Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-04, Vol.12 (7), p.1075
• Main Authors: Heinemann, Sascha, Siegmann, Bastian, Thonfeld, Frank, Muro, Javier, Jedmowski, Christoph, Kemna, Andreas, Kraska, Thorsten, Muller, Onno, Schultz, Johannes, Udelhoven, Thomas, Wilke, Norman, Rascher, Uwe
• Format: Article
• Language: English
• Subjects: Agricultural management; Agriculture; Algorithms; Altitude; Atmospheric correction; Crop diseases; Crop yield; Crops; Drought; Earth surface; Emissivity; Error analysis; Ground stations; Ground truth; Heat; Humidity; Infrared detectors; Irrigation; Land cover; Land surface temperature; Land use; LST; Meteorological parameters; Mosaics; multispectral VNIR; NDVI thresholds; Plant diseases; Remote sensing; Retrieval; Sensors; Studies; thermal infrared; Thermal measurement; UAV; Uncertainty analysis; Unmanned aerial vehicles; Vegetation; Water exchange; Water shortages; Water supply; Weather stations
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12071075

Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data.