High Resolution Thermal Infrared Research Articles

PVF-10: A high-resolution unmanned aerial vehicle thermal infrared image dataset for fine-grained photovoltaic fault classification

Accurate identification of faulty photovoltaic (PV) modules is crucial for the effective operation and maintenance of PV systems. Deep learning (DL) algorithms exhibit promising potential for classifying PV fault (PVF) from thermal infrared (TIR) images captured by unmanned aerial vehicle (UAV), contingent upon the availability of extensive and high-quality labeled data. However, existing TIR PVF datasets are limited by low image resolution and incomplete coverage of fault types. This study proposes a high-resolution TIR PVF dataset with 10 classes, named PVF-10, comprising 5579 cropped images of PV panels collected from 8 PV power plants. These classes are further categorized into two groups according to the repairability of PVF, with 5 repairable and 5 irreparable classes each. Additionally, the circuit mechanisms underlying the TIR image features of typical PVF types are analyzed, supported by high-resolution images, thereby providing comprehensive information for PV operators. Finally, five state-of-the-art DL algorithms are trained and validated based on the PVF-10 dataset using three levels of resampling strategy. The results show that the overall accuracy (OA) of these algorithms exceeds 83%, with the highest OA reaching 93.32%. Moreover, the preprocessing procedure involving resampling and padding strategies are beneficial for improving PVF classification accuracy using PVF-10 datasets. The developed PVF-10 dataset is expected to stimulate further research and innovation in PVF classification.

A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km2 is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported.

Surface downward longwave radiation estimation from new generation geostationary satellite data

Surface downward longwave radiation (SDLR) is a key component of the Earth's energy budget. To use new-generation geostationary satellite data with high spatial and spectral resolution for SDLR estimation, we developed a clear-sky SDLR algorithm based on multi-channel brightness temperature (BT) and precipitable water vapor (PWV), named as MBTW, for strictly clear conditions, and a clear-sky SDLR algorithm based on atmospheric parameters (named as NewAtmos) for probably clear conditions. Then, we developed a framework to estimate all-sky SDLR; the framework combined the two clear-sky algorithms and an existing cloudy-sky algorithm. Finally, the clear-sky and all-sky SDLRs were calculated using the proposed algorithms and framework for the FY-4A and GOES-16 data. The validation results against field measurements indicated that the MBTW and NewAtmos algorithms performed well under the expected clear-sky conditions. The clear-sky SDLRs combined using MBTW and NewAtmos algorithms had RMSEs of 24.8 W/m2 and 18.3 W/m2 for the FY-4A and GOES-16 data, which is comparable to those derived using a single algorithm based on atmospheric parameters; the former portrayed better consistency with field measurements than the latter. The RMSE of instantaneous all-sky SDLR calculated using the proposed method were 24.6 W/m2 and 20.1 W/m2 for FY-4A and GOES-16 data, and they were 23.7 W/m2 and 18.3 W/m2 for the hourly SDLR. The hourly SDLR from the combined algorithms showed a better accuracy than those of the ERA5 SDLR at the same resolution, and also yielded more details of spatial variation than those from ERA5 and from single atmospheric-parameter-based algorithm, because high-resolution thermal infrared (TIR) BT was used in our method. Furthermore, using ERA5 derived PWV, cloud liquid water path and ice water path produced very similar SDLR to those produced using satellite parameters; therefore, for the conditions missing satellite parameters, the ERA5 parameters can be an alternative to achieve continuous SDLR estimation.

A TIR-Visible Automatic Registration and Geometric Correction Method for SDGSAT-1 Thermal Infrared Image Based on Modified RIFT

