Hyperspectral Imaging Spectrometer Research Articles

Multi-Channel Hyperspectral Imaging Spectrometer Design for Ultraviolet Detection in the Atmosphere of Venus

The spectroscopic detection of SO2 and unknown UV absorber substance in the H2SO4 cloud layer of Venus’ atmosphere is currently a focal point in the study of the habitability of Venusian atmospheric clouds. This paper addresses the simultaneous detection requirements of multiple substances in the ultraviolet range of Venus’ atmosphere and proposes a multi-channel hyperspectral imaging system design using pupil separation prisms and grating multilevel spectra. The system achieves a multi-channel design by splitting the entrance pupil of the telescope using prisms. Spectra from different channels are diffracted to the same detector through different orders of the grating. The system features a single spectrometer and detector, enabling simultaneous detection of spectra from different channels. It also boasts advantages such as compact size, ultra-high spectral resolution, and simultaneous multi-channel detection. The system design results indicate that within the working spectral range of three channels, the spectral resolution is better than 0.15 nm, surpassing previous in-orbit or current in-orbit planetary atmospheric detection spectrometers. With a Nyquist frequency of 56 lp/mm, the full-field MTF exceeds 0.7. The system’s smile is less than 0.05 μm, and the keystone is less than 0.04 μm, meeting the requirements for imaging quality.

Analysis of the impact of temperature on the spectral shift in ultraviolet hyperspectral imaging spectrometers.

The imaging spectrometer's high performance in practical applications may be compromised by environmental factors, particularly temperature variations, posing a challenge to its stability. Temperature fluctuations can induce spectral shift, directly impacting the accuracy of spectral measurements, subsequently influencing the precision of radiometric measurements. To address this issue, this study investigates a dual-channel UV imaging spectrometer. This instrument boasts a wavelength calibration accuracy of 0.01 nm. This paper conducts an in-depth analysis of the various mechanisms through which temperature changes influence the spectral line offset in the imaging spectrometer, integrating actual orbital temperature data to discuss the instrument's temperature load settings. The impact of temperature on spectral shift is examined using finite element analysis and optical design software. Estimations of spectral shift were made based on temperature variations. Simulation results indicated that the maximum deviation of spectral shift is estimated at 0.018 nm under a temperature condition of 16 ± 1°C. Under a more controlled orbital temperature condition (16 ± 0.3°C), the maximum deviation of spectral shift decreased to 0.01 nm. Experimental data revealed that at 16 ± 1°C, the maximum deviation of spectral shift did not exceed 0.01 nm. This effectively corroborates our theoretical analysis. The relationship between temperature and spectral shift offers a crucial theoretical foundation for calibrating spectral measurements and managing the thermal conditions of the instrument.

Design of a Spaceborne, Compact, Off-Axis, Multi-Mirror Optical System Based on Freeform Surfaces

Based on the application requirements of high spectral resolutions, high spatial resolutions and wide swatches, a new-generation, high-performance, spaceborne, hyperspectral imaging spectrometer (NGHSI) with a spatial resolution of 15 m and a swatch of 90 km is proposed. The optical system of the NGHSI has a focal length of 1128 mm, an F-number of three, a field of view (FOV) of 7.32° and a slit length of 144 mm. A new off-axis, multi-mirror telescope structure with intermediate images is put forward, which solves the design problem that realizes secondary imaging and good telecentricity at the same time. And a new off-axis lens-compensation Offner configuration is adopted to address the challenge of the high-fidelity design of spectral imaging systems with long slit lengths. The relationship between X-Y polynomials and aberration coefficients is analyzed, and the X-Y polynomial freeform surfaces are used to correct the off-axis aberrations. The design results show that the image quality of the telescope system is close to the diffraction limit. The smile, known as the spectral distortion along the line, and keystone, which is the magnification difference for different wavelengths, of the spectral imaging system are less than 1/10 pixel size. The complete optical system of the NGHSI, including the telescope system and the spectral imaging system, has excellent imaging quality and the layout is compact and reasonable, which realizes the miniaturization design.

