of remote sensing, using a rice-detection algorithm. The results indicated a strong association between the number of free grazing ducks and the number of months during which second-crop rice harvest takes place, as well as with the rice crop intensity as predicted by remote sensing. These results confirmed that free grazing duck husbandry was strongly driven by agricultural land use and rice crop intensity, and that this later variable can be readily predicted using remote sensing. Analysis of rice cropping patterns may provide an indication of the location of populations of free grazing ducks in other countries with similar mixed duck and rice production systems and less detailed duck census data. Apart from free ranging ducks and rice cropping, the role of hydrology and seasonality of wetlands and water bodies in the HPAI risk analysis is also discussed in relation to the presumed dry season aggregation of wild waterfowl and aquatic poultry offering much scope for virus transmission.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Avian influenza, domestic ducks and rice agriculture in Thailand

Gilbert

Xiao

Chaitaweesub

et al. 2007

Agriculture, Ecosystems & Environment

Self Cite

118

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show abstract

“…Previous studies show that the integration of mid-and low-resolution images is the primary methods used for investigating large-scale crop planting areas [4][5][6][7]. A mid-resolution image has a high level of accuracy in crop recognition, but lacks availability and coverage [8][9].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Maize Planting Area through the Fusion of Multi-source Images

Pan

et al. 2012

Computer and Computing Technologies in Agriculture V

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Abstract. The limitations on spatial resolution and on the availability and measurement accuracy of remote sensing images are the primary problems in the estimation of the large-scale planting area for maize. The integration of mid-and low-resolution images is the one of primary methods used for the estimation of large-scale crop planting areas using remote sensing. The use of a single-temporal thematic mapper (TM) image results in a low accuracy of maize recognition, so a mid-scale time-series normalized difference vegetation index (NDVI) dataset, which was derived from the fusion of the moderate-resolution imaging spectroradiometer (MODIS) and TM images based on the wavelet transform, was established. The planting area was estimated using the minimum distance model and the accuracy was evaluated using in-situ samples. The results show that the estimation of the maize-sown area based on the time-series NDVI information of the integrated images reached high levels of gross and position accuracy (89% and 90%), indicating that this method could fully utilize the time-series information from the MODIS images and the spatial resolution of a TM image. The use of the difference in phenophases among fall crops enables the effective classification of the spatial distribution of these crops.

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Northward expansion of paddy rice in northeastern Asia during 2000–2014

Dong

Xiao

Zhang

et al. 2016

Geophysical Research Letters

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Paddy rice in monsoon Asia plays an important role in global food security and climate change. Here we documented annual dynamics of paddy rice areas in the northern frontier of Asia, including northeastern (NE) China, North Korea, South Korea, and Japan, from 2000 to 2014 through analysis of satellite images. The paddy rice area has increased by 120% (2.5 to 5.5 million ha) in NE China, in comparison to a decrease in South Korea and Japan, and the paddy rice centroid shifted northward from 41.16°N to 43.70°N (~310 km) in this period. Market, technology, policy, and climate together drove the rice expansion in NE China. The increased use of greenhouse nurseries, improved rice cultivars, agricultural subsidy policy, and a rising rice price generally promoted northward paddy rice expansion. The potential effects of large rice expansion on climate change and ecosystem services should be paid more attention to in the future.

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Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

Cited by 655 publications

References 33 publications

Avian influenza, domestic ducks and rice agriculture in Thailand

Avian influenza, domestic ducks and rice agriculture in Thailand

Estimation of Maize Planting Area through the Fusion of Multi-source Images

Northward expansion of paddy rice in northeastern Asia during 2000–2014

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