Relative Spectral Response Functions Research Articles

Calibration method of relative spectral response function of indirect imaging spectrometer

In imaging spectrometers, area array detectors are usually used as photoelectric conversion devices, but the inconsistency of the spectral response among pixels can distort the collected target spectra. To improve the spectral radiometric accuracy of imaging spectrometers, calibrating and correcting the inconsistency of the spectral response among pixels is essential. The signal received by each pixel of area array detector of the indirect imaging spectrometer is usually the superposition of the target multi-spectral radiation signals or full-spectral radiation signals. Therefore, its relative spectral radiometric calibration requires measuring the spectral response of each pixel at different wavelengths on the array detector. Under the ideal conditions, the response values of each pixel in the area array detector are different, so the indirect imaging spectrometer cannot simply calibrate the relative spectral response (RSR) function between pixels by using the method of “monochromator + integrating sphere”. In this work, taking the interferometric imaging spectrometer for example, we analyze the influence of the inconsistency of the RSR among pixels on the target spectral radiation measurement accuracy, and propose a system-level RSR function measurement method for the indirect imaging spectrometer based on the Fourier transform modulation calibration source. In addition, we establish a mathematical model for calibrating the RSR function,and provide guidelines for selecting calibration system parameters such as light source, spectral resolution, and OPD sampling interval. The simulation results show that under the ideal noise-free condition, the 1% spectral response inconsistency among pixels results in a relative error of 1.02% to the recovered spectra. After RSR correction, the relative error of the recovered spectra of different rows decreases to 0.08%. Furthermore, in this work we simulate and analyse the influence of spectral signal-to-noise ratio on the calibration accuracy of the RSR function, and point out that increasing the brightness of the calibration light source, extending exposure time, and combining multi-frame interferograms can enhance RSR function calibration accuracy in practical applications. The research result can provide a theoretical basis for realizing the relative spectral radiometric calibration of indirect imaging spectrometer, which is of great significance in promoting quantitative spectral remote sensing.

Spectral band-shifting of multispectral remote-sensing reflectance products: Insights for matchup and cross-mission consistency assessments

Comparing or integrating remote-sensing reflectance, Rrsλsr−1, data from multispectral sensors is hindered by differences in their nominal bands and relative spectral-response functions. Similarly, assessing satellite-to-in situ-matchup assessments of Rrs using autonomous ground radiometers, such as the Ocean Color component of the Aerosol Robotic Network (AERONET-OC), faces the same challenges. Therefore, spectral band-shifting techniques are required to approximate Rrs in spectral bands that are mutually unavailable. We present a spectral-matching technique (SMTH) using a novel look-up-table (LUT) generated from Rrs obtained from the Hyperspectral Imager for the Coastal Ocean (HICO) sensor. This LUT consists of nearly 6.3 million unique Rrs spectra, processed from 8893 Level-2 HICO scenes. An extensive in situ hyperspectral dataset (n = 8289) was used to compare the performance of SMTH with three commonly used techniques, namely Melin and Sclep (2015) “MS-QAA” model, linear (LNR) and cubic spline (CS) interpolations. The comprehensive evaluation of eight sensors using in situ dataset revealed that the SMTH has significantly better overall accuracy with respect to spectral band-shifting than the existing approaches. The Bias was improved 7-fold, whereas the root-mean-square difference (RMSD) and mean absolute difference (MAD) were ∼ 15–60% lower. Even so, each model exhibits outstanding performance in certain bands. For instance, the LNR model excels in the 380 nm band, while the MS-QAA emerges as the optimal choice for the 400 and 412 nm bands. SMTH outperforms other models in the green-red region, notably above 600 nm, where most existing ocean-color sensors have a limited number of spectral bands. Despite this variability, all models, showcase satisfactory performance in the 443–560 nm region. Significantly, both SMTH and LNR demonstrated a 100% retrieval rate, the ratio of band-shifted samples to the input samples. In contrast, MS-QAA and CS returned invalid values for 7% and 39% of the input samples, respectively. Further evaluation of regional, global, and composite satellite data suggests that the trophic state significantly influences the model performance. The MS-QAA model exhibited the smallest differences within the oligotrophic waters, characterized by low chlorophyll-a concentrations. In contrast, the SMTH model demonstrates the lowest differences in mesotrophic/eutrophic waters and regions subject to harmful algal blooms. While HICO primarily sampled coastal waters, estuaries, and shallow regions from mid- to low latitudes, there is an opportunity to enhance the representativeness of our LUT by incorporating open-ocean and high-latitude Rrs spectra from the upcoming hyperspectral missions, such as NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE).

