Search

The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images to surface reflectance. Several calibration methods exist, but the advantages and disadvantages of each are not well understood. We performed an empirical analysis of five different methods for calibrating a 10-band multispectral camera, the MicaSense RedEdge MX Dual Camera System, by comparing multispectral images with spectrometer measurements taken in the field on the same day. Two datasets were collected, one in clear-sky and one in overcast conditions on the same field. We found that the empirical line method (ELM), using multiple radiometric reference targets imaged at mission altitude performed best in terms of bias and RMSE. However, two user-friendly commercial solutions relying on one single grey reference panel were only slightly less accurate and resulted in sufficiently accurate reflectance maps for most applications, particularly in clear-sky conditions. In overcast conditions, the increase in accuracy of more elaborate methods was higher. Incorporating measurements of an integrated downwelling light sensor (DLS2) did not improve the bias nor RMSE, even in overcast conditions. Ultimately, the choice of the calibration method depends on required accuracy, time constraints and flight conditions. When the more accurate ELM is not possible, commercial, user-friendly solutions like the ones offered by Agisoft Metashape and Pix4D can be good enough. [ABSTRACT FROM AUTHOR]

In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene is an essential component of many analysis tasks. The empirical line method (ELM) is the most used technique among remote sensing practitioners due to its reliability and production of accurate reflectance measurements. However, the at-altitude radiance ratio (AARR), a more recently proposed methodology, is attractive as it allows reflectance conversion to be carried out in real time throughout data collection, does not require calibrated samples of pre-measured reflectance to be placed in scene, and can account for changes in illumination conditions. The benefits of AARR can substantially reduce the level of effort required for collection setup and subsequent data analysis, and provide a means for large-scale automation of remote sensing data collection, even in atypical flight conditions. In this study, an onboard, downwelling irradiance spectrometer integrated onto a small unmanned aircraft system (sUAS) is utilized to characterize the performance of AARR-generated reflectance from hyperspectral radiance data under a variety of challenging illumination conditions. The observed error introduced by AARR is often on par with ELM and acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. Additionally, a number of radiometric and atmospheric corrections are proposed that could increase the accuracy of the method in future trials, warranting further research. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy's structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun–canopy–sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red ( F 687 ) and far-red ( F 760 ) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m − 2 sr − 1 nm − 1 . The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m − 2 sr − 1 nm − 1 , whereas vegetation targets had a mean F 687 of 1.13 and F 760 of 1.96 mW m − 2 sr − 1 nm − 1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F 687 and F 760 ; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants' photosynthetic capacity. [ABSTRACT FROM AUTHOR]

This study evaluated the use of low-cost materials for radiometric calibration of multispectral images. Four materials were tested: plywood panels painted with matte paint (M1); plywood panels covered with synthetic nappa leather (M2); Ethylene Vinyl Acetate (EVA) panels (M3), and plywood panels covered with Polyvinyl chloride (PVC) canvas (M4). The useful life of all materials and the errors associated with the calibration was determined. The M1 and M2 panels presented the lowest errors (RMSE), whereas the M4 panels showed the highest errors. Finally, the M3 panels showed the lowest resistance to use, whereas the M1 panels showed the greatest durability. [ABSTRACT FROM AUTHOR]

The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images to surface reflectance. Several calibration methods exist, but the advantages and disadvantages of each are not well understood. We performed an empirical analysis of five different methods for calibrating a 10-band multispectral camera, the MicaSense RedEdge MX Dual Camera System, by comparing multispectral images with spectrometer measurements taken in the field on the same day. Two datasets were collected, one in clear-sky and one in overcast conditions on the same field. We found that the empirical line method (ELM), using multiple radiometric reference targets imaged at mission altitude performed best in terms of bias and RMSE. However, two user-friendly commercial solutions relying on one single grey reference panel were only slightly less accurate and resulted in sufficiently accurate reflectance maps for most applications, particularly in clear-sky conditions. In overcast conditions, the increase in accuracy of more elaborate methods was higher. Incorporating measurements of an integrated downwelling light sensor (DLS2) did not improve the bias nor RMSE, even in overcast conditions. Ultimately, the choice of the calibration method depends on required accuracy, time constraints and flight conditions. When the more accurate ELM is not possible, commercial, user-friendly solutions like the ones offered by Agisoft Metashape and Pix4D can be good enough.

