Estimation of cropped area and grain yield of rice using remote sensing data

Tennakoon, S.B.; Murty, V.V.N.; Eiumnoh, Apisit

doi:10.1080/01431169208904047

Cited by 93 publications

(40 citation statements)

References 2 publications

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“…Hence, there needs to be an effective means for rice monitoring and yield prediction with high-accuracy and low-cost. Since remote sensing data could be derived from different sensors, there has been an increasing amount of international interest in rice monitoring through satellites [2][3][4] . However, rice crop is mainly cultivated in warm climate with plentiful rainfall and dense cloud cover.…”

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confidence: 99%

A scheme for regional rice yield estimation using ENVISAT ASAR data

Shen

Yang

BingBai

et al. 2009

Sci. China Ser. D-Earth Sci.

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Information on rice growing areas and rice production is critical for most rice growing countries to make state and economic policies. However, the areas where rice crop is cultivated are often cloudy and rainy, which entails the use of radar remote sensing data for rice monitoring. In this paper, a practical scheme to integrate multi-temporal and multi-polarization ENVISAT ASAR data into rice crop model for regional rice yield estimation has been presented. To achieve this, rice distribution information should be obtained first by rice mapping method to retrieve rice fields from ASAR images, and then an assimilation method is applied to use the observed multi-temporal rice backscattering coefficients which are grouped for each rice pixel to re-initialize ORYZA2000 to predict rice yield. The assimilation method re-initializes the model with optimal input parameters, allowing a better temporal agreement between the rice backscattering coefficients retrieved from ASAR data and the rice backscattering coefficients simulated by a coupled model, i.e., the combination of ORYZA2000 and a semi-empirical rice backscatter model through LAI. The SCE-UA optimization algorithm is employed to determine the optimal set of input parameters. After the re-initialization, rice yield for each rice pixel is calculated, and the yield map over the area of interest is produced. The scheme was validated over Xinghua study area located in the middle of Jiangsu Province of China by using the data set of an experimental campaign carried out during the 2006 rice season. The result shows that the obtained rice yield map generally overestimates the actual rice production by 13% on average and with a root mean square error of approximately 1133 kg/ha on validation sites, but the tendency of rice growth status and spatial variation of the rice yield are well predicted and highly consistent with the actual production variation.rice yield map, crop model, data assimilation, optimization algorithm, classification, ASAR

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confidence: 99%

A scheme for regional rice yield estimation using ENVISAT ASAR data

Shen

Yang

BingBai

et al. 2009

Sci. China Ser. D-Earth Sci.

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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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“…Remote sensing-based methods have already been proven as an effective alternative for mapping rice area (Tennakoon et al, 1992;Yang et al, 2008). The benefits of remote sensing technology include: (i) spatial coverage over a large geographic area (ii) availability during all seasons (iii) relatively low cost, since some optical images are freely available (i.e., MODIS, Landsat); although radar data are usually a bit costly (e.g., CAD$4000 per scene); (iv) efficient analysis (v) they provide information in a timely manner and (vi) they are capable of delineating detailed spatial distributions of areas under rice cultivation (Mosleh et al, 2015).…”

Section: Issn: 2319-7706 Volume 6 Number 7 (2017) Pp 2327-2335mentioning

confidence: 99%