High-resolution thermal infrared (TIR) remote sensing images can more accurately retrieve land surface temperature and describe the spatial pattern of urban thermal environment. The Thermal Infrared Spectrometer (TIS), which has high spatial resolution among spaceborne thermal infrared sensors at present, and global data acquisition capability, is one of the sensors equipped in the SDGSAT-1. It is an important complement to the existing international mainstream satellites. In order to produce standard data products, rapidly and accurately, the automatic registration and geometric correction method needs to be developed. Unlike visible–visible image registration, thermal infrared images are blurred in edge details and have obvious non-linear radiometric differences from visible images, which make it challenging for the TIR-visible image registration task. To address these problems, homomorphic filtering is employed to enhance TIR image details and the modified RIFT algorithm is proposed to achieve TIR-visible image registration. Different from using MIM for feature description in RIFT, the proposed modified RIFT uses the novel binary pattern string to descriptor construction. With sufficient and uniformly distributed ground control points, the two-step orthorectification framework, from SDGSAT-1 TIS L1A image to L4 orthoimage, are proposed in this study. The first experiment, with six TIR-visible image pairs, captured in different landforms, is performed to verify the registration performance, and the result indicates that the homomorphic filtering and modified RIFT greatly increase the number of corresponding points. The second experiment, with one scene of an SDGSAT-1 TIS image, is executed to test the proposed orthorectification framework. Subsequently, 52 GCPs are selected manually to evaluate the orthorectification accuracy. The result indicates that the proposed orthorectification framework is helpful to improve the geometric accuracy and guarantee for the subsequent thermal infrared applications.

Eddies in motion: visualizing boundary-layer turbulence above an open boreal peatland using UAS thermal videos

Abstract. High-resolution thermal infrared (TIR) imaging is opening up new vistas in biosphere–atmosphere heat exchange studies. The rapidly developing unmanned aerial systems (UASs) and specially designed cameras offer opportunities for TIR survey with increasingly high resolution, reduced geometric and radiometric noise, and prolonged flight times. A state-of-the-art science platform is assembled using a Matrice 210 V2 drone equipped with a Zenmuse XT2 thermal camera and deployed over a pristine boreal peatland with the aim of testing its performance in a heterogeneous sedge-fen ecosystem. The study utilizes the capability of the UAS platform to hover for prolonged times (about 20 min) at a height of 500 m a.g.l. while recording high frame rate (30 Hz) TIR videos of an area of ca. 430 × 340 m. A methodology is developed to derive thermal signatures of near-ground coherent turbulent structures impinging on the land surface, surface temperature spectra, and heat fluxes from the retrieved videos. The size, orientation, and movement of the coherent structures are computed from the surface temperature maps, and their dependency on atmospheric conditions is examined. A range of spectral and wavelet-based approaches are used to infer the properties of the dominant turbulent scene structures. A ground-based eddy-covariance system and an in situ meteorological setup are used for reference.

Evaluation of a two-source patch model to estimate vineyard energy balance using high-resolution thermal images acquired by an unmanned aerial vehicle (UAV)

The use of unmanned aerial vehicles (UAVs) equipped with high-resolution thermal infrared (TIR) cameras can provide the necessary spatial variability for estimating energy balance (EB) components over heterogeneous canopies, such as those of vineyards. An experiment was carried out to evaluate a two-source patch energy balance (TSPEB) model for computing the net radiation (Rn), sensible heat flux (H), soil heat flux (G), and latent heat flux (LE) over two drip-irrigated vineyards. These vineyards were trained on a vertical shoot-positioned (VSP) system and located in the Molina and Pencahue valleys, Maule Region, Chile (35° 20’L. S, 71° 46’L. W, 86 m.a.s.l.). For this study, a UAV was equipped with a TIR camera to retrieve the surface temperature (Ts) at a very high resolution (6 cm x 6 cm). At the time of the UAV overpass, the meteorological variables and EB components were collected above the vineyard. The TSPEB model was evaluated using the H and LE measurements from an eddy covariance (EC) system. Additionally, the computed values of Rn and G were compared with field measurements from a four-way net radiometer and flux plates, respectively. The results indicated that the TSPEB model estimated Rn, H, G, and LE with errors of 7, 14, 7, and 9%, respectively. For the EB components, the values of the mean square error (RMSE) and mean absolute error (MAE) ranged from 13-75 and 11-61 W m−2, respectively. The main uncertainties were associated with errors in the estimation of the soil and canopy sensible heat fluxes.