Unmanned aerial vehicles (UAVs) present themselves as an alternative to overcome the limitations of satellite sensors in monitoring agricultural crops, motivating many studies with UAVs. They can carry sensors, which need studies for better understanding. The present study aimed to vicariously calibrate a Red-Green-Near infrared (RG-NIR) low-cost sensor on board a UAV, and to develop predictive models of biophysical parameters for a maize crop. To achieve this purpose, 15 sets of images were captured over 61 days after emergence (DAE) of the maize crop plantation. Each set of images was mosaicked and had their digital numbers (DN) converted to reflectance. After calibration, normalized difference vegetation index (NDVI) and cumulative NDVI (cNDVI) were calculated to serve as an independent variable in the models for estimating crop parameters. In the field, 54 plants were collected and evaluated for height, leaf area and dry biomass. It was observed that the NIR band had an influence on the red band, but this influence was attenuated with the empirical line calibration. NDVI was able to detect seasonal and spatial variations in maize. The NDVI model obtained on the collection day to estimate the total dry above ground biomass had better results, generating RMSE of 68.68 g m−2 and R2 of 0.81, in comparison with cNDVI. For productivity, the result was satisfactory with cNDVI, showing RMSE of 134.00 g m−2 and R2 of 0.63. Calibration of the sensor was shown to be important to attenuate influence between bands. [ABSTRACT FROM AUTHOR]

Recently, remote sensing based on the unmanned aerial vehicle (UAV) has become a novel remote sensing technology for researching the characteristics of the earth and the properties of the objects near the earth's surface. Accurate reflectance products are fundamental for optical remote sensing applications. Although the application of remote sensing based on UAV is widely applied, the quality, standards, strategies, and procedures of remote sensing products are faced challenges. To obtain convincing reflectance products, the methods, processes, and strategies obtaining the reflectance products for UAV-remote sensing are validated, compared, and improved. Meanwhile, three methods (ELM, MIR, and ARTM) are exploited to obtain spectral reflectance for hyperspectral images based on UAV in this paper. The comparison with the spectral reflectance of verified targets measured in the field indicates that the spectral reflectance of MIR has higher accuracy than others, with the average spectral absolute error (ASAE) of ±2.5%. By contrast, the ASAE of ELM and ARTM is ±7.0% and ±9.6%, respectively. Besides, based on the principle of radiative transfer, the radiation factors including incident radiation, background radiance, and environmental radiance are analyzed for improving the performance of the three methods. By recalibrating the hyperspectral sensor, the average absolute error of MIR is reduced to ±1.8%. By optimizing the parameters, the performance of ELM and ARTM is improved significantly, and the average absolute error of the two methods is reduced to ±1.7% and ±4.4%, respectively.

Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy’s structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun–canopy–sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red (F687) and far-red (F760) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m−2 sr−1 nm−1. The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m−2 sr−1 nm−1, whereas vegetation targets had a mean F687 of 1.13 and F760 of 1.96 mW m−2 sr−1 nm−1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F687 and F760; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants’ photosynthetic capacity.

Spatial quantification of soil attributes is needed to assess and monitor soil resources. Our objective was to map soil total carbon (TC) over 580 ha bare soils, in Djerid arid area (SW Tunisia). One hundred and forty-four soil samples were collected in nodes of 200 m square grid and their spectra acquired in the laboratory at 400–2500 nm have served to radiometrically correct ASTER image using the empirical line method. TC was predicted using partial least squares regression-kriging model, based on the 144 spectra extracted from the nine visible-near infrared ASTER bands. Residual interpolation has improved prediction efficiency. Indeed, PLSR-kriging achieved higher R2 and lower RMSE than PLSR, respectively: 0.78 against 0.53 and 0.52% against 0.16%. Furthermore, spatial distribution of these quantifications has a physical significance. Our results offer relevant method for soil attribute mapping on large scale. [ABSTRACT FROM AUTHOR]

The present investigation aims at providing a reliable evaluation of the geometric and radiometric accuracy of the airborne Daedalus-Coastal Zone Color Scanner (CZCS), through cal/val techniques. To this aim, on November 2014, we accomplished a remote sensing campaign over the Peligna Valley (Italy). Together with the personnel of the III Nucleo Aereo of the Italian Coast Guard, based at the Pescara Airport, we deployed the Daedalus AA1268EM1 CZCS scanner on board the fixed-wing aircraft type ATR42MP and a ASD FieldSpec spectroradiometer for a simultaneous field survey. We used vicarious calibration and secondary non-parametric geometric correction to achieve absolute atmospheric correction and geometric calibration, respectively. Although the validation and calibration targets used in this study were similar in nature, correlation coefficients of the prediction equations between at-sensor radiance and ground reflectance were > 0.90 for each of the 10 CZCS wavebands and the independent error assessment demonstrate that the empirical line method can be applied to correct CZCS imagery with satisfactory results.

