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ARS Home » Pacific West Area » Parlier, California » San Joaquin Valley Agricultural Sciences Center » Water Management Research » Research » Publications at this Location » Publication #342910

Research Project: Develop Water Management Strategies to Sustain Water Productivity and Protect Water Quality in Irrigated Agriculture

Location: Water Management Research

Title: Better almond water stress monitoring using fractional-order moments of non-normalized difference vegetation index

Author
item Zhao, Tiebiao - University Of California
item Doll, David - University Of California - Cooperative Extension Service
item Wang, Dong
item Chen, Yangquan - University Of California

Submitted to: Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 7/12/2017
Publication Date: 7/17/2017
Citation: Zhao, T., Doll, D., Wang, D., Chen, Y. 2017. Better almond water stress monitoring using fractional-order moments of non-normalized difference vegetation index. American Society of Agricultural and Biological Engineering (ASABE) Annual International Meeting, Spokane, Washington. Paper No. 1701593. 1-6. doi: 10.13031/aim.201701593.

Interpretive Summary:

Technical Abstract: Stem water potential (SWP) has become a very popular tool for farmers to monitor the water status of almond trees. However, it is labor intensive and time consuming to scale up the measurements in the large field. With the development of unmanned aerial vehicles (UAVs) and sensing payload, it becomes possible to monitor the water status much more efficiently with UAV-based multispectral images of higher spatial resolution and more flexible temporal resolution. Driven by this possibility, studies have been started in a commercial almond orchard since 2014 to research almond water stress monitoring using the small unmanned aerial vehicle and the modified multispectral camera. More specifically, we are researching how to predict almond SWP by extracting information from these multispectral images. Recent experiments showed that traditional vegetation indices such as normalized difference vegetation index (NDVI) do not work very well with high resolution aerial images to predict SWP. Meanwhile, we found non-normalized difference vegetation index (NNDVI) between near-infrared (NIR) band, blue band, and its higher order moments have a better correlation with SWP. In this paper, we proposed the fractional-order moments of NNDVI and discussed its correlation with SWP. It is shown that the correlation between the proposed fractional-order moments of NNDVI and SWP is more significant than that between the traditional NDVI and SWP.