Mapping paddy rice areas based on vegetation phenology and surface moisture conditions

Qiu, Bingwen; Li, Weijiao; Tang, Zhenghong; Chen, Chongcheng; Qi, Wen

doi:10.1016/j.ecolind.2015.03.039

Cited by 86 publications

(57 citation statements)

References 36 publications

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“…3). The different VIs also have various sensitivities, for example, Motohka et al (2009) found that EVI was superior to NDVI in terms of agreement with the in situ phenology data; Qiu et al (2015) found the variation of LSWI was relatively smaller in rice than in other crops during the period from tillering to heading dates. The applications of temporal VIs in paddy rice mapping are described in Sections 4.2-4.4.…”

Section: Biophysical Characteristics and Growth Phases Of Paddy Ricementioning

confidence: 90%

“…Multi-temporal profile analysis of paddy rice features is widely used in existing paddy rice mapping efforts and generally utilizes temporal analysis of VIs (Qiu et al, 2015;Xiao et al, 2006Xiao et al, , 2005. For example, the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) show an increase and decrease variation respectively with a peak in the heading phase .…”

Section: Biophysical Characteristics and Growth Phases Of Paddy Ricementioning

confidence: 99%

See 1 more Smart Citation

Evolution of regional to global paddy rice mapping methods: A review

Dong

Xiao

2016

ISPRS Journal of Photogrammetry and Remote Sensing

207

127

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a b s t r a c tPaddy rice agriculture plays an important role in various environmental issues including food security, water use, climate change, and disease transmission. However, regional and global paddy rice maps are surprisingly scarce and sporadic despite numerous efforts in paddy rice mapping algorithms and applications. With the increasing need for regional to global paddy rice maps, this paper reviewed the existing paddy rice mapping methods from the literatures ranging from the 1980s to 2015. In particular, we illustrated the evolution of these paddy rice mapping efforts, looking specifically at the future trajectory of paddy rice mapping methodologies. The biophysical features and growth phases of paddy rice were analyzed first, and feature selections for paddy rice mapping were analyzed from spectral, polarimetric, temporal, spatial, and textural aspects. We sorted out paddy rice mapping algorithms into four categories: (1) Reflectance data and image statistic-based approaches, (2) vegetation index (VI) data and enhanced image statistic-based approaches, (3) VI or RADAR backscatter-based temporal analysis approaches, and (4) phenology-based approaches through remote sensing recognition of key growth phases. The phenology-based approaches using unique features of paddy rice (e.g., transplanting) for mapping have been increasingly used in paddy rice mapping. Current applications of these phenologybased approaches generally use coarse resolution MODIS data, which involves mixed pixel issues in Asia where smallholders comprise the majority of paddy rice agriculture. The free release of Landsat archive data and the launch of Landsat 8 and Sentinel-2 are providing unprecedented opportunities to map paddy rice in fragmented landscapes with higher spatial resolution. Based on the literature review, we discussed a series of issues for large scale operational paddy rice mapping. Ó

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Section: Biophysical Characteristics and Growth Phases Of Paddy Ricementioning

confidence: 90%

Section: Biophysical Characteristics and Growth Phases Of Paddy Ricementioning

confidence: 99%

Evolution of regional to global paddy rice mapping methods: A review

Dong

Xiao

2016

ISPRS Journal of Photogrammetry and Remote Sensing

207

127

View full text Add to dashboard Cite

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“…Qiu et al proposed a method for mapping rice planting areas by considering the vegetation phenology and surface water variations. The ratios of the changes in amplitude of the LSWI to the two-band Enhanced Vegetation Index 2 (EVI2) during the period from the tillering to the heading stage were used as one indicator to discriminate rice from non-rice fields [26]. Mosleh and Hassan developed a method for mapping "Boro" rice in Bangladesh using the MODIS-derived 16-day composite NDVI at a spatial resolution of 250 m [27].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images

Shi

Huang

2015

Remote Sensing

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Abstract:A large-area map of the spatial distribution of rice is important for grain yield estimations, water management and an understanding of the biogeochemical cycling of carbon and nitrogen. In this paper, we developed the Normalized Weighted Difference Water Index (NWDWI) for identifying the unique characteristics of rice during the flooding and transplanting period. With the aid of the ASTER Global Digital Elevation Model and the phenological data observed at agrometeorological stations, the spatial distributions of single cropping rice and double cropping early and late rice in the Yangtze River Delta region were generated using the NWDWI and time-series Enhanced Vegetation Index data derived from MODIS/Terra data during the 2000-2010 period. The accuracy of the MODIS-derived rice planting area was validated against agricultural census data at the county level. The spatial accuracy was also tested based on a land use map and Landsat ETM+ data. The decision coefficients for county-level early and late rice were 0.560 and 0.619, respectively. The MODIS-derived area of late rice exhibited higher consistency with the census data during the 2000-2010 period. The algorithm could detect and monitor rice fields with different cropping patterns at the same site and is useful for generating spatial datasets of rice on a regional scale.

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“…In recent studies, phenology has been used for crop classification [11,22,33,34]. However, the capability of phenology has not been investigated yet for the OBIA-based rice classification.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the low temporal resolution and high price of SAR data limit its use at a large scale. A number of techniques have been applied to discriminate rice area, mainly the thresholding method [17][18][19], the supervised classification method [20,21] and phenology-based mapping [11,22]. However, these methods have limitations.…”

Section: Introductionmentioning

confidence: 99%

An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India

Singha

Zhang

2016

Remote Sensing

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Abstract:Rice is the staple food for half of the world's population. Therefore, accurate information of rice area is vital for food security. This study investigates the effect of phenology for rice mapping using an object-based image analysis (OBIA) approach. Crop phenology is combined with high spatial resolution multispectral data to accurately classify the rice. Phenology was used to capture the seasonal dynamics of the crops, while multispectral data provided the spatial variation patterns. Phenology was extracted from MODIS NDVI time series, and the distribution of rice was mapped from China's Environmental Satellite (HJ-1A/B) data. Classification results were evaluated by a confusion matrix using 100 sample points. The overall accuracy of the resulting map of rice area generated by both spectral and phenology is 93%. The results indicate that the use of phenology improved the overall classification accuracy from 2%-4%. The comparison between the estimated rice areas and the State's statistics shows underestimated values with a percentage difference of´34.53%. The results highlight the potential of the combined use of crop phenology and multispectral satellite data for accurate rice classification in a large area.

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Mapping paddy rice areas based on vegetation phenology and surface moisture conditions

Cited by 86 publications

References 36 publications

Evolution of regional to global paddy rice mapping methods: A review

Evolution of regional to global paddy rice mapping methods: A review

Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images

An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India

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