The consolidation of unmanned aerial vehicle (UAV) photogrammetric techniques for campaigns with high and medium observation scales has triggered the development of new application areas. Most of these vehicles are equipped with common visible-band sensors capable of mapping areas of interest at various spatial resolutions. It is often necessary to identify vegetated areas for masking purposes during the postprocessing phase, excluding them for the digital elevation models (DEMs) generation or change detection purposes. However, vegetation can be extracted using sensors capable of capturing the near-infrared part of the spectrum, which cannot be recorded by visible (RGB) cameras. In this study, after reviewing different visible-band vegetation indices in various environments using different UAV technology, the influence of the spatial resolution of orthomosaics generated by photogrammetric processes in the vegetation extraction was examined. The triangular greenness index (TGI) index provided a high level of separability between vegetation and nonvegetation areas for all case studies in any spatial resolution. The efficiency of the indices remained fundamentally linked to the context of the scenario under investigation, and the correlation between spatial resolution and index incisiveness was found to be more complex than might be trivially assumed.

Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing can be quite challenging. Usually, healthy vegetation can be extracted at the near-infrared part of the spectrum (approximately between 760–900 nm), which is not captured by the visible (RGB) cameras. In this study, we explore several visible (RGB) vegetation indices in different environments using various UAV sensors and cameras to validate their performance. For this purposes, openly licensed unmanned aerial vehicle (UAV) imagery has been downloaded “as is” and analyzed. The overall results are presented in the study. As it was found, the green leaf index (GLI) was able to provide the optimum results for all case studies.

The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for the reduction of atmospheric contributions in unmanned aerial systems (UAS) imagery and for the production of scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions were used to develop and test an ELM radiometric calibration framework designed for the reduction of atmospheric contributions from UAS imagery in studies with limited site accessibility or data acquisition time constraints. The ELM radiometric calibration framework was developed specifically for water-based operations and the efficacy of using generalized study area calibration equations averaged across variable illumination and atmospheric conditions was assessed. The framework was effective in reducing atmospheric and other external contributions in UAS imagery. Unique to the proposed radiometric calibration framework is the radiance-to-reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equation development. While image-by-image calibrations are still preferred for high accuracy applications, this paper provides an ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in UAS imagery in situations with limited site accessibility or data acquisition constraints.

The present investigation aims at providing a reliable evaluation of the geometric and radiometric accuracy of the airborne Daedalus-Coastal Zone Color Scanner (CZCS), through cal/val techniques. To this aim, on November 2014, we accomplished a remote sensing campaign over the Peligna Valley (Italy). Together with the personnel of the III Nucleo Aereo of the Italian Coast Guard, based at the Pescara Airport, we deployed the Daedalus AA1268EM1 CZCS scanner on board the fixed-wing aircraft type ATR42MP and a ASD FieldSpec spectroradiometer for a simultaneous field survey. We used vicarious calibration and secondary non-parametric geometric correction to achieve absolute atmospheric correction and geometric calibration, respectively. Although the validation and calibration targets used in this study were similar in nature, correlation coefficients of the prediction equations between at-sensor radiance and ground reflectance were > 0.90 for each of the 10 CZCS wavebands and the independent error assessment demonstrate that the empirical line method can be applied to correct CZCS imagery with satisfactory results. [ABSTRACT FROM AUTHOR]