Assessing Stream Thermal Heterogeneity and Cold-Water Patches from UAV-Based Imagery: A Matter of Classification Methods and Metrics

Understanding stream thermal heterogeneity patterns is crucial to assess and manage river resilience in light of climate change. The dual acquisition of high-resolution thermal infrared (TIR) and red–green–blue-band (RGB) imagery from unmanned aerial vehicles (UAVs) allows for the identification and characterization of thermally differentiated patches (e.g., cold-water patches—CWPs). However, a lack of harmonized CWP classification metrics (patch size and temperature thresholds) makes comparisons across studies almost impossible. Based on an existing dual UAV imagery dataset (River Ovens, Australia), we present a semi-automatic supervised approach to classify key riverscape habitats and associated thermal properties at a pixel-scale accuracy, based on spectral properties. We selected five morphologically representative reaches to (i) illustrate and test our combined classification and thermal heterogeneity assessment method, (ii) assess the changes in CWP numbers and distribution with different metric definitions, and (iii) model how climatic predictions will affect thermal habitat suitability and connectivity of a cold-adapted fish species. Our method was successfully tested, showing mean thermal differences between shaded and sun-exposed fluvial mesohabitats of up to 0.62 °C. CWP metric definitions substantially changed the number and distance between identified CWPs, and they were strongly dependent on reach morphology. Warmer scenarios illustrated a decrease in suitable fish habitats, but reach-scale morphological complexity helped sustain such habitats. Overall, this study demonstrates the importance of method and metric definitions to enable spatio-temporal comparisons between stream thermal heterogeneity studies.

Surveying fumarole sites and hydrothermal alteration by unoccupied aircraft systems (UAS) at the La Fossa cone, Vulcano Island (Italy)

Degassing volcanic systems, expressed by fumaroles, thermal anomalies, and hydrothermal alteration and deposition at the surface provide insights into the underlying structural architecture and the magmatic system. While the fumarole sites are easily identified and investigated, areas of diffuse degassing and associated hydrothermal alteration are barely explored. Here we investigate high-resolution optical and thermal infrared (TIR) data, acquired by unoccupied aircraft systems (UAS) at the La Fossa cone (Vulcano Island) in November 2018. The data provides insights into the structural complexity of degassing sites and associated processes at the surface. Applying the Structure from Motion (SfM) approach, we generate a photomosaic database with a 0.05 m and 0.7 m pixel resolution for the optical and infrared datasets, respectively. A Principal Component Analysis (PCA) was applied to the optical data to detect, define and extract areas of hydrothermal alteration and sulfuric deposition on a pixel base, with a feature detection threshold of up to 25 cm2. By comparing optical data, PCA results and the IR data, we found a broad alteration zone dominated by diffuse degassing surrounding the main fumaroles, which with ~ 60,000 m2 is ten times larger than the area covered by fumaroles and yellowish sulfuric deposits. Spectral and thermal characteristics of this alteration zone suggest a segmentation into at least 13 distinct subregions. Hydrothermal alteration and deposition were analyzed considering their pixel density and spectral signature (RGB) and show the highest pixel density in the center of the fumarole field, accompanied by a systematic color shift. The same region is characterized by a systematic change in azimuths of thermal lineaments and sulfuric clusters from the dominating trend NW-SE by ~90 degrees to NE-SW. We conjecture this to be controlled by a permeability contrast due to a subsurface structure or crater intersection, facilitating a more direct gas ascent in the center of the fumarole field. We provide a precise and complete database for the state and extent of the La Fossa fumarole field, which can be used for comparative monitoring of spatio-temporal changes within the hydrothermal system at the surface.

Assessment of the clumped model to estimate olive orchard evapotranspiration using meteorological data and UAV-based thermal infrared imagery