Spectral Adjustment Model's Analysis and Application to Remote Sensing Data

Differences in the relative spectral response functions of sensors lead to data inconsistencies that should be harmonized before multisensor exploitation. In this paper, we use spectral libraries to simulate satellite data and build models to correct them. We then explore and compare different models for coarse and medium spatial resolution optical sensors, including moderate resolution imaging spectroradiometer, advanced very high resolution radiometer (AVHRR), visible infrared imaging radiometer suite, multispectral instrument aboard Sentinel-2, and Operational Land Imager aboard Landsat 8. We found that optimal correction of different bands depends on the model used. For the green and near infrared bands, a multilinear land cover dependent regression improves the accuracy by up to 80.9%. For the red band, a novel exponential dependence of the spectral band adjustment factor with the normalized difference vegetation index (NDVI) provides an accuracy improvement of up to 72.8%. The best way to correct the NDVI value is to use the corrected NIR and red bands using these models. We apply the proposed methods to 445 BELMANIP2 sites using AVHRR data from the long-term data record from 1982–2017. High NDVI pixels result in 30-year trends varying up to 0.06 when comparing uncorrected to spectrally adjusted NDVI. Further application of these methods to NASA's Harmonized Landsat and Sentinel 2 product shows that for the red band and NDVI, our proposed method provides improved accuracy (54.6% and 62.5%) over the linear spectral adjustment currently used.

Impact of Blackbody Warm-Up Cool-Down Cycle on the Calibration of Aqua MODIS and S-NPP VIIRS Thermal Emissive Bands

This paper evaluates the calibration quality during the blackbody (BB) warm-up cool-down cycle for thermal emissive bands onboard Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS). This evaluation utilizes data from Aqua MODIS Collection 6 Level-1B products and VIIRS Sensor Data Records in 6-min granule format provided by the NASA Land Science Investigator-led Processing System. Nearly simultaneous hyperspectral measurements from the Aqua Atmospheric Infrared Sounder (AIRS) and the S-NPP Cross-track Infrared Sounder (CrIS) are used as references for MODIS and VIIRS, respectively. Each AIRS footprint of 13.5 km is co-located with multiple MODIS pixels while each CrIS field of view of 14 km is co-located with multiple VIIRS pixels. The corresponding AIRS-simulated MODIS and CrIS-simulated VIIRS radiances are derived by convolutions based on sensor-dependent relative spectral response functions. In this paper, the analysis mainly focuses on the bands that are used in sea surface temperature products. The results show that there is virtually no impact for MODIS bands 22 and 23 and bands 31 and 32 for a BB temperature below 290 K; however, when the BB temperature increases above 290 K, the impact is up to 0.3 K for bands 22 and 23 and 0.05 K for bands 31 and 32, respectively. For VIIRS, BB temperature-dependent drifts are observed in M15 and M16, which can reach 0.15 and 0.1 K, respectively, over the operational BB temperature range and the VIIRS brightness temperature range.

Application of Sentinel 2 MSI Images to Retrieve Suspended Particulate Matter Concentrations in Poyang Lake

Suspended particulate matter (SPM) is one of the dominant water constituents in inland and coastal waters, and SPM concnetration (CSPM) is a key parameter describing water quality. This study, using in-situ spectral and CSPM measurements as well as Sentinel 2 Multispectral Imager (MSI) images, aimed to develop CSPM retrieval models and further to estimate the CSPM values of Poyang Lake, China. Sixty-eight in-situ hyperspectral measurements and relative spectral response function were applied to simulate Sentinel 2 MIS spectra. Thirty-four samples were used to calibrate and the left samples were used to validate CSPM retrieval models, respectively. The developed models were then applied to two Sentinel 2 MSI images captured in wet and dry seasons, and the derived CSPM values were compared with those derived from MODIS B1 (λ = 645 nm). Results showed that the Sentinel 2 MSI B4–B8b models achieved acceptable to high fitting accuracies, which explained 81–93% of the variation of CSPM. The validation results also showed the reliability of these six models, and the estimated CSPM explained 77–93% of the variation of measured CSPM with the mean absolute percentage error (MAPE) ranging from 36.87% to 21.54%. Among those, a model based on B7 (λ = 783 nm) appeared to be the most accurate one. The Sentinel 2 MSI-derived CSPM values were generally consistent in spatial distribution and magnitude with those derived from MODIS. The CSPM derived from Sentinel 2 MSI B7 showed the highest consistency with MODIS on 15 August 2016, while the Sentinel 2 MSI B4 (λ = 665 nm) produced the highest consistency with MODIS on 2 April 2017. Overall, this study demonstrated the applicability of Sentinel 2 MSI for CSPM retrieval in Poyang Lake, and the Sentinel 2 MSI B4 and B7 are recommended for low and high loadings of SPM, respectively.