Analysis of visible remote sensing data research requires removing atmospheric effects by conversion from radiance to at-surface reflectance. This conversion can be achieved through theoretical radiative transfer models, which yield good results when well-constrained by field observations, although these measurements are often lacking. Additionally, radiative transfer models often perform poorly in marine or lacustrine settings or when complex air masses with variable aerosols are present. The empirical line method (ELM) measures reference targets of known reflectance in the scene. ELM methods require minimal environmental observations and are conceptually simple. However, calibration coefficients are unique to the image containing the reflectance reference. Here we compare the conversion of hyperspectral radiance observations obtained with the NASA Glenn Research Center Hyperspectral Imager to at-surface reflectance factor using two reflectance reference targets. The first target employs spherical convex mirrors, deployed on the water surface to reflect ambient direct solar and hemispherical sky irradiance to the sensor. We calculate the mirror gain using near concurrent at-sensor reflectance, integrated mirror radiance, and in situ water reflectance. The second target is the Lambertian-like blacktop surface at Maumee Bay State Park, Oregon, OH, where reflectance was measured concurrently by a downward looking, spectroradiometer on the ground, the aerial hyperspectral imager and an upward looking spectroradiometer on the aircraft. These methods allows us to produce an independently calibrated at-surface water reflectance spectrum, when atmospheric conditions are consistent. We compare the mirror and blacktop-corrected spectra to the in situ water reflectance, and find good agreement between methods. The blacktop method can be applied to all scenes, while the mirror calibration method, based on direct observation of the light illuminating the scene validates the results. The two methods are complementary and a powerful evaluation of the quality of atmospheric correction over extended areas. We decompose the resulting spectra using varimax-rotated, principal component analysis, yielding information about the underlying color producing agents that contribute to the observed reflectance factor scene, identifying several spectrally and spatially distinct mixtures of algae, cyanobacteria, illite, haematite, and goethite. These results have implications for future hyperspectral remote sensing missions, such as PACE, HyspIRI, and GeoCAPE.

An increasing demand for full spatio-temporal coverage of soil information drives the growing use of soil spectroscopy. Soil spectroscopy application performed under laboratory conditions or in-field studies in semi-arid areas have shown promising results. However, when acquiring data in temperate zones, limitations by vegetation-free coverage, variation in soil moisture and management are driving coherent spatio-temporal data collection. This study explores the use of multi-temporal imaging spectroscopy data to increase the total mapping area of bare soils in a heterogeneous agricultural landscape. Spectrally and spatially high-resolution data from the Airborne Prism Experiment (APEX) were collected in September 2013, April 2014 and April 2015. Bare soils in all acquisitions were identified. To eliminate short-term differences in soil moisture and soil surface roughness, the empirical line method was used to calibrate the reflectance values of the singular images (2013 and 2015) towards the singular image with most bare soil pixels (2014). Difference indicators show that the calibration was successful (decrease in root mean square difference and angle difference, increase in R2 and gain and offset close to one and zero). Finally, the multi-temporal composite image contained more than double the amount of bare soil pixels as compared to a singular acquisition. Summary statistics show that reflectance values of the multi-temporal composite approximate the single image data of 2014 (mean and standard deviation of 2014: 24.2±8.9 vs. 24.0±9.5 for the multi-temporal composite of 2013, 2014 and 2015). This indicates that global differences in soil moisture and land management have been corrected for. As a result, an improved spatial representation of soil parameters can be retrieved from the composite data. Spatial distribution of the correction factors and analysis of the spatial variability of all images, however, indicate that non-linear, short-term differences like variation in soil moisture and land management largely influence the result of the multi-temporal composite. Quantification and attribution of those factors will be required in the future to allow correcting for them. [ABSTRACT FROM AUTHOR]

The Empirical Line Method (ELM) enables the calibration of multi- and hyper-airborne/satellite image converting DN or radiance to reflectance values performed by using at ground data. High quality outcome can be reached with the selection of appropriate Pseudo-Invariant Targets (PIT). In this paper, spectral variability of “usual” (asphalt and concrete) and “unusual” (calcareous gravel, basaltic paving, concrete bricks, tartan paving and artificial turf) PITs is retrieved for ELM application. Such PITs are used to calibrate the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) airborne sensor in 12 different Runs. Firstly, processing of field spectral data enables the evaluation of pseudo-invariance of targets by studying their spectral changes in space and in time. Finally, these surfaces are used as Ground Calibration (GCT) and Validation Targets (GVT) in ELM. High calibration accuracy values are observed in Visible (VIS) range (98.9%) while a general decrease of accuracy in Near-InfraRed (NIR) (96.6%) and Middle-InfraRed (SWIR) (88.1%) are reached. Outcomes show that “usual” surfaces as asphalt and concrete and “unusual” surfaces such as tartan can be successfully used for MIVIS image calibration. [ABSTRACT FROM AUTHOR]

