Thursday, 10 January 2019: 2:15 PM
North 126BC (Phoenix Convention Center - West and North Buildings)
Agricultural drought refers to circumstances when soil moisture is insufficient to support crop growth and production. It is one of major recurring natural disasters, which increasingly threats global agriculture, food security, and economy. Remote sensing has been a major technique for monitoring agricultural drought in large geographic area. Most of remote sensing techniques used in agricultural drought monitoring is based on remotely sensed vegetation indexes. One of the commonly used indexes is the vegetation condition index (VCI), which compares the current vegetation condition with historic normal to determine the severity of agricultural drought. VCI has been successfully used to monitor the agricultural drought at regional, country, continental, and global scales. However, many of its shortcomings have also been found during its applications. One of major shortcomings is that it doesn’t consider the impacts of inter-annual temperature variability on vegetation condition, especially at early or late growing session when temperature is a major control factor for vegetation condition. To address this problem, a remote-sensing-based vegetation health index (VHI), which linearly combines the VCI and Temperature Condition Index (TCI), has been used to monitor agricultural drought. However, VHI suffers two major problems: 1) the temperature’s impact on vegetation condition varies with the progress of a growing season but VHI treats the temperature’s impact all the same for the entire growing season; and 2) it calculates the TCI using instant skin temperature which can be easily changed by very short-term weather conditions. In order to monitor agricultural drought timely and accurately, in this study the Agricultural Drought Index (ADI) is proposed to remedy the two major problems associated with VHI by using temporal-dependent weight for combining VCI and TCI and using weekly moving average instead of instant skin temperature to calculate VHI. The index is conceptually and computationally straightforward and derived from satellite remote sensing data only, and thus has great potential for operational applications in large geographic areas. The performance of ADI has been evaluated by comparing it with two well-recognized agricultural drought indicators: VCI and VHI. The evaluation results indicate that ADI performs much better than the two indicators in monitoring agricultural drought. This presentation discusses the concept and feasibility of ADI and the case study of using ADI in agricultural drought monitoring.
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