Design of UV-band hyperspectral resolution imaging spectrometer with small <i>F</i>-number and high variable ratio

Design of UV-band hyperspectral resolution imaging spectrometer with small <i>F</i>-number and high variable ratio

Hyper-Spectral Imaging Spectrometer for Solar-Induced Chlorophyll Fluorescence of Vegetation Observation (Invited)

Hyper-Spectral Imaging Spectrometer for Solar-Induced Chlorophyll Fluorescence of Vegetation Observation (Invited)

Study on the rapid measurement of carbon content in marine sediments based on the model transfer of hyperspectral imaging camera and spectrometer

Laboratory spectrometer and hyperspectral imaging camera are two instruments for obtaining spectral information of substances. Both instruments have advantages in practical applications. In this paper, a total of 164 sediment samples were collected from two locations in the marine intertidal zone of Qingdao Aoshan Bay. The total carbon content (TC) concentration in marine sediments was obtained by chemical method. The spectral data of sediments were obtained by the spectrometer and the hyperspectral data were obtained by a hyperspectral imaging camera. With a spectrometer as the main instrument and a hyperspectral imaging camera as the slave instrument, the rapid measurement of carbon content in marine sediments was realized through the model transfer. Two model transfer algorithms were used: piecewise direct standardization, deviation model updating, and slope/bias correction method (PDS-DMP-S/B), and piecewise direct standardization, model updating, and slope/bias correction method (PDS-MP-S/B). Different modeling results were obtained by changing the proportion of the calibration set and test set and adding preprocessing. Compared with the single model transfer algorithm, PDS-MP-S/B and PDS-DMP-S/B were more effective. Standard normal variable transform (SNV) preprocessing had little effect on the modeling results in the model transfer between the spectrometer and hyperspectral imaging camera. After the data normalization of spectral data, all models were improved. When the ratio of the calibration set to the test set was 3:1, the PDS-DMP-S/B algorithm was used for the model transfer, the RP2 value of the modeling result was 0.693, the RMSEP value was 2.332, and the RPD value was 1.615, which achieved a relatively ideal modeling result. This study provides a new and fast method for predicting the carbon content in marine sediments.

Improving quantification of methane point source emissions from imaging spectroscopy

The matched filter (MF) method is widely used for hyperspectral imaging spectrometers to detect and quantify methane point sources due to its high computational efficiency. However, it would result in an unavoidable underestimation, especially for large concentration enhancements. The lognormal matched filter (LMF) has provided a unique and not limited to weak methane plumes theory through lognormal background radiances modeling, but validations on simulated experiments and applications to real data remain to be explored. Moreover, the covariance contamination caused by the enhanced pixels and the surface heterogeneity have detrimental effects on the detection methods for real data application. In this study, we propose the iterative lognormal matched filter (ILMF) method to address these challenges. We evaluate the performance of the ILMF with two ideal simulations and the end-to-end simulation. The results of random simulated enhancement retrievals show that the retrieved enhancement by the ILMF method agrees well with the true enhancement, with an R2 of 0.984 and a small root-mean-square error (RMSE) of 55.856 ppb. The ILMF method reduce the RMSE of retrieved enhancement by 80% compared with the MF method. The results of end-to-end simulations show the underestimation of MF method at different sites regarding the emission rate, as well as the improvement of the ILMF method. Further, we apply the ILMF method to detect and quantify point sources in some methane hotspot regions. Our study reports the underestimation in traditional MF method and provides an unbiased and robust method for quantifying methane emissions.

Optical Design and Simulation of Snapshot Hyperspectral Dental Imaging Spectrometer with One-Dimensional Random Coding

To achieve perfect teeth color reproduction, we propose the design of a snapshot hyperspectral imaging spectrometer that can accurately measure the teeth spectrum. In this paper, the optical system of a snapshot hyperspectral dental imaging spectrometer is designed. In particular, to encode images in both the spatial and spectral dimensions, a one-dimensional random coding mask rotated 45 degrees around the optical axis is used. The system has a spectral resolution of approximately 2.2∼10 nm in the 450∼700 nm spectral range, and the total length of the system is less than 150 mm. The whole design can be used as a miniaturized handheld instrument. To verify the feasibility of whiteness measuring, a simulation experiment is carried out. The target spectral information is reconstructed through the reconstruction algorithm. The simulation result shows that spectral imaging has the potential to improve the accuracy and precision of teeth whiteness measurements.