An increasing demand for full spatio-temporal coverage of soil information drives the growing use of soil spectroscopy. Soil spectroscopy application performed under laboratory conditions or in-field studies in semi-arid areas have shown promising results. However, when acquiring data in temperate zones, limitations by vegetation-free coverage, variation in soil moisture and management are driving coherent spatio-temporal data collection. This study explores the use of multi-temporal imaging spectroscopy data to increase the total mapping area of bare soils in a heterogeneous agricultural landscape. Spectrally and spatially high-resolution data from the Airborne Prism Experiment (APEX) were collected in September 2013, April 2014 and April 2015. Bare soils in all acquisitions were identified. To eliminate short-term differences in soil moisture and soil surface roughness, the empirical line method was used to calibrate the reflectance values of the singular images (2013 and 2015) towards the singular image with most bare soil pixels (2014). Difference indicators show that the calibration was successful (decrease in root mean square difference and angle difference, increase in R2 and gain and offset close to one and zero). Finally, the multi-temporal composite image contained more than double the amount of bare soil pixels as compared to a singular acquisition. Summary statistics show that reflectance values of the multi-temporal composite approximate the single image data of 2014 (mean and standard deviation of 2014: 24.2 ± 8.9 vs. 24.0 ± 9.5 for the multi-temporal composite of 2013, 2014 and 2015). This indicates that global differences in soil moisture and land management have been corrected for. As a result, an improved spatial representation of soil parameters can be retrieved from the composite data. Spatial distribution of the correction factors and analysis of the spatial variability of all images, however, indicate that non-linear, short-term differences like variation in soil moisture and land management largely influence the result of the multi-temporal composite. Quantification and attribution of those factors will be required in the future to allow correcting for them.

In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene is an essential component of many analysis tasks. The empirical line method (ELM) is the most used technique among remote sensing practitioners due to its reliability and production of accurate reflectance measurements. However, the at-altitude radiance ratio (AARR), a more recently proposed methodology, is attractive as it allows reflectance conversion to be carried out in real time throughout data collection, does not require calibrated samples of pre-measured reflectance to be placed in scene, and can account for changes in illumination conditions. The benefits of AARR can substantially reduce the level of effort required for collection setup and subsequent data analysis, and provide a means for large-scale automation of remote sensing data collection, even in atypical flight conditions. In this study, an onboard, downwelling irradiance spectrometer integrated onto a small unmanned aircraft system (sUAS) is utilized to characterize the performance of AARR-generated reflectance from hyperspectral radiance data under a variety of challenging illumination conditions. The observed error introduced by AARR is often on par with ELM and acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. Additionally, a number of radiometric and atmospheric corrections are proposed that could increase the accuracy of the method in future trials, warranting further research.

Abstract: Because atmospheric effects can have a significant impact on the data obtained from multi-spectral satellite remote sensing, it is frequently necessary to make corrections before any other image processing can be started. This paper describes a robust and relatively simple atmospheric correction method that uses pseudo-invariant targets (PITs) in conjunction with the empirical line method. The method is based on the selection of a number of suitable generic PITs, on the basis that they are large, distinctive in shape, and occur in many geographical areas. Whereas the multi-temporal normalization method corrects all images to a selected reference image, in this method images are simultaneously corrected using targets with a range of estimated surface reflectance values. The paper describes some applications of the method for a range of environmental studies involving water quality and air pollution monitoring, and mapping land-cover changes. [Copyright &y& Elsevier]

