Development of a remote sensing-based rice yield forecasting model
نویسندگان
چکیده
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. Additional key words: food security; MODIS; multi-temporal dataset; normalized difference vegetation index. Abbreviations used: BARC (Bangladesh Agricultural Research Council); BBS (Bangladesh Bureau of Statistics); MODIS (moderate resolution imaging spectroradiometer); NDVI (normalized difference vegetation index); NIR (near infrared); R (red); R2 (coefficient of determination); RE (relative error); RMSE (root mean square error). Authors’ contributions: Conception or design: MKM and QKH. Acquisition, analysis, or interpretation of data and writing of the manuscript: MKM, QKH, and EHC. Supervision: QKH. Citation: Mosleh, M. K.; Hassan, Q. K.; Chowdhury, E. H. (2016). Development of a remote sensing-based rice yield forecasting model. Spanish Journal of Agricultural Research, Volume 14, Issue 3, e0907. http://dx.doi.org/10.5424/sjar/2016143-8347. Received: 20 Jul 2015. Accepted: 13 Jul 2016. Copyright © 2016 INIA. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial (by-nc) Spain 3.0 Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Funding: National Sciences and Engineering Research Council of Canada (grant to QKH); Egyptian Government (PhD scholarship to MKM). Competing interests: The authors have declared that no competing interests exist. Correspondence should be addressed to Quazi K. Hassan: [email protected].
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