Bidirectional reflectance factor measurement of conifer needles with microscopic spectroscopy imaging

Leaf reflectance is widely used to retrieve leaf chlorophyll content (Cab) and parameterize canopy radiative transfer models. Measurements of broadleaf reflectance are typically made by using integrating sphere devices, but the approach is generally limited in conifer needle measurements due to the narrow needle coverage relative to the sample port of the integrating sphere. In this study, we proposed a method to measure the bidirectional reflectance factor (BRF) of needles by integrating a hyperspectral imaging spectrometer and an optical microscope. Pure needle pixels can be easily extracted from hyperspectral images after microscope magnification. The method was first validated by the narrow strips of broad leaves as proxies of needles, resulting in the difference between the measured BRF of strips and the BRF of whole broad leaves in the visible bands (400–700 nm) and in the near-infrared bands (700–1000 nm) being 0.0043 and 0.019, respectively. The method was also indirectly verified by tracking the variation in the natural needle BRF with its Cab, resulting in the change in BRF being consistent with the change in Cab. In addition, linear relationships between the needle Cab and the classic vegetation indices (i.e., the red-edge simple ratio and normalized difference vegetation index) calculated from the BRF were found. Apart from the above, we found that (1) the BRF of the abaxial side has a discrepancy with that of the adaxial side of needles, (2) the BRF of a needle varies with its azimuth, hence affecting the correlation between vegetation indices and Cab, and (3) filtering the specular reflection on the microsurfaces of the needle could enhance the correlation between the vegetation indices and needle Cab. The method is expected to accurately measure the BRF of needles and hence to improve the applications of needle optical properties in plant sciences, ecology, and remote sensing fields.

Mapping methane super-emitters in China and United States with GF5-02 hyperspectral imaging spectrometer

Mapping methane super-emitters in China and United States with GF5-02 hyperspectral imaging spectrometer

Progress in the Preparation and Characterization of Convex Blazed Gratings for Hyper-Spectral Imaging Spectrometer: A Review.

Convex blazed gratings, which can effectively broaden the spectral range and improve spectral resolution, have gradually evolved into a crucial optical component for lightweight and compact imaging spectroscopy instruments. Their design, processing, and testing involve multidisciplinary interdisciplinary scientific issues, and they continue to be a major area of research in imaging optics applications. This paper summarizes the effects of various grating groove shapes and structural parameters on the spectral range and diffraction efficiency of convex blazed gratings, after providing a brief introduction to the typical functions and applications of convex blazed gratings. Firstly, the latest progress in typical processing methods for convex blazed gratings is reviewed. It focuses on the current fabrication processes and reviews their capabilities in creating convex blazed gratings from three main types of technologies, namely ultra-precision machining, high-energy density beam processing, and chemically assisted fabrication processes. Secondly, the adaptability of the manufacturing process for convex blazed gratings on different scales is summarized, analyzing the adaptation of current procedures to various grating fabrication scales and their bottlenecks. Finally, the characterization methods and future feasible characterization methods for convex blazed gratings are reviewed. The development trend of efficient and precise preparation of convex blazed gratings is pointed out.

Spectral unmixing analysis for the lunar surface mineralogy using Chandrayaan-2 IIRS imagery

Hyperspectral imagery (HSI) in the visible to near-infrared wavelength region has a high potential for deciphering mineral compositions of terrestrial and planetary surfaces. Thus, ISRO's Imaging Infrared Spectrometer (IIRS) sensor onboard Chandrayaan-2 (Ch-2) orbiter provides an opportunity to utilise the hyperspectral observations to characterise the lunar surface minerals and composition significantly. Hence, a sensitivity study of hyperspectral observations of the IIRS sensor was carried out for the detection and mapping of various lunar minerals using spectral unmixing analysis. A common region within the hyperspectral image obtained from Chandrayaan-1's (Ch-1) Moon Mineralogy Mapper (M3) sensor and Ch-2 IIRS sensor has been identified in the South-East of the Taurus-Littrow valley nearby Gardner crater for comparison and validation purposes. Ch-2 IIRS is an advanced hyperspectral imaging spectrometer capable of collecting spectra over a wider spectral range (800 ​nm ​− ​5000 ​nm) with high spatial (~80 m) and spectral resolution (20 n m ​− ​25 ​n m ). To examine spectral unmixing analysis, an electromagnetic range (800 ​nm ​− ​2500 ​nm) in the near-infrared region was chosen for the Ch-2 IIRS sensor. An L 1 ​− ​ norm based denoising algorithm is used for mixed noise removal from both the IIRS and M3 reflectance datasets. Endmember extraction and fractional abundance estimation are carried out using the N-Findr algorithm and the fully-constrained least square method, respectively. Our results reveal different minerals such as high-calcium pyroxene (Clinopyroxene), low-calcium pyroxene (Orthopyroxene), and regolith surface (matured and younger). The results obtained from the IIRS and M3 observations are in strong agreement. Hence, the hyperspectral observations made by the IIRS sensor are extremely effective for describing the mineral and chemical compositions of the lunar surface. • Spectral unmixing analysis is executed to determine lunar surface mineralogy and their abundance using Hyperspectral data. • For comparative analysis, co-located datasets from Chandrayaan-2 (Ch-2) IIRS and Chandrayaan-1(Ch-1) M3 are investigated. • An L1-norm-based denoising algorithm is introduced to significantly improve the data quality by removing the complex noise. • The methodology can be automated to develop the lunar mineralogical and chemical map in a time-efficient and systematic way.

