Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015

Zhang, Geli; Xiao, Xiangming; Biradar, Chandrashekhar; Dong, Jinwei; Qin, Yuanwei; Menarguez, Michael A.; Zhou, Yuting; Yao, Zhilei; Cui, Jin; Wang, Jie; Ding, M. D.; Moore, Berrien

doi:10.1016/j.scitotenv.2016.10.223

Cited by 142 publications

(112 citation statements)

References 64 publications

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“…Yet, in situ data is rarely available at both the spatial and temporal detail required for impactful agricultural monitoring and intervention. Therefore, remote sensing has been investigated as a potential solution to ongoing efforts to monitor and evaluate rice production [6][7][8][9][10][11][12][13][14][15]. This study extends those previous efforts toward this endeavor, and highlights improvements in spatiotemporal rice mapping in the heterogeneous landscape of Bangladesh during the dry season.…”

Section: Introductionmentioning

confidence: 54%

“…The spatial extents of these studies range from experimental plots to continental scales and employ unsupervised and supervised [19][20][21][22] rule-based [23], phenology-based [11,24], and time series algorithms [18,24,25]. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been one of the most widely used platforms for rice mapping and monitoring applications at larger regional scales because of the daily revisit, relatively small data size, and availability of spectral information that is particularly pertinent to agriculture [9,10,23,26,27]. MODIS time series images have also been combined with synthetic aperture radar (SAR) data for rice monitoring [28,29], exemplifying new initiatives and innovative techniques that are becoming available with the advent of free access to remotely sensed datasets and improved information on regional rice production systems.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

Shew

Ghosh

2019

Remote Sensing

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In many countries, in situ agricultural data is not available and cost-prohibitive to obtain. While remote sensing provides a unique opportunity to map agricultural areas and management characteristics, major efforts are needed to expand our understanding of cropping patterns and the potential for remotely monitoring crop production because this could support predictions of food shortages and improve resource allocation. In this study, we demonstrate a new method to map paddy rice using Google Earth Engine (GEE) and the Landsat archive in Bangladesh during the dry (boro) season. Using GEE and Landsat, dry-season rice areas were mapped at 30 m resolution for approximately 90,000 km2 annually between 2014 and 2018. The method first reconstructs spectral vegetation indices (VIs) for individual pixels using a harmonic time series (HTS) model to minimize the effect of any sensor inconsistencies and atmospheric noise, and then combines the time series indices with a rule-based algorithm to identify characteristics of rice phenology to classify rice pixels. To our knowledge, this is the first time an annual pixel-based time series model has been applied to Landsat at the national level in a multiyear analysis of rice. Findings suggest that the harmonic-time-series-based vegetation indices (HTS-VIs) model has the potential to map rice production across fragmented landscapes and heterogeneous production practices with comparable results to other estimates, but without local management or in situ information as inputs. The HTS-VIs model identified 4.285, 4.425, 4.645, 4.117, and 4.407 million rice-producing hectares for 2014, 2015, 2016, 2017, and 2018, respectively, which correlates well with national and district estimates from official sources at an average R-squared of 0.8. Moreover, accuracy assessment with independent validation locations resulted in an overall accuracy of 91% and a kappa coefficient of 0.83 for the boro/non-boro stable rice map from 2014 to 2018. We conclude with a discussion of potential improvements and future research pathways for this approach to spatiotemporal mapping of rice in heterogeneous landscapes.

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Section: Introductionmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

Shew

Ghosh

2019

Remote Sensing

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“…It is the largest freshwater lake in China used by 500,000 wild birds belonging to 75 species as part of their habitat, depending on the season. The lake is surrounded by croplands, which over time have gradually replaced natural wetlands with intensively cropped rice paddy fields [19]. Rice and duck farming are strongly associated in many Asian countries, and 26 million duck and geese and 21 million chickens are raised in the 10 counties surrounding Poyang lake.…”

Section: Main Textmentioning

confidence: 99%

“…A recent study showed that a vast area of intensive cropping developed in north-eastern China in the last 10 years. This may now have formed a new ideal interface for wild and domestic poultry [19] in the northwest of South Korea, possibly creating a new important zones for avian influenza reassortment and transmission in North-eastern Asia. In summary, the emergence of avian influenza HPAI H5N1, H5N6, H5N8 and LPAI H7N9 Fig.…”

Section: Main Textmentioning

confidence: 99%

Intensifying poultry production systems and the emergence of avian influenza in China: a ‘One Health/Ecohealth’ epitome

2017

Self Cite

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Several kinds of pressure can lead to the emergence of infectious diseases. In the case of zoonoses emerging from livestock, one of the most significant changes that has taken place since the mid twentieth century is what has been termed the “livestock revolution”, whereby the stock of food animals, their productivity and their trade has increased rapidly to feed rising and increasingly wealthy and urbanized populations. Further increases are projected in the future in low and middle-income countries. Using avian influenza as an example, we discuss how the emergence of avian influenza H5N1 and H7N9 in China was linked to rapid intensification of the poultry sector taking place in landscapes rich in wetland agriculture and wild waterfowls habitats, providing an extensive interface with the wild reservoir of avian influenza viruses. Trade networks and live-poultry markets further exacerbated the spread and persistence of avian influenza as well as human exposure. However, as the history of emergence of highly pathogenic avian influenza (HPAI) demonstrates in high-income countries such as the USA, Canada, Australia, the United Kingdom or the Netherlands, this is by no way specific to low and middle-income countries. Many HPAI emergence events took place in countries with generally good biosecurity standards, and the majority of these in regions hosting intensive poultry production systems. Emerging zoonoses are only one of a number of externalities of intensive livestock production systems, alongside antimicrobial consumption, disruption of nutrient cycles and greenhouse gases emissions, with direct or indirect impacts on human health. In parallel, livestock production is essential to nutrition and livelihoods in many low-income countries. Deindustrialization of the most intensive production systems in high-income countries and sustainable intensifications in low-income countries may converge to a situation where the nutritional and livelihood benefits of livestock production would be less overshadowed by its negative impacts on human an ecosystem health.

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“…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]. Information regarding the spatial distribution of multiple cropping is therefore essential for a wide range of applications and studies [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

et al. 2017

Remote Sensing

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Multiple cropping, a common practice of intensive agriculture that grows crops multiple times in the agricultural land in one growing season, is an effective way to fulfill the food demand given limited cropland areas. Deriving cropping cycles from satellite data provides the spatial distribution of cropping intensities that allows for monitoring of the multiple cropping activities over large areas. Although efforts have been made to map cropping cycles at 500 m or coarser resolution, producing cropping cycle maps at high resolution remain challenging because data from single satellite sensor do not provide sufficient spatiotemporal observations. In this paper, we generate dense time series of satellite data at 30 m resolution by fusion of Landsat and MODIS data, and derive the cropping cycles from the fused time series data. The method achieves overall accuracies of 92.5% and 89.2%, respectively, for two typical regions of multiple cropping in China using samples identified based on satellite time series data, and an overall accuracy of 81.2% for four subregions using all samples identified based on multi-temporal high resolution images. The mapped crop cycles show to be reasonable geographically and agree with the national census data. The fusion approach provides a feasible way to map cropping cycles at 30 m resolution and enables improved depiction of the spatial distribution of multiple cropping.

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Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015

Cited by 142 publications

References 64 publications

Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

Identifying Dry-Season Rice-Planting Patterns in Bangladesh Using the Landsat Archive

Intensifying poultry production systems and the emergence of avian influenza in China: a ‘One Health/Ecohealth’ epitome

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

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