Use of Remote Sensing to Estimate Paddy Area and Production:

Improving the Estimation of Rice Crop Damage from Flooding Events Using Open-Source Satellite Data and UAV Image Data

“…This issue was also emphasized in a study by Rahman and Di (2020), where they highlighted the challenges associated with past crop loss assessments, which were generalized and time-intensive due to reliance on survey-based data collection. To enable rapid flood loss assessment, according to them, the availability of remote sensing data plays a key role [6,17]. Remote sensing, as defined by the US Geological Survey (USGS), is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation from a distance [33].…”

“…Currently, the estimation of agricultural damage caused by flooding in most parts of the country relies on manual approaches. Rotairo et al [6] stated that many developing Asian and Pacific economies rely on administrative reporting systems to gather data on agricultural production and land use, primarily due to limited financial resources for conducting comprehensive agricultural surveys or censuses. However, a crucial need exists to bolster statistical capacity and enhance skills in these regions before large-scale censuses can be implemented.…”

“…This approach is cost-effective and offers more timely estimates than censuses, but it can result in measurement errors, subjectivity, and data inconsistencies. Reports from local administrative offices sometimes contain planned or adjusted figures, leading to inaccuracies and flawed decision-making by local officials [6]. The national disaster database in the Philippines, containing information on agricultural losses, is not always available.…”

Leveraging Technology and Innovation for Disaster Risk Management and Financing

“…Another company uses artificial intelligence techniques to combine aerial imagery of structural characteristics and other relevant factors such as elevation and vegetation proximity with historical and simulated data on potential hazards to provide additional insights on potential risk to support underwriting decisions (Zesty.ai n.d.). In some cases, the additional risk insight has increased the availability of coverage (see Box 5.2) Earth observation imagery can play a critical role in monitoring cultivation and yields for the extension of agricultural insurance (Mcintosh and Mansini 2018), (Rotairo et al 2019), (ADB 2018b), either on a stand-alone basis or in combination with field survey data to address gaps in coverage (Guan et al 2018). One recent study found that the use of publicly available Google Earth imagery for measuring the size of cultivated land in the Lao People's Democratic Republic, the Philippines, Thailand, and Viet Nam provided sufficient accuracy to serve as a cost-effective alternative for validating self-reported information from farmers (Dillon and Rao 2018).…”