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

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.

Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

“…LST and other parameters such as the normalized difference vegetation index (NDVI) or bare soil index (BSI) have been mostly studied in relation to urbanization [8,9]. Several studies have been conducted to analyze how changes in LST affect the variability of the microclimate of an area, the phenological cycle of vegetation [5,6], the agricultural production [10], and the land cover changes [11] using time series analysis of data. It has been observed that LST in subtropical and mid-latitude climates (between 30 • and 45 • latitude) is inversely proportional to the vegetation or tree cover of an area [12][13][14][15].…”

Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture

“…It has been reported that the emissivity of vegetation ranges from 0.96 to 0.99, while the emissivity of soils is around 0.98 [41,42]. The inaccuracy in emissivity can result in temperature errors of up to several degrees Celsius [43], and in this study we estimated emissivity of vegetation canopy and soil using the Normalized Difference Vegetation Index (NDVI), as proposed by [44].…”

Utilizing Multi-Temporal Thermal Data To Assess Environmental Land Degradation Impacts: Example From Suez Canal Region, Egypt.

“…These changes affect the degree of absorption of solar radiation, albedo, surface temperature, evaporation rates, heat transmission to the soil, heat storage, and wind turbulence. Including responsible geo-environmental issues with different contaminants such as water, air pollution, greenhouse gas emissions, and even soil surface temperature (LST) would rise rapidly, which is projected to expose 69% of the world's population by 200 of this vulnerability (Weng et al, 2004, Weng et al, 2006, Mallick et al, 2008; United Nations, 2010; Zhang et al, 2013;Zhang et al, 2016;Pal and Ziaul, 2017;Heinemann et al, 2020). Increasing the demand for the application of modern technology to de ne, manage, and incorporate environmental and hydro-environmental geospatial data processes is, therefore, important for the overall sustainable development of any country's prerequisites.…”