Learning the Link between Albedo and Reflectance: Machine Learning-Based Prediction of Hyperspectral Bands from CTX Images

The instruments of the Mars Reconnaissance Orbiter (MRO) provide a large quantity and variety of imagining data for investigations of the Martian surface. Among others, the hyper-spectral Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) captures visible to infrared reflectance across several hundred spectral bands. However, Mars is only partially covered with targeted CRISM at full spectral and spatial resolution. In fact, less than one percent of the Martian surface is imaged in this way. In contrast, the Context Camera (CTX) onboard the MRO delivers images with a higher spatial resolution and the image data cover almost the entire Martian surface. In this work, we examine to what extent machine learning systems can learn the relation between morphology, albedo and spectral composition. To this end, a dataset of 67 CRISM-CTX image pairs is created and different deep neural networks are trained for the pixel-wise prediction of CRISM bands solely based on the albedo information of a CTX image. The trained models enable us to estimate spectral bands across large areas without existing CRISM data and to predict the spectral composition of any CTX image. The predictions are qualitatively similar to the ground-truth spectra and are also able to recover finer grained details, such as dunes or small craters.

A GA and SVM Classification Model for Pine Wilt Disease Detection Using UAV-Based Hyperspectral Imagery

Pine wilt disease (PWD), caused by the pine wood nematode (Bursaphelenchus xylophilus), is a global destructive threat to forests and has led to serious economic losses all over the world. Therefore, it is necessary to establish a feasible and effective method to accurately monitor and estimate PWD infection. In this study, we used hyperspectral imagery (HI) collected by an unmanned airship with a hyperspectral imaging spectrometer to detect PWD in healthy, early, middle and serious infection stages. To avoid massive calculations on the full spectral dimensions of the HI, 16 spectral features were extracted from the HI, and a genetic algorithm (GA) was implemented to identify the optimal ones with the least fitness. Simultaneously, a support vector machine (SVM) classifier was established to predict the PWD infection stage for an individual pine tree. The following results were obtained: (1) the spectral characteristics for pine trees in different PWD infection stages were distinctive in the green region (510–580 nm), red edge (680–760 nm) and near-infrared (780–1000 nm) spectra; (2) the six optimal spectral features (Dgreen, SDgreen, Dred, DRE, DNIR, SDNIR) selected with the GA effectively distinguished the PWD infection stages of pine trees with a lower calculation cost; (3) compared with the traditional classifiers, such as k-nearest neighbor (KNN), random forest (RF) and single SVM, the proposed GA and SVM classifier achieved the highest overall accuracy (95.24%) and Kappa coefficient (0.9234). The approach could also be employed for monitoring and detecting other forest pests.

