Development of a Remote Sensing-Based “Boro” Rice Mapping System

Abstract

Rice is one of the staple foods across the world, thus information about its production is essential for ensuring food security. Here, our objective was to develop a method for mapping -boro‖ rice (i.e., cultivated during the months January to May) in a Bangladeshi context. In this paper, we used a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 16-day composite of normalized difference vegetation index (NDVI) at 250 m spatial resolution in conjunction with ancillary datasets (i.e., land use map, crop calendar, and ground-based rice production information) during the period 2007-2012. The proposed method consisted of three procedures: (i) ISODATA clustering and determining the boro rice signatures in temporal dimension using data from the period 2007-2009; (ii) formulating a mathematical model for extracting the boro rice areas using data from the period 2007-2009; and (iii) model calibration using data from the period 2007-2009 and its validation using data from the period 2010-2012. The implementation of the abovementioned procedures revealed reasonable agreements between the model (i.e., MODIS-based) and ground-based estimates of boro rice area at both country (i.e., percentage error in the range −0.83-1.42%) and district levels (i.e., r 2 in the range 0.69-0.89) during the period 2010-2012. Our proposed method demonstrated its effectiveness in mapping rice system at the regional/country scale.

Development of a remote sensing-based rice yield forecasting model

“…It would be interesting to note that the outcomes of the proposed method outperformed the earlier one. For example, the overall RE's were in the range ±10% using the proposed method in comparison to about ±30% using the method described in the earlier study (Mosleh & Hassan, 2014) (see Fig. 6 for details).…”

“…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.…”

“…Our findings implied that more images induced more error similar to other studies, e.g., (LeGrand, 2002); which were most likely related with data/image redundancies. In order to address this, one of the possible alternates would be the employment of 'principle component analysis' over all the ten images acquired during the entire growing season (e.g., Mosleh & Hassan, 2014); which was beyond the scope of this paper. Note that both methods would complement each other as understanding the area (thus the yield as described previously) approximately one to two months prior to harvesting would be critical in ensuring food security.…”

Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and Land Based Observations

“…The PBIAS between the predicted yield showed a variation of less than ±10 per cent in 5 out of the 10 districts under study. This disagreements between the actual and predicted rice yield estimates could be attributed by other factors, such as atmospheric interference such as cloud (Mkhabela et al, 2011), micro level weather variation and water availability (Son et al, 2013) and uncertaininty associated with ground based estimates (Mosleh and Hassan, 2014).…”

Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images

“…The ratios of the changes in amplitude of the LSWI to the two-band Enhanced Vegetation Index 2 (EVI2) during the period from the tillering to the heading stage were used as one indicator to discriminate rice from non-rice fields [26]. Mosleh and Hassan developed a method for mapping "Boro" rice in Bangladesh using the MODIS-derived 16-day composite NDVI at a spatial resolution of 250 m [27]. The ISODATA clustering and the formulation of the mathematical model were the key procedures of this algorithm.…”