Optimization of water use relies on accurate measurement of water status of crops. Stem water potential (SWP) has become one of the most popular methods to monitor the water status of almond trees. However, it needs to take twice visit and at least thirty minutes to obtain one measurement, which makes it very difficult to understand the water status information in the orchard level. Unmanned aerial vehicle (UAV) based remote sensing promises to deliver reliable and precise field-scale information more efficiently by providing multispectral higher-resolution images with much lower cost and higher flexibility. This paper aims to extract almond water status from UAV-based multispectral images via building the correlation between SWP and vegetation indices. Different from the traditional method that focuses on normalized difference vegetation index (NDVI) means, higher-order moments of non-normalized canopy distribution descriptors were discussed to model SWP measurements. Results showed that the proposed methods performed better than traditional NDVI mean.

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