OpenHSI: A Complete Open-Source Hyperspectral Imaging Solution for Everyone

OpenHSI is an initiative to lower the barriers of entry and bring compact pushbroom hyperspectral imaging spectrometers to a wider audience. We present an open-source optical design that can be replicated with readily available commercial-off-the-shelf components, and an open-source software platform openhsi that simplifies the process of capturing calibrated hyperspectral datacubes. Some of the features that the software stack provides include: an ISO 19115-2 metadata editor, wavelength calibration, a fast smile correction method, radiance conversion, atmospheric correction using 6SV (an open-source radiative transfer code), and empirical line calibration. A pipeline was developed to customise the desired processing and make openhsi practical for real-time use. We used the OpenHSI optical design and software stack successfully in the field and verified the performance using calibration tarpaulins. By providing all the tools needed to collect documented hyperspectral datasets, our work empowers practitioners who may not have the financial or technical capability to operate commercial hyperspectral imagers, and opens the door for applications in new problem domains.

Estimating near-infrared reflectance of vegetation from hyperspectral data

Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is a grand challenge to the remote sensing and ecophysiology communities. Since Solar Induced chlorophyll Fluorescence (SIF) is uniquely emitted from vegetation, it can be used to evaluate how well reflectance-based vegetation indices (VIs) can separate the vegetation and soil components. Due to the residual soil background contributions, Near-infrared (NIR) reflectance of vegetation (NIRv) and Difference Vegetation index (DVI) present offsets when compared to SIF (i.e., the value of NIRv or DVI is non-zero when SIF is zero). In this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts. NIRvH takes advantage of the spectral shape variations in the red-edge region to minimize the soil effects. We evaluated the capability of NIRvH, NIRv and DVI in isolating the true NIR reflectance of vegetation using the data from both the model-based simulations and Hyperspectral Plant imaging spectrometer (HyPlant) measurements. Benchmarked by simultaneously measured SIF, NIRvH has the smallest offset (0–0.037), as compared to an intermediate offset of 0.047–0.062 from NIRv, and the largest offset of 0.089–0.112 from DVI. The magnitude of the offset can vary with different soil reflectance spectra across spatio-temporal scales, which may lead to bias in the downstream NIRv-based photosynthesis estimates. NIRvH and SIF measurements from the same sensor platform avoided complications due to different geometry, footprint and time of observation across sensors when studying the radiative transfer of reflected photons and SIF. In addition, NIRvH was primarily determined by canopy structure rather than chlorophyll content and soil brightness. Our work showcases that NIRvH is promising for retrieving canopy structure parameters such as leaf area index and leaf inclination angle, and for estimating fluorescence yield with current and forthcoming hyperspectral satellite measurements.

Experimental research on oil film thickness and its microwave scattering during emulsification

Synthetic Aperture Radar (SAR) plays a major role in identifying oil spills on the sea surface. However, obtaining information of oil spill thickness (volume) is still a challenge. Emulsification is an important process affecting the thickness and normalized radar cross section (NRCS) of oil film. Experiments of crude oil emulsification with C-band fully-polarized scatterometer were conducted combining airborne hyperspectral imaging spectrometer and 3D laser scanner observation data, to provide experimental parameters and method to support accurate remote sensing monitoring on marine oil spill. It is further proved that through quantitative homogeneous emulsified oil spill experiments, to a certain extent, the NRCS of oil film increased during the emulsification process of crude oil. The backscattering mechanism of crude oil emulsification was explored using a semi-empirical model (SEM); the change of oil film NRCS was modulated by its dielectric constant and surface roughness, in which the dielectric constant showed a dominant effect. The relationship between thickness and NRCS of oil film was studied under two experimental conditions. The differences of NRCS between oil film and adjacent seawater (Δσ0) and the damping ratio (DR) were found to have a linear relationship with oil thickness, which were best in the vertical polarization mode (VV) at 45° incident angle during the quantitative crude oil homogeneous emulsification process. In the natural emulsification process of continuous oil spill in which oil film was mixed with both crude oil and emulsified oil, an empirical equation of oil film thickness is preliminarily established. The Δσ0, DR, and the empirical equation of oil film thickness were applied to the marine continuous oil spill incident on a 19–3 oil platform with spaceborne SAR image and successfully explained the distribution of the relative thickness of the oil film.