A study was performed to evaluate the clumped model in estimating olive orchard evapotranspiration (ETa) using meteorological data and high-resolution thermal infrared (TIR) imagery obtained from a camera onboard an unmanned aerial vehicle (UAV). An experimental site was established within a superintensive drip-irrigated olive (cv. Arbequina) orchard located in the Pencahue Valley (35.49° S, 71.73°W, and 85 m above sea level), Maule Region, Chile. UAV-based TIR images were collected to obtain the land surface temperature at a very high resolution on 12 clear-sky days during the 2015–2016 growing season. Measurements of the latent heat flux (LE) obtained from an eddy covariance (EC) system were analyzed to assess the clumped model. In addition, submodels to calculate the net radiation (Rn) and soil heat flux (G) were evaluated using a four-way net radiometer and soil heat flux plates with soil thermocouples, respectively. Comparisons indicated that the root mean square error (RMSE) and mean absolute error (MAE) values for LE were 37 and 27 W m−2, respectively, while those for ETa were 0.44 and 0.35 mm day−1, respectively. Both UAV-based values for Rn and G were estimated with RMSE < 31 W m−2 and MAE < 18 W m−2. The relative RMSE (rRMSE) values were 26% for LE, 24% for ETa, 5% for Rn, and 11% for G. The results suggest that the clumped model based on UAV-based TIR imagery and meteorological data could produce maps with a very high resolution to estimate the intraorchard spatial variability in olive orchard water requirements.

Research on Wide-Range and High-Resolution Thermal Infrared Remote Sensing Imaging Technology

Research on Wide-Range and High-Resolution Thermal Infrared Remote Sensing Imaging Technology

Unmanned Aerial Vehicle (UAV)-Based Thermal Infra-Red (TIR) and Optical Imagery Reveals Multi-Spatial Scale Controls of Cold-Water Areas Over a Groundwater-Dominated Riverscape

The forecast of warmer weather, and reduced precipitation and streamflow under climate change makes freshwater biota particularly vulnerable to being exposed to temperature extremes. Given the importance of temperature to regulate vital physiological processes, the availability of discrete cold-water patches (CWPs) in rivers to act as potential thermal refugia is critical to support freshwater ecosystem function. Being able to predict their spatial distribution at riverscape scales is the first step to understanding the capacity to maintain thermal refuges and to inform future river management strategies. Novel Unmanned Aerial Vehicle (UAV)-based Thermal Infra-Red (TIR) imagery technologies provide an opportunity to assess riverscape stream temperature. On the example of a 50 km linear length of the groundwater-dominated Upper Ovens River (Australia), this study presents a methodology addressing critical challenges in UAV-based TIR and optical data acquisition, processing, and interpretation. Our methodological approach generated 49 georeferenced high-resolution TIR and optical orthomosaicked imagery sets. The imagery sets allowed us to identify river-length longitudinal patterns of temperature and to detect, characterize, and classify 260 CWPs. Both stream and CWPs temperatures increased but presented considerable variability with downstream distance. CWPs were non-uniformly distributed along the riverscape, with emergent hyporheic water types dominating, followed by deep pools, shading, side channels, and tributaries. We found associations between CWPs and key physical controls including land use, riparian vegetation, longitudinal and lateral CWP location, and CWP area size, illustrating processes acting at multiple spatial scales. This study provides a basis for future works on the thermal associations with physical controls over a riverscape, and it highlights the major challenges and limitations of the use of UAV-based TIR and optical imagery to be used in future applications. In conjunction with studies of thermally linked ecological processes, the predictions of CWPs can help prioritize river restoration measures as effective climate adaptation tools.

Monitoring 10-m LST from the Combination MODIS/Sentinel-2, Validation in a High Contrast Semi-Arid Agroecosystem

Downscaling techniques offer a solution to the lack of high-resolution satellite Thermal InfraRed (TIR) data and can bridge the gap until operational TIR missions accomplishing spatio-temporal requirements are available. These techniques are generally based on the Visible Near InfraRed (VNIR)-TIR variable relations at a coarse spatial resolution, and the assumption that the relationship between spectral bands is independent of the spatial resolution. In this work, we adopted a previous downscaling method and introduced some adjustments to the original formulation to improve the model performance. Maps of Land Surface Temperature (LST) with 10-m spatial resolution were obtained as output from the combination of MODIS/Sentinel-2 images. An experiment was conducted in an agricultural area located in the Barrax test site, Spain (39°03′35″ N, 2°06′ W), for the summer of 2018. Ground measurements of LST transects collocated with the MODIS overpasses were used for a robust local validation of the downscaling approach. Data from 6 different dates were available, covering a variety of croplands and surface conditions, with LST values ranging 300–325 K. Differences within ±4.0 K were observed between measured and modeled temperatures, with an average estimation error of ±2.2 K and a systematic deviation of 0.2 K for the full ground dataset. A further cross-validation of the disaggregated 10-m LST products was conducted using an additional set of Landsat-7/ETM+ images. A similar uncertainty of ±2.0 K was obtained as an average. These results are encouraging for the adaptation of this methodology to the tandem Sentinel-3/Sentinel-2, and are promising since the 10-m pixel size, together with the 3–5 days revisit frequency of Sentinel-2 satellites can fulfill the LST input requirements of the surface energy balance methods for a variety of hydrological, climatological or agricultural applications. However, certain limitations to capture the variability of extreme LST, or in recently sprinkler irrigated fields, claim the necessity to explore the implementation of soil moisture or vegetation indices sensitive to soil water content as inputs in the downscaling approach. The ground LST dataset introduced in this paper will be of great value for further refinements and assessments.

Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard

Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field-scale using a data assimilation strategy.

High-resolution Thermal Infrared Imaging of 3200 Phaethon

Abstract We present thermal infrared observations of the active asteroid (and Geminid meteoroid stream parent) 3200 Phaethon using the Very Large Telescope. The images, at 10.7 μm wavelength, were taken with Phaethon at its closest approach to Earth (separation of 0.07 au) in 2017 December, at a linear resolution of about 14 km. We probe the Hill sphere (of radius ∼66 km) for trapped dust and macroscopic bodies, finding neither, and we set limits to the presence of unbound dust. The derived limits to the optical depth of dust near Phaethon depend somewhat on the assumed geometry, but are of an order of 10−5. The upper limit to the rate of loss of mass in dust is ≲14 kg s−1. This is ∼50 times smaller than the rate needed to sustain the Geminid meteoroid stream in steady state. The observations thus show that the production of the Geminids does not proceed in a steady state.

Using Very High Resolution Thermal Infrared Imagery for More Accurate Determination of the Impact of Land Cover Differences on Evapotranspiration in an Irrigated Agricultural Area

Land cover has a strong effect on the evapotranspiration (ET) and the hydrologic cycle. Urbanization alters the land cover affecting the surface energy balance and ET by, for example, urban encroachment in agricultural areas. This study investigates the potential utility of high resolution ET in determining more accurately the impact of land cover on water use for an agricultural area. The approach was to apply the physically based two-source energy balance (TSEB) model to very high resolution (~8 m) aircraft thermal data and compare the ET pattern and distribution to TSEB output using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data acquired on 2 August 2012. Modeled flux components were validated using measurements collected from a network of 16 eddy covariance (EC) towers at the study site. The modeled ET using the aircraft data agreed satisfactorily with the flux tower measurements and had better performance than the TSEB model applied to the ASTER data. The percent errors between ET closed by the Bowen ratio (BR) and residual (RE) approaches were 3 and 1%, respectively. It is shown that the high resolution aircraft ET can more accurately determine the change in ET magnitude by having pure pixels of the main land cover types, namely urban, agriculture, and natural vegetation. As a result, the ET histogram exhibits a significant bi-modal distribution which can be used to accurately distinguish the impact on ET from urban versus agricultural land cover areas and potentially monitor the effect on ET over a landscape due to small changes in land cover. At the coarser 90 m resolution of ASTER, the TSEB ET estimates are more often a combination of urban and agricultural land cover ET near the urban-agriculture land cover boundaries. As a result, the bi-modal distribution in ET is almost nonexistent. This study demonstrates the potential utility of high resolution ET mapping for more accurately determining the magnitude of the ET differences between cropland and urban land cover. It also suggests that, with high resolution thermal imagery, TSEB is a potential tool for monitoring the impact on ET due to relatively small changes in land cover as a result of urban expansion. Such a tool would be useful for watershed management.

