UAV-based high spatial and temporal resolution monitoring and mapping of surface moisture status in a vineyard

Abstract

Drone-based RGB and thermal infrared remote sensing are rapidly becoming useful tools for monitoring crop and soil status for precision farming. The objectives of this study are as follows: (1) flying UAV or drone to collect large spatial scale RGB and infrared aerial images during the growing season and (2) producing high spatial–temporal resolution digital surface models and imagery mosaics to help farmers monitor surface moisture status in the study vineyard. A total of four field aerial surveys were conducted during the growing season of 2018 from May to August. A surface moisture mapping index (SMMI) model was proposed based on modified normalized difference water index and topographic wetness index. This model combines the factors of both radiant reflection properties by moisture bearing surfaces of a farming field, and the slope gradient and micro-topographic positions in the field. The results indicate that the spatial pattern of SMMI, or the relative quantities of surface moisture contents, across the vineyard was maintained the same during the entire growing season. This might be impacted by localized factors of surface slope gradient and topographic positions. Meanwhile, temporal variations of surface moisture status are mainly influenced by frequency and intensity of the precipitations.