Early estimation of rice area using temporal ERS-1 synthetic aperture radar data a case study for the Howrah and Hughly districts of West Bengal, India

Panigrahy, Sushma; Chakraborty, Manab; Sharma, Suchita; Kundu, N.; Ghose, Sankar; Pal, Monit

doi:10.1080/014311697218133

Cited by 39 publications

(10 citation statements)

References 2 publications

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“…It revealed strong relations (i.e., R 2 in the range 0.93 to 0.95 and RMSE in the range 30,519 to 37,451 ha, at 23 district-levels) between modeled and ground-based area estimates. Our results were also similar to other studies: Panigrahy et al (1997) obtained accuracies in the range 85.8 to 91.5% in the early estimation of rice areas in Wet Bengal, India; (ii) Patel et al (2004) found approximately 95% accuracy in forecasting Kharif rice acreage in Orissa, India; (iii) Chen et al (2011) In addition, we also compared the boro rice area estimated using only two images acquired during the initial/transplantation and peak greenness stages in the scope of this paper with those extracted in an earlier study (Mosleh & Hassan, 2014). In that study, we employed ten 16-day composite of NDVI images acquired over the entire growing season and estimated the boro rice area after the harvesting of the crop.…”

Section: Delineation Of Areas Under Rice Cultivationsupporting

confidence: 83%

Development of a remote sensing-based rice yield forecasting model

Mosleh

Hassan

Chowdhury

2016

Span. j. agric. res.

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This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

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Section: Delineation Of Areas Under Rice Cultivationsupporting

confidence: 83%

Development of a remote sensing-based rice yield forecasting model

Mosleh

Hassan

Chowdhury

2016

Span. j. agric. res.

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“…Bush and Ulaby (1978) showed that use of multi-date data improves the crop type discrimination. Synthetic Aperture Radar data from ERS-1 and 2 had shown promising results for rice crop detection Patel et al, 1995;Panigrahy et al, 1997;Premlata et al, 1994). Three date data acquired during crop establishment, vegetative and grain filling stages were used for in-season rice area estimation in India .…”

Section: Introductionmentioning

confidence: 95%

Multiple forecasts of kharif rice in orissa state-four year experience of fasal pilot study

Patel

Chakraborty

Dutta

et al. 2004

J Indian Soc Remote Sens

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Considering the requirement of multiple pre-harvest crop forecasts, the concept of Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL) has been formulated. Development of procedure and demonstration of this technique for four in-season forecasts for kharif rice has been carried out as a pilot study in Orissa State since 1998. As the availability of cloud-free optical remote sensing data during kharif season is very poor for Orissa state, multi-date RADARSAT SCANSAR data were used for acreage estimation of kharif rice. Meteorological models have been developed for early assessment of acreage and prediction of yield at mid and late crop growth season. Four in-season forecasts were made during four kharif seasons (1998)(1999)(2000)(2001); the first forecast of zone level rice acreage at the beginning of kharif crop season using meteorological models, second forecast of district level acreage at mid growth season using two-date RADARSAT SCANSAR data and yield using meteorological models, third forecast at late growth season of district level acreage using three-date RADARSAT SCANSAR data and yield using meteorological models and revised forecast incorporating field observations at maturity. The results of multiple forecasts have shown rice acreage estimation and yield prediction with deviation up to 14 and 11 per cent respectively. This study has demonstrated the potential of FASAL concept to provide inseason multiple forecasts using data of remote sensing, meteorology and land based observations.

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“…Capturing the flooding stage in SAR images is essential for the correct identification of rice fields [39]. SAR images acquired at the right time to capture flooding [39] and early and late growth stages [40] improve rice crop identification. The availability of high-spatial-resolution SAR images with multitemporal resolution of 11 to 16 days such as Terra SAR-X and Cosmo Skymed (CSK) can be used to capture small patches of rice paddies.…”

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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Early estimation of rice area using temporal ERS-1 synthetic aperture radar data a case study for the Howrah and Hughly districts of West Bengal, India

Cited by 39 publications

References 2 publications

Development of a remote sensing-based rice yield forecasting model

Development of a remote sensing-based rice yield forecasting model

Multiple forecasts of kharif rice in orissa state-four year experience of fasal pilot study

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

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