Estimation of rice yield using IRS-1A digital data in coastal tract of Orissa

Patel, N. K.; Ravi, N.; Navalgund, R. R.; Dash, Ratnakar; Das, Kajal; Patnaik, S.

doi:10.1080/01431169108955256

Cited by 35 publications

(12 citation statements)

References 7 publications

(4 reference statements)

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“…Yield models based on vegetation indices derived from spectral data have been developed for crops like rice (Ravi et at., 1992;Patel et al, 1991) and wheat Dnbey and Sharma, 1993). Development of yield models using SAR data is in the preliminary stage.…”

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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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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“…Rice has seen fewer studies (e.g. Patel et al, 1991;Tennakoon et al, 1992;Wang et al, 2010;Peng et al, 2014;Son et al, 2014) even though it is also a globally dominant agricultural commodity. Reasons for this are unknown but it could be a result of tending to be grown in more tropical, thus cloudy, regions make its monitoring from commonly used optical type sensors an increased challenge.…”

Section: Introductionmentioning

confidence: 98%

A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

Johnson

2016

International Journal of Applied Earth Observation and Geoinfor

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a b s t r a c tAn exploratory assessment was undertaken to determine the correlation strength and optimal timing of several commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) composited imagery products against crop yields for 10 globally significant agricultural commodities. The crops analyzed included barley, canola, corn, cotton, potatoes, rice, sorghum, soybeans, sugarbeets, and wheat. The MODIS data investigated included the Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Production (GPP), in addition to daytime Land Surface Temperature (DLST) and nighttime LST (NLST). The imagery utilized all had 8-day time intervals, but NDVI had a 250 m spatial resolution while the other products were 1000 m. These MODIS datasets were also assessed from both the Terra and Aqua satellites, with their differing overpass times, to document any differences. A follow-on analysis, using the Terra 250 m NDVI data as a benchmark, looked at the yield prediction utility of NDVI at two spatial scales (250 m vs. 1000 m), two time precisions (8-day vs. 16-day), and also assessed the Enhanced Vegetation Index (EVI, at 250 m, 16-day). The analyses spanned the major farming areas of the United States (US) from the summers of 2008-2013 and used annual county-level average crop yield data from the US Department of Agriculture as a basis. All crops, except rice, showed at least some positive correlations to each of the vegetation related indices in the middle of the growing season, with NDVI performing slightly better than FPAR. LAI was somewhat less strongly correlated and GPP weak overall. Conversely, some of the crops, particularly canola, corn, and soybeans, also showed negative correlations to DLST mid-summer. NLST, however, was never correlated to crop yield, regardless of the crop or seasonal timing. Differences between the Terra and Aqua results were found to be minimal. The 1000 m resolution NDVI showed somewhat poorer performance than the 250 m and suggests spatial resolution is helpful but not a necessity. The 8-day versus 16-day NDVI relationships to yields were very similar other than for the temporal precision. Finally, the EVI often showed the very best performance of all the variables, all things considered.Published by Elsevier B.V.

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“…These images, in general, depict crop-specific characteristics, which could be important in developing pre-harvest yield forecasting models (Jing-Feng et al, 2002;Liu & Kogan, 2002;Prasad et al, 2006;Salazar et al, 2007;Mkhabela et al, 2011). For example: (i) Patel et al (1991) established an empirical relationship between the Indian Remote Sensing Linear Imaging Self Scanning (IRS LISS)-derived ratio between near infrared (NIR) and red (R) spectral bands and ground-based yield; and found that the coefficient of determination (R 2 ), root mean square error (RMSE), and deviations were 0.52, 2.62, and in the range 2-14%, respectively, over India; (ii) Rahman et al (2009;2012) utilized Advanced Very High Resolution Radiometer (AVHRR)-derived 7-day composite of normalized difference vegetation index (NDVI) and brightness temperature at 16 km resolution to compute several vegetation health-related indices such as vegetation condition index, temperature condition index, and vegetation health index in forecasting yield for two types of rice, i.e., aus and aman over Bangladesh. They observed that modelled and ground-based rice yield revealed a R 2 of 0.56 and 0.89 for aus and aman respectively; (iii) Savin & Isaev (2010) used MODIS-derived 10-day composite of NDVI at 250m resolution, fraction of absorbed radiation and two meteorological variables (i.e., temperature and incident solar radiation) to develop a process-based model for forecasting rice yield over Republic of Kalmykia.…”

Section: Introductionmentioning

confidence: 99%

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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Estimation of rice yield using IRS-1A digital data in coastal tract of Orissa

Cited by 35 publications

References 7 publications

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

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

A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

Development of a remote sensing-based rice yield forecasting model

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