Role of middle infrared bands of Landsat thematic mapper in determining the classification accuracy of rice

Panigrahy, Sushma; Parihar, J. S.

doi:10.1080/01431169208904092

Cited by 32 publications

(17 citation statements)

References 4 publications

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“…Then Tennakoon et al (1992) found that the spectral characteristics of paddy rice are subject to growth stages and highly influenced by soil moisture; furthermore, they found that, when using MLC approach, Landsat TM band combination 5/4/3 was an appropriate band combination for the identification and area estimation of rice at the maturity stage and bands 5/3/1 provided better information at the young stage of rice. However, Panigrahy and Parihar (1992) found the Landsat TM band 2/3/4/5 and 2/3/4/7 performed better than the combination of TM band 1/2/3/4 by using the same approach (MLC). These explorations of reflectance data selection and its sensitivity to growth phases were still constrained how- ever, by the temporally limited data.…”

Section: Category One: Reflectance Data and Image Statistic-based Appmentioning

confidence: 84%

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

Dong

Xiao

2016

ISPRS Journal of Photogrammetry and Remote Sensing

219

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: Category One: Reflectance Data and Image Statistic-based Appmentioning

confidence: 84%

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

Dong

Xiao

2016

ISPRS Journal of Photogrammetry and Remote Sensing

219

127

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“…Medium-resolution optical images such as SPOT [12,13], Landsat TM [14][15][16][17][18][19] and ETM [20] have been used successfully for paddy field delineation. Flood damage assessment in rice areas and detection of changes in rice area extent, composition and field conditions due to crop rotation, natural vegetation transformation and natural disasters (i.e., floods or storm) are other applications of medium-resolution satellite optical images [21].…”

Section: Introductionmentioning

confidence: 99%

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

et al. 2014

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Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal optical and multitemporal high-resolution SAR imagery. We demonstrate the use of MODIS data for rice-based system characterization and X-band SAR data from TerraSAR-X and CosmoSkyMed for the identification and detailed mapping of rice areas and flooding/transplanting dates. MODIS was classified using ISODATA to generate cropping calendar, cropping intensity, cropping pattern and rice ecosystem information. Season and location specific thresholds from field observations were used to generate detailed maps of rice areas and flooding/transplanting dates from the SAR data. Error matrices were used for the accuracy assessment of the MODIS-derived rice characteristics map and the SAR-derived detailed rice area map, while Root Mean Square Error (RMSE) and linear correlation were used to assess the TSX-derived flooding/transplanting dates. Results showed that multitemporal high spatial resolution SAR data is effective for mapping rice areas and flooding/transplanting dates with an overall accuracy of 90% and a kappa of 0.72 and that hypertemporal moderate-resolution optical imagery is effective for the basic characterization of rice areas with an overall accuracy that ranged from 62% to 87% and a kappa of 0.52 to 0.72. This study has also provided the first assessment of the temporal variation in the backscatter of rice from CSK and TSX using large incidence angles covering all rice crop stages from pre-season until harvest. This complementarity in optical and SAR data can be further exploited in the near future with the increased availability of space-borne optical and SAR sensors. This new information can help improve the identification of rice areas.

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“…The SWIR band is known to provide information on water content of plants and has been used to assess water stress of plants (Tucker, 1980;Alrichs and Bauer, 1983;Hunt et al, 1987;Baret et al, 1988;Chuvieco et al, 2002). Apart from studying the water stress, the utility of SWIR band has been demonstrated for better crop separability (Sharma et al, 1995;Dadhwal et al, 1996) and more accurate crop classification (Panigrahy and Parihar, 1992;Manjunath et al, 1998). In this context, the present study was conducted to find out the usefulness of SWIR band in AWiFS data, for the discrimination of different Rabi season crops and other vegetation using various multivariate statistics and classification approaches.…”

Section: Introductionmentioning

confidence: 98%