Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor
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 ph...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/7/1075 |
| _version_ | 1797626545681989632 |
|---|---|
| author | Sascha Heinemann Bastian Siegmann Frank Thonfeld Javier Muro Christoph Jedmowski Andreas Kemna Thorsten Kraska Onno Muller Johannes Schultz Thomas Udelhoven Norman Wilke Uwe Rascher |
| author_facet | Sascha Heinemann Bastian Siegmann Frank Thonfeld Javier Muro Christoph Jedmowski Andreas Kemna Thorsten Kraska Onno Muller Johannes Schultz Thomas Udelhoven Norman Wilke Uwe Rascher |
| author_sort | Sascha Heinemann |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:11:55Z |
| format | Article |
| id | doaj.art-904542d3eec64899b703725e209e1c49 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T10:11:55Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-904542d3eec64899b703725e209e1c492023-11-16T14:27:04ZengMDPI AGRemote Sensing2072-42922020-03-01127107510.3390/rs12071075Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral SensorSascha Heinemann0Bastian Siegmann1Frank Thonfeld2Javier Muro3Christoph Jedmowski4Andreas Kemna5Thorsten Kraska6Onno Muller7Johannes Schultz8Thomas Udelhoven9Norman Wilke10Uwe Rascher11Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52428 Jülich, GermanyInstitute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52428 Jülich, GermanyGerman Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Weßling, GermanyCenter for Remote Sensing of Land Surfaces (ZFL), University of Bonn, 53115 Bonn, GermanyInstitute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52428 Jülich, GermanyDepartment of Geophysics, University of Bonn, 53115 Bonn, GermanyField Lab Campus Klein-Altendorf, University of Bonn, 53359 Rheinbach, GermanyInstitute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52428 Jülich, GermanyDepartment of Geography, Ruhr-University Bochum, 44801 Bochum, GermanyDepartment of Environmental Remote Sensing & Geoinformatics, University of Trier, 54296 Trier, GermanyInstitute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52428 Jülich, GermanyInstitute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52428 Jülich, GermanyLand 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.https://www.mdpi.com/2072-4292/12/7/1075UAVthermal infraredmultispectral VNIRLSTemissivityNDVI thresholds |
| spellingShingle | Sascha Heinemann Bastian Siegmann Frank Thonfeld Javier Muro Christoph Jedmowski Andreas Kemna Thorsten Kraska Onno Muller Johannes Schultz Thomas Udelhoven Norman Wilke Uwe Rascher Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor Remote Sensing UAV thermal infrared multispectral VNIR LST emissivity NDVI thresholds |
| title | Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor |
| title_full | Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor |
| title_fullStr | Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor |
| title_full_unstemmed | Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor |
| title_short | Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor |
| title_sort | land surface temperature retrieval for agricultural areas using a novel uav platform equipped with a thermal infrared and multispectral sensor |
| topic | UAV thermal infrared multispectral VNIR LST emissivity NDVI thresholds |
| url | https://www.mdpi.com/2072-4292/12/7/1075 |
| work_keys_str_mv | AT saschaheinemann landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT bastiansiegmann landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT frankthonfeld landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT javiermuro landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT christophjedmowski landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT andreaskemna landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT thorstenkraska landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT onnomuller landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT johannesschultz landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT thomasudelhoven landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT normanwilke landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor AT uwerascher landsurfacetemperatureretrievalforagriculturalareasusinganoveluavplatformequippedwithathermalinfraredandmultispectralsensor |