Abstract
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.
Highlights
Global climate change and natural disasters are anticipated to exert a substantial impact on food production worldwide, and adaptation measures to mitigate this impact have attracted considerable attention
Scatter diagrams between the bacterial leaf blight (BLB) damage intensity and the blue, green, red, and red edge bands as well as RGI were created, which confirmed a clear tendency that the higher the BLB damage intensity is, the larger the reflectance value becomes at each band and index
As for normalized difference vegetation index (NDVI) and green NDVI (GNDVI), which are frequently used for vegetation research, the results confirmed that the higher the BLB damage intensity is, the smaller the reflectance value becomes at each band and index
Summary
Global climate change and natural disasters are anticipated to exert a substantial impact on food production worldwide, and adaptation measures to mitigate this impact have attracted considerable attention. In their fifth assessment report, the Intergovernmental Panel on Climate Change (IPCC) has predicted a high probability of negative impact of climate change on food crops (IPCC, 2014). The food security vulnerability and impact of climate change can reduce the speed of economic growth and induce socio-economic disadvantages. To resolve this situation, the concept of agricultural insurance was introduced and has gradually disseminated internationally. Agricultural insurance is an important social infrastructure to secure stability, which is defined as one of the four pillars of food security by the Food and Agriculture Organization jas.ccsenet.org
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