Study of Temperature Heterogeneities at Sub-Kilometric Scales and Influence on Surface–Atmosphere Energy Interactions

The retrieval of land surface temperature (LST) from remote sensing techniques has been studied and validated during the past 40 years, leading to important improvements. Accurate LST values are currently obtained through measurements using medium resolution thermal infrared (TIR) sensors. However, the most recent review reports demonstrated that the future TIR LST products need to obtain reliable temperature values at a high spatial resolution (100 m or higher) to study temperature variations between different elements in a heterogeneous kilometric area. The launch of high-resolution TIR sensors in the near future requires studies of the temporal evolution and spatial heterogeneities of the elements in a mixed region. The present study analyzes the LST in a sub-kilometric highly heterogeneous area, combining the use of LST products from high-resolution TIR orbiting sensors with the LST maps created from a TIR camera onboard an unmanned aerial vehicle (UAV). The aim is to estimate the LST variability in a heterogeneous area containing different surfaces (roads, buildings, and grass), observed from different TIR sensors at different spatial resolutions, covering from the meter to the kilometer scales. Several results showed that variations in the LST up to 18 °C were identified with the UAV-TIR camera, and significant differences were also present in the LST products obtained from simultaneous overpasses of high-resolution satellite TIR sensors. A second objective of the study, due to the availability of the high-resolution LST fields, was to explore the thermal advection between different elements and determine if it correlates with the surface energy budget in the same area, thus indicating that this process is of importance for heterogeneous terrains at these scales. This paper also highlights the relevance of the UAV-TIR camera flight for future studies since it is not commonly used in TIR remote sensing but has substantial potential advantages.

Analysis of Airborne Optical and Thermal Imagery for Detection of Water Stress Symptoms

High-resolution airborne thermal infrared (TIR) together with sun-induced fluorescence (SIF) and hyperspectral optical images (visible, near- and shortwave infrared; VNIR/SWIR) were jointly acquired over an experimental site. The objective of this study was to evaluate the potential of these state-of-the-art remote sensing techniques for detecting symptoms similar to those occurring during water stress (hereinafter referred to as ‘water stress symptoms’) at airborne level. Flights with two camera systems (Telops Hyper-Cam LW, Specim HyPlant) took place during 11th and 12th June 2014 in Latisana, Italy over a commercial grass (Festuca arundinacea and Poa pratense) farm with plots that were treated with an anti-transpirant agent (Vapor Gard®; VG) and a highly reflective powder (kaolin; KA). Both agents affect energy balance of the vegetation by reducing transpiration and thus reducing latent heat dissipation (VG) and by increasing albedo, i.e., decreasing energy absorption (KA). Concurrent in situ meteorological data from an on-site weather station, surface temperature and chamber flux measurements were obtained. Image data were processed to orthorectified maps of TIR indices (surface temperature (Ts), Crop Water Stress Index (CWSI)), SIF indices (F687, F780) and VNIR/SWIR indices (photochemical reflectance index (PRI), normalised difference vegetation index (NDVI), moisture stress index (MSI), etc.). A linear mixed effects model that respects the nested structure of the experimental setup was employed to analyse treatment effects on the remote sensing parameters. Airborne Ts were in good agreement (∆T &lt; 0.35 K) compared to in situ Ts measurements. Maps and boxplots of TIR-based indices show diurnal changes: Ts was lowest in the early morning, increased by 6 K up to late morning as a consequence of increasing net radiation and air temperature (Tair) and remained stable towards noon due to the compensatory cooling effect of increased plant transpiration; this was also confirmed by the chamber measurements. In the early morning, VG treated plots revealed significantly higher Ts compared to control (CR) plots (p = 0.01), while SIF indices showed no significant difference (p = 1.00) at any of the overpasses. A comparative assessment of the spectral domains regarding their capabilities for water stress detection was limited due to: (i) synchronously overpasses of the two airborne sensors were not feasible, and (ii) instead of a real water stress occurrence only water stress symptoms were simulated by the chemical agents. Nevertheless, the results of the study show that the polymer di-1-p-menthene had an anti-transpiring effect on the plant while photosynthetic efficiency of light reactions remained unaffected. VNIR/SWIR indices as well as SIF indices were highly sensitive to KA, because of an overall increase in spectral reflectance and thus a reduced absorbed energy. On the contrary, the TIR domain was highly sensitive to subtle changes in the temperature regime as induced by VG and KA, whereas VNIR/SWIR and SIF domain were less affected by VG treatment. The benefit of a multi-sensor approach is not only to provide useful information about actual plant status but also on the causes of biophysical, physiological and photochemical changes.

