Soil moisture retrieval model design with multispectral and infrared images from unmanned aerial vehicles using convolutional neural network

Abstract

This paper deals with a soil moisture retrieval model design with airborne measurements for remote monitoring of soil moisture level in large crop fields. A small quadrotor unmanned aerial vehicle (UAV) is considered as a remote sensing platform for high spatial resolutions of airborne images and easy operations. A combination of multispectral and infrared (IR) sensors is applied to overcome the effects of canopies convering the field on the sensor measurements. Convolutional neural network (CNN) is utilized to take the measurement images directly as inputs for the soil moisture retrieval model without loss of information. The procedures to obtain an input image corresponding to a certain soil moisture level measurement point are addressed, and the overall structure of the proposed CNN-based model is suggested with descriptions. Training and testing of the proposed soil moisture retrieval model are conducted to verify and validate its performance and address the effects of input image sizes and errors on input images. The soil moisture level estimation performance decreases when the input image size increases as the ratio of the pixel corresponding to the point to estimate soil moisture level to the total number of pixels in the input image, whereas the input image size should be large enough to include this pixel under the errors in input images. The comparative study shows that the proposed CNN-based algorithm is advantageous on estimation performance by maintaining spatial information of pixels on the input images.