scholarly journals Improving Accuracy of Unmanned Aerial System Thermal Infrared Remote Sensing for Use in Energy Balance Models in Agriculture Applications

2021 ◽  
Vol 13 (9) ◽  
pp. 1635
Author(s):  
Mitchell S. Maguire ◽  
Christopher M. U. Neale ◽  
Wayne E. Woldt
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Thermal Infrared ◽  
Temperature Measurements ◽  
Unmanned Aerial System ◽  
Thermal Camera ◽  
Thermal Imagery ◽  
Warm Up ◽  
Thermal Measurements ◽  
Improving Accuracy

Unmanned aerial system (UAS) remote sensing has rapidly expanded in recent years, leading to the development of several multispectral and thermal infrared sensors suitable for UAS integration. Remotely sensed thermal infrared imagery has been used to detect crop water stress and manage irrigation by leveraging the increased thermal signatures of water stressed plants. Thermal infrared cameras suitable for UAS remote sensing are often uncooled microbolometers. This type of thermal camera is subject to inaccuracies not typically present in cooled thermal cameras. In addition, atmospheric interference also may present inaccuracies in measuring surface temperature. In this study, a UAS with integrated FLIR Duo Pro R (FDPR) thermal camera was used to collect thermal imagery over a maize and soybean field that contained twelve infrared thermometers (IRT) that measured surface temperature. Surface temperature measurements from the UAS FDPR thermal imagery and field IRTs corrected for emissivity and atmospheric interference were compared to determine accuracy of the FDPR thermal imagery. The comparison of the atmospheric interference corrected UAS FDPR and IRT surface temperature measurements yielded a RMSE of 2.24 degree Celsius and a R2 of 0.85. Additional approaches for correcting UAS FDPR thermal imagery explored linear, second order polynomial and artificial neural network models. These models simplified the process of correcting UAS FDPR thermal imagery. All three models performed well, with the linear model yielding a RMSE of 1.27 degree Celsius and a R2 of 0.93. Laboratory experiments also were completed to test the measurement stability of the FDPR thermal camera over time. These experiments found that the thermal camera required a warm-up period to achieve stability in thermal measurements, with increased warm-up duration likely improving accuracy of thermal measurements.

scholarly journals Editorial for the Special Issue “Remote Sensing Monitoring of Land Surface Temperature”

2021 ◽  
Vol 13 (9) ◽  
pp. 1765
Author(s):  
Juan M. Sánchez ◽  
César Coll ◽  
Raquel Niclòs
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
State Of The Art ◽  
Thermal Infrared ◽  
The State ◽  
Special Issue ◽  
Tir Domain ◽  
Remote Sensing Monitoring

The combination of the state-of-the-art in the thermal infrared (TIR) domain [...]

Surface Temperature Monitoring by Satellite Thermal Infrared Imagery at Mayon Volcano of Philippines, 1988-2019

2020 ◽  
Author(s):  
Hai-Po Chan ◽  
Kostas Konstantinou
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Satellite Remote Sensing ◽  
Thermal Infrared ◽  
Active Volcano ◽  
Infrared Imagery ◽  
Mayon Volcano ◽  
Thermal Infrared Imagery

<p>Mayon Volcano on eastern Luzon Island is the most active volcano in the Philippines. It is named and renowned as the "perfect cone" for the symmetric conical shape and has recorded eruptions over 50 times in the past 500 years. Geographically the volcano is surrounded by the eight cities and municipalities with 1 million inhabitants. Currently, its activity is daily monitored by on-site observations such as seismometers installed on Mayon's slopes, plus, electronic distance meters (EDMs), precise leveling benchmarks, and portable fly spectrometers. Compared to existing direct on-site measurements, satellite remote sensing is currently assuming an essential role in understanding the whole picture of volcanic processes. The vulnerability to volcanic hazards is high for Mayon given that it is located in an area of high population density on Luzon Island. However, the satellite remote sensing method and dataset have not been integrated into Mayon’s hazard mapping and monitoring system, despite abundant open-access satellite dataset archives. Here, we perform multiscale and multitemporal monitoring based on the analysis of a nineteen-year Land Surface Temperature (LST) time series derived from satellite-retrieved thermal infrared imagery. Both Landsat thermal imagery (with 30-meter spatial resolution) and MODIS (Moderate Resolution Imaging Spectroradiometer) LST products (with 1-kilometer spatial resolution) are used for the analysis. The Ensemble Empirical Mode Decomposition (EEMD) is applied as the decomposition tool to decompose oscillatory components of various timescales within the LST time series. The physical interpretation of decomposed LST components at various periods are explored and compared with Mayon’s eruption records. Results show that annual-period components of LST tend to lose their regularity following an eruption, and amplitudes of short-period LST components are very responsive to the eruption events. The satellite remote sensing approach provides more insights at larger spatial and temporal scales on this renowned active volcano. This study not only presents the advantages and effectiveness of satellite remote sensing on volcanic monitoring but also provides valuable surface information for exploring the subsurface volcanic structures in Mayon.</p>

scholarly journals Near-surface temperature inversion during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

2018 ◽  
Vol 12 (3) ◽  
pp. 907-920 ◽  
Author(s):  
Alden C. Adolph ◽  
Mary R. Albert ◽  
Dorothy K. Hall
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
Temperature Inversion ◽  
Temperature Measurements ◽  
Cold Bias ◽  
Near Surface ◽  
In Situ Ir

Abstract. As rapid warming of the Arctic occurs, it is imperative that climate indicators such as temperature be monitored over large areas to understand and predict the effects of climate changes. Temperatures are traditionally tracked using in situ 2 m air temperatures and can also be assessed using remote sensing techniques. Remote sensing is especially valuable over the Greenland Ice Sheet, where few ground-based air temperature measurements exist. Because of the presence of surface-based temperature inversions in ice-covered areas, differences between 2 m air temperature and the temperature of the actual snow surface (referred to as “skin” temperature) can be significant and are particularly relevant when considering validation and application of remote sensing temperature data. We present results from a field campaign extending from 8 June to 18 July 2015, near Summit Station in Greenland, to study surface temperature using the following measurements: skin temperature measured by an infrared (IR) sensor, 2 m air temperature measured by a National Oceanic and Atmospheric Administration (NOAA) meteorological station, and a Moderate Resolution Imaging Spectroradiometer (MODIS) surface temperature product. Our data indicate that 2 m air temperature is often significantly higher than snow skin temperature measured in situ, and this finding may account for apparent biases in previous studies of MODIS products that used 2 m air temperature for validation. This inversion is present during our study period when incoming solar radiation and wind speed are both low. As compared to our in situ IR skin temperature measurements, after additional cloud masking, the MOD/MYD11 Collection 6 surface temperature standard product has an RMSE of 1.0 ∘C and a mean bias of −0.4 ∘C, spanning a range of temperatures from −35 to −5 ∘C (RMSE = 1.6 ∘C and mean bias = −0.7 ∘C prior to cloud masking). For our study area and time series, MODIS surface temperature products agree with skin surface temperatures better than previous studies indicated, especially at temperatures below −20 ∘C, where other studies found a significant cold bias. We show that the apparent cold bias present in other comparisons of 2 m air temperature and MODIS surface temperature may be a result of the near-surface temperature inversion. Further investigation of how in situ IR skin temperatures compare to MODIS surface temperature at lower temperatures (below −35 ∘C) is warranted to determine whether a cold bias exists for those temperatures.

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