Spatiotemporal dynamics of leaf transpiration quantified with time-series thermal imaging

Accurately capturing the spatiotemporal dynamics of transpiration from sub-leaf to ecosystem scales remains a key challenge in eco-physiology and hydrology as typical methods face a trade-off between spatial coverage and temporal resolution. Here, we developed a new scalable, semi-automated method to produce highly precise estimates of water and energy fluxes and applied it to single leaves. High-resolution thermal infrared (TIR) images and paired colour photographs of excised soybean leaves were captured at 15 s intervals until wilting, automatically registered and segmented, and used as input for transient energy balance models to estimate latent heat flux (transpiration) at a temporal resolution of one second. Three approaches to estimating leaf boundary layer conductance to heat (gHa) and sensible heat flux were compared, two of which did not require the use of any dry or wet reference surface. The accuracy of water loss modeled using average leaf temperature was also compared to models retaining pixel-scale temperature heterogeneity at a spatial resolution of 0.326 mm2. Cumulative leaf water-losses modeled using average leaf temperature closely matched gravimetric measurements (r2 = 0.95) and pixel-scale models identified striking spatiotemporal patterns of water loss at the sub-leaf scale. Different methods of estimating gHa did not significantly alter model results. Use of leaf energy balance models with time series thermal images to quantify transient transpiration fluxes was able to accurately resolve 1-s time-varying leaf water loss in outdoor conditions, did not require any reference surfaces, and also produced data on the characteristic length scales of heterogeneous sub-leaf response. Given the ability to omit reference surfaces and retain accuracy, this approach also has the potential to be scaled-up to quantify energy fluxes in more complex plant canopies.

Characterizing Urban Fabric Properties and Their Thermal Effect Using QuickBird Image and Landsat 8 Thermal Infrared (TIR) Data: The Case of Downtown Shanghai, China

The combined usage of high-resolution satellite images and thermal infrared (TIR) data helps understanding the thermal effect of urban fabric properties and the mechanism of urban heat island (UHI) formation. In this study, three typical urban functional zones (UFZs) of downtown Shanghai were chosen for quantifying the relationship between fine-scale urban fabric properties and their thermal effect. Nine land surfaces and 146 aggregated land parcels extracted from a QuickBird image (dated 14 April 2014) were used to characterize urban fabric properties. The thermal effect was deduced from land surface temperature (LST), intra-UHI intensity, blackbody flux density (BBFD) and blackbody flux (BBF). The net BBF was retrieved from the Landsat 8 TIR band 10 dated 13 August 2013, and 28 May 2014. The products were resampled to fine resolution using a geospatial sharpening approach and further validated. The results show that: (1) On the UFZ level, there is a significant thermal differential among land surfaces. Water, well-vegetated land, high-rises with light color and high-rises with glass curtain walls exhibited relatively low LST, UHI intensity and BBFD. In contrast, mobile homes with light steel roofs, low buildings with bituminous roofs, asphalt roads and composite material pavements showed inverse trends for LST, UHI intensity, and BBFD; (2) It was found that parcel-based per ha net BBF, which offsets the “size-effect” among parcels, is more reasonable and comparable when quantifying excess surface flux emitted by the parcels; (3) When examining the relationship between parcel-level land surfaces and per ha BBF, a partial least squares (PLS) regression model showed that buildings and asphalt roads are major contributors to parcel-based per ha BBF, followed by other impervious surfaces. In contrast, vegetated land and water contribute with a much lower per ha net BBF to parcel warming.

Review of Thermal Infrared Applications and Requirements for Future High-Resolution Sensors

High-resolution thermal infrared (TIR) remote sensing has a wide range of applications. In this paper, we describe the different applications and requirements identified in a literature review and during a consultation meeting with researcher experts in different fields. As a result, more than 30 applications were identified within three different fields: 1) land and solid Earth; 2) health and hazards; and 3) security and surveillance. A complete set of requirements (spatial, temporal, and radiometric resolution, algorithms used, and supporting data, among others) for each application is also provided. The results presented in this paper provide useful information to enhance the importance of high-resolution TIR data for civil applications and may serve as a reference document for future TIR mission concepts.