Separation and enrichment of nanoplastics in environmental water samples via ultracentrifugation

Nanoplastics are an emerging contaminant in aquatic environments. However, analytical methods for the separation, concentration, and identification of nanoplastics, which are essential to assess nanoplastic presence in the environment, are lacking. Here, we developed a new and easy-to-use method to separate and enrich nanoplastics in field water samples with ultracentrifugation. River water was spiked with polystyrene fragments (< 1000 nm) at an environmentally relevant concentration (108–109 particles/L). The polystyrene fragments were successfully separated and enriched by a factor of nearly 50 times with a high recovery rate (87.1%) after undergoing our process. Particles were then characterized using UV-vis spectroscopy, scanning electron microscopy (SEM), and enhanced darkfield microscopy with a hyperspectral imaging (HSI) spectrometer. These techniques are non-destructive and allow the assessment of plastic concentration, morphology, and polymer type. Our method can potentially be applied to other water samples to supply clean, enriched nanoplastic samples that can facilitate their identification in environmental samples.

Fusion neural networks for plant classification: learning to combine RGB, hyperspectral, and lidar data.

Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but methods to delineate and classify individual plant species using the collected data are still actively being developed and improved. The Integrating Data science with Trees and Remote Sensing (IDTReeS) plant identification competition openly invited scientists to create and compare individual tree mapping methods. Participants were tasked with training taxon identification algorithms based on two sites, to then transfer their methods to a third unseen site, using field-based plant observations in combination with airborne remote sensing image data products from the National Ecological Observatory Network (NEON). These data were captured by a high resolution digital camera sensitive to red, green, blue (RGB) light, hyperspectral imaging spectrometer spanning the visible to shortwave infrared wavelengths, and lidar systems to capture the spectral and structural properties of vegetation. As participants in the IDTReeS competition, we developed a two-stage deep learning approach to integrate NEON remote sensing data from all three sensors and classify individual plant species and genera. The first stage was a convolutional neural network that generates taxon probabilities from RGB images, and the second stage was a fusion neural network that “learns” how to combine these probabilities with hyperspectral and lidar data. Our two-stage approach leverages the ability of neural networks to flexibly and automatically extract descriptive features from complex image data with high dimensionality. Our method achieved an overall classification accuracy of 0.51 based on the training set, and 0.32 based on the test set which contained data from an unseen site with unknown taxa classes. Although transferability of classification algorithms to unseen sites with unknown species and genus classes proved to be a challenging task, developing methods with openly available NEON data that will be collected in a standardized format for 30 years allows for continual improvements and major gains for members of the computational ecology community. We outline promising directions related to data preparation and processing techniques for further investigation, and provide our code to contribute to open reproducible science efforts.

DLR Earth Sensing Imaging Spectrometer (DESIS) Level 1 Product Evaluation Using RadCalNet Measurements

The DLR Earth Sensing Imaging Spectrometer (DESIS) is the first hyperspectral imaging spectrometer installed in the Multi-User System for Earth Sensing (MUSES) on the International Space Station (ISS) for acquiring routine science grade images from orbit. It was launched on 29 June 2018 and integrated into MUSES. DESIS measures energy in the spectral range of 400 to 1000 nm with high spatial and spectral resolution: 30 m and 2.55 nm, respectively. DESIS data should be sufficiently quantitative and accurate to use it for different applications and research. This work performs a radiometric evaluation of DESIS Level 1 product (Top of Atmosphere (TOA) reflectance) by comparing it with coincident Radiometric Calibration Network (RadCalNet) measurements at Railroad Valley Playa (RVUS), Gobabeb (GONA), and La Crau (LCFR). RVUS, GONA, and LCFR offer 4, 15, and 5 coincident datasets between DESIS and RadCalNet measurements, respectively. The results show an agreement between DESIS and RadCalNet TOA reflectance within ~5% for most spectral regions. However, there is an additional ~5% disagreement across the wavelengths affected by water vapor absorption and atmospheric scattering. Among the three RadCalNet sites, RVUS and GONA show a similar measurement disagreement with DESIS of ~5%, while LCFR differs by ~10%. Agreement between DESIS and RadCalNet measurements is variable across all three sites, likely due to surface type differences. DESIS and RadCalNet agreement show a precision of ~2.5%, 4%, and 7% at RVUS, GONA, and LCFR, respectively. RVUS and GONA, which have a similar surface type, sand, have a similar level of radiometric accuracy and precision, whereas LCFR, which consists of sparse vegetation, has lower accuracy and precision. The observed precision of DESIS Level 1 products from all the sites, especially LCFR, can be improved with a better Bidirectional Reflection Distribution Function (BRDF) characterization of the RadCalNet sites.