Multi- and hyperspectral cameras on drones can be valuable tools in environmental monitoring. A significant shortcoming complicating their usage in quantitative remote sensing applications is insufficient robust radiometric calibration methods. In a direct reflectance transformation method, the drone is equipped with a camera and an irradiance sensor, allowing transformation of image pixel values to reflectance factors without ground reference data. This method requires the sensors to be calibrated with higher accuracy than what is usually required by the empirical line method (ELM), but consequently it offers benefits in robustness, ease of operation, and ability to be used on Beyond-Visual Line of Sight flights. The objective of this study was to develop and assess a drone-based workflow for direct reflectance transformation and implement it on our hyperspectral remote sensing system. A novel atmospheric correction method is also introduced, using two reference panels, but, unlike in the ELM, the correction is not directly affected by changes in the illumination. The sensor system consists of a hyperspectral camera (Rikola HSI, by Senop) and an onboard irradiance spectrometer (FGI AIRS), which were both given thorough radiometric calibrations. In laboratory tests and in a flight experiment, the FGI AIRS tilt-corrected irradiances had accuracy better than 1.9% at solar zenith angles up to 70°. The system's low-altitude reflectance factor accuracy was assessed in a flight experiment using reflectance reference panels, where the normalized root mean square errors (NRMSE) were less than ±2% for the light panels (25% and 50%) and less than ±4% for the dark panels (5% and 10%). In the high-altitude images, taken at 100–150 m altitude, the NRMSEs without atmospheric correction were within 1.4%–8.7% for VIS bands and 2.0%–18.5% for NIR bands. Significant atmospheric effects appeared already at 50 m flight altitude. The proposed atmospheric correction was found to be practical and it decreased the high-altitude NRMSEs to 1.3%–2.6% for VIS bands and to 2.3%–5.3% for NIR bands. Overall, the workflow was found to be efficient and to provide similar accuracies as the ELM, but providing operational advantages in such challenging scenarios as in forest monitoring, large-scale autonomous mapping tasks, and real-time applications. Tests in varying illumination conditions showed that the reflectance factors of the gravel and vegetation targets varied up to 8% between sunny and cloudy conditions due to reflectance anisotropy effects, while the direct reflectance workflow had better accuracy. This suggests that the varying illumination conditions have to be further accounted for in drone-based in quantitative remote sensing applications. [Display omitted] • We propose a direct reflectance workflow for drone hyperspectral imaging. • A novel atmospheric correction method for direct reflectance workflows • Radiometric calibration of the hyperspectral camera and the irradiance sensor • The tilt-corrected onboard irradiances had accuracy better than 1.9%. • The reflectance factors had a max error of 2.6% in VIS bands and 5.3% in NIR bands. [ABSTRACT FROM AUTHOR]

The consolidation of unmanned aerial vehicle (UAV) photogrammetric techniques for campaigns with high and medium observation scales has triggered the development of new application areas. Most of these vehicles are equipped with common visible-band sensors capable of mapping areas of interest at various spatial resolutions. It is often necessary to identify vegetated areas for masking purposes during the postprocessing phase, excluding them for the digital elevation models (DEMs) generation or change detection purposes. However, vegetation can be extracted using sensors capable of capturing the near-infrared part of the spectrum, which cannot be recorded by visible (RGB) cameras. In this study, after reviewing different visible-band vegetation indices in various environments using different UAV technology, the influence of the spatial resolution of orthomosaics generated by photogrammetric processes in the vegetation extraction was examined. The triangular greenness index (TGI) index provided a high level of separability between vegetation and nonvegetation areas for all case studies in any spatial resolution. The efficiency of the indices remained fundamentally linked to the context of the scenario under investigation, and the correlation between spatial resolution and index incisiveness was found to be more complex than might be trivially assumed. [ABSTRACT FROM AUTHOR]

Climate change and the urban heat island effect pose significant health, energy and economic risks. Urban heat mitigation research promotes the use of reflective surfaces to counteract the negative effects of extreme heat. Surface reflectance is a key parameter for understanding, modeling and modifying the urban surface energy balance to cool cities and improve outdoor thermal comfort. The majority of urban surface studies address the impacts of horizontal surface properties at the material and precinct scales. However, there is a gap in research focusing on individual building facades. This paper analyses the results of a novel application of the empirical line method to calibrate a terrestrial low-cost multispectral sensor to recover spectral reflectance from urban vertical surfaces. The high correlation between measured and predicted mean reflectance values per waveband (0.940 (Red) < rs > 0.967 (NIR)) confirmed a near-perfect positive agreement between pairs of samples of ranked scores. The measured and predicted distributions exhibited no statistically significant difference at the 95% confidence level. Accuracy measures indicate absolute errors within previously reported limits and support the utility of a single-target spectral reflectance recovery method for urban heat mitigation studies focusing on individual building facades. [ABSTRACT FROM AUTHOR]