Abstract
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.
Highlights
Land surface temperature (LST) is the primary driving force of the exchange between turbulent heat flux and long-wave infrared (LWIR) radiation (8–15 μm) at the interface of the Earth’s surface and atmosphere
The objective of this study is to retrieve precise LST, taking into account the highly variable Land surface emissivity (LSE) derived by using an Normalised Difference Vegetation Index (NDVI) threshold approach, as well as the atmospheric impacts
The NDVI threshold method (NDVI-THM), using certain NDVI thresholds, was first introduced in [51], where it was applied to Advanced Very High Resolution Radiometer (AVHRR) satellite data. We identified this method as the most suitable, as it classifies the emissivity into three different surface types: bare soil (NDV I < NDV Isoil), full vegetation (NDV I > NDV Iveg), and mixed areas
Summary
Land surface temperature (LST) is the primary driving force of the exchange between turbulent heat flux and long-wave infrared (LWIR) radiation (8–15 μm) at the interface of the Earth’s surface and atmosphere. The LST is a key parameter for the physical description of the surface energy and water balance processes at the local to global scale [1,2] The potential of this crucial parameter has been repeatedly demonstrated in various thermal infrared-based studies and applications, such as evapotranspiration [3,4], hydrological modelling [5], vegetation monitoring [6], ‘urban heat island and urban development’ [7,8,9], climate change and weather conditions [1,10], agriculture [3,11], and the monitoring of land use changes in wetlands [12]. High repetition rates (depending on water supply and weather conditions) or even continuous temperature measurements are required to account for the detailed and accurate thermal mapping of water-stressed crops or in irrigation management
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