Ground control points (GCPs) are critical for agricultural remote sensing that require georeferencing and calibration of images collected from an unmanned aerial vehicles (UAV) at different times. However, the conventional stationary GCPs are time-consuming and labor-intensive to measure, distribute, and collect their information in a large field setup. An autonomous mobile GCP and a collaboration strategy to communicate with the UAV were developed to improve the efficiency and accuracy of the UAV-based data collection process. Prior to actual field testing, preliminary tests were conducted using the system to show the capability of automatic path tracking by reducing the root mean square error (RMSE) for lateral deviation from 34.3 cm to 15.6 cm based on the proposed look-ahead tracking method. The tests also indicated the feasibility of moving reflectance reference panels successively along all the waypoints without having detrimental effects on pixel values in the mosaicked images, with the percentage errors in digital number values ranging from −1.1% to 0.1%. In the actual field testing, the autonomous mobile GCP was able to successfully cooperate with the UAV in real-time without any interruption, showing superior performances for georeferencing, radiometric calibration, height calibration, and temperature calibration, compared to the conventional calibration method that has stationary GCPs.

Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high- and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68 °C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0 °C in the un-calibrated image to 1.01 °C in the calibrated image. Furthermore, a multiple linear regression model (R2 = 0.78; p-value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.

Abstract:In the United States, pathogens are the leading cause for rivers and streams to exceed water quality standards. The Spatially Explicit Load Enrichment Calculation Tool (SELECT) was developed to estimate bacterially contaminated water bodies based on spatial factors such as land use, soil, and population density. SELECT was originally automated using Visual Basics for Applications (VBA), which is no longer supported by the current version of ArcGIS. The aim of this research was to develop a new SELECT interface, pySELECT, using the Python programming language and to incorporate a rainfall-runoff E. coli transport module to simulate E. coli loads resulting from urban sources, such as dogs and on-site wastewater treatment systems. The pySELECT tool was applied to Lavon Lake, a semi urban study watershed in Northeast Texas. The highest potential E. coli loads were in the areas closest to the Dallas-Fort Worth metroplex, and the highest transported loads were located downstream from those identified hotspots or where the most runoff was generated. Watershed managers can use pySELECT to develop best management practices on the specific areas and fecal sources that contribute fecal contamination into a waterbody.

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