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Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.

This study aimed to develop a new method for assessing damage to rice plants by pests and diseases using remote sensing data to enable greater efficiency and accuracy for payment of indemnity in the agricultural insurance system of Indonesia, formally operationalized in 2016. The relationships between bacterial leaf blight (BLB) damage ratio in rice crops evaluated by pest observers using the current visual inspection method and the reflectance of each observation band of RapidEye and Sentinel 2, normalized difference vegetation index (NDVI), green NDVI (GNDVI), and red edge multiplied by the green band index (RGI) were studied. The results showed a positive relationship between BLB damage intensity and reflectance of visible wavelength bands, and a particularly strong positive correlation between the red band and BLB damage intensity. The BLB damage intensity can be evaluated based on pixels and paddy parcels. Time series analysis was conducted using Sentinel-2 data acquired during different plant growth periods such as heading, flowering, ripening, maturity, and harvesting. The results showed a strong correlation between the BLB damage intensity and reflectance of the red edge band at the rice heading and flowering stages; the correlation of BLB damage intensity with the reflectance of the visible range became stronger as the rice plant approached the harvesting stage. This study clearly demonstrated that BLB symptoms can be successfully detected and evaluated approximately one or one and a half months before the harvesting period using remote sensing data. We propose that the BLB damage intensity, currently assessed by pest observers through visual inspection methods, can be calculated from satellite data, suggesting that the satellite sensor could play a role similar to the human eye.

This study presents a method for detecting rice crop damage due to bacterial leaf blight (BLB) infestation. Rice crop samples are first analyzed using a handheld spectroradiometer. Then, multi-temporal satellite image analysis is used to determine the most suitable vegetation indices for detecting BLB. The results showed that healthy plants have the highest first derivative value of spectral reflectance of the different categories of diseased plants. Significant difference can be found at approximately 690-770 nm (red edge region) which peak or maximum of the first derivative occurs in healthy crop whereas the highest percentage of BLB showed the lowest in that region. Moreover, visible bands such as blue, green, red, and red edge 1 band show variation of correlation in the early (vegetative) to generative stage then getting high especially in early of harvesting stage than the other bands; the NIR band exhibits a low correlation from the early stage of the growing season whereas the red and red edge bands reveal the highest correlations in the later stage of harvesting. Similarly, the satellite image analysis also reveals that disease incidence gradually increases with increasing age of the plant. The vegetation indices whose formulas consist of blue, green, red, and red edge bands (NGRDI, NPCI, and PSRI) exhibit the highest correlation with BLB infestation. NPCI and PSRI indices indicate that crop stress due to BLB is detected from ripening stage of NPCI then the senescence condition is then detected 12 days later. The coefficients of determination between these indices and BLB are 0.44, 0.63, and 0.67, respectively