Inversion of radiative transfer model for retrieval of wheat biophysical parameters from broadband reflectance measurements

This study describes the retrieval of wheat biophysical variables of leaf chlorophyll (Cab), leaf area index (LAI), canopy chlorophyll (CCC), and leaf wetness (Cw) from broadband reflectance data corresponding to IRS LISS-3 (Linear Imaging Self Scanner) sensor by inversion of PROSAIL5B canopy radiative transfer model. Reflectance data of wheat crop, grown under different treatments, were measured by hand-held spectroradiometer and later integrated to LISS-3 reflectance using its band-wise relative spectral response function. Three inversion techniques were used and their performance was compared using different statistical parameters and target diagram. The inversion techniques tried were: a look up table with best solution (LUT-I), a look up table with mean of best 10% solutions (LUT-II) and an artificial neural network (ANN). All the techniques could estimate the biophysical variables by capturing variability in their observed values, though accuracy of estimation varied among the three techniques. Target diagram clearly depicted the superiority of LUT-II over the other two approaches indicating that a mean of best 10% solutions is a better strategy while ANN was worst performer showing highest bias for all the parameters. In all the three inversion techniques, the general order of retrieval accuracy was LAI>Cab>CCC>Cw. The range of Cw was very narrow and none of the techniques could estimate variations in it. In most of the cases, the parameters were underestimated by model inversion. The best identified LUT-II technique was then applied to retrieve wheat LAI from IRS LISS-3 satellite image of 5-Feb-2012 in Sheopur district. The comparison with ground observations showed that the RMSE of LAI retrieval was about 0.56, similar to that observed in ground experimentation. The findings of this study may help in refining the protocol for generating operational crop biophysical products from IRS LISS-3 or similar sensors.

Feasibility of estimating leaf water content using spectral indices from WorldView-3’s near-infrared and shortwave infrared bands

ABSTRACTA recently-launched high-resolution commercial satellite, DigitalGlobe’s WorldView-3, has 8 bands in the shortwave infrared (SWIR) wavelength region, which may be capable of estimating canopy water content at 3.7-m spatial resolution. WorldView-3 also has 8 multispectral bands at 1.24-m resolution with two bands in the near-infrared (NIR). The relative spectral response functions for WorldView-3 were provided by DigitalGlobe, Inc., and band reflectances were determined for reflectance spectra of PROSPECT model simulations and leaf data from maize, trees, grasses, and broadleaf herbaceous eudicots. For laboratory measurements, the range of leaf water contents was extended by including drying leaves and leaf stacks of corn, soybean, oaks, and maples. Correlations between leaf water content and spectral indices from model simulations suggested that indices using SWIR band 1 (center wavelength 1210 nm) had low variability with respect to leaf water content, but also low sensitivity. Other indices using SWIR band 5 (2165 nm) had the highest sensitivity, but also had high variability caused by different values of the leaf structure parameter in PROSPECT. Indices using SWIR bands 2, 3 and 4 (1570, 1660, and 1730 nm, respectively) had high correlations and intermediate variability from the leaf structure parameter. Spectral indices calculated from the leaf data had the same overall patterns as the simulations for variation and sensitivity; however, indices using SWIR band 1 had low correlations, and the best correlations were from indices that used SWIR bands 2, 3 and 4. Spectral indices for maize, grasses, and herbaceous crops and weeds had similar responses to leaf water content; tree leaves had higher index values and saturated at lower leaf water contents. The specified width of NIR band 2 (860–1040 nm) overlaps the water absorption feature at 970 nm wavelength; however, the normalized difference of NIR band 1 and 2 was insensitive to water content because NIR band 2’s spectral response was most heavily weighted to wavelengths less than 930 nm. The high spatial resolution of the WorldView-3 SWIR data will help analyze how variation among plant species and functional groups affects spectral responses to differences in canopy water content.

The new method monitoring crop water content based on NIR-Red spectrum feature space

Moisture content is an important index of crop water stress condition, timely and effective monitoring of crop water content is of great significance for evaluating crop water deficit balance and guiding agriculture irrigation. The present paper was trying to build a new crop water index for winter wheat vegetation water content based on NIR-Red spectral space. Firstly, canopy spectrums of winter wheat with narrow-band were resampled according to relative spectral response function of HJ-CCD and ZY-3. Then, a new index (PWI) was set up to estimate vegetation water content of winter wheat by improveing PDI (perpendicular drought index) and PVI (perpendicular vegetation index) based on NIR-Red spectral feature space. The results showed that the relationship between PWI and VWC (vegetation water content) was stable based on simulation of wide-band multispectral data HJ-CCD and ZY-3 with R2 being 0.684 and 0.683, respectively. And then VWC was estimated by using PWI with the R2 and RMSE being 0.764 and 0.764, 3.837% and 3.840%, respectively. The results indicated that PWI has certain feasibility to estimate crop water content. At the same time, it provides a new method for monitoring crop water content using remote sensing data HJ-CCD and ZY-3.

아리랑 2호/3호 영상을 이용한 영상융합 비교 분석

본 논문에서는 사용되는 센서의 특성, 특히 밴드 간 분광 응답함수의 차이에 따라 달라질 수 있는 영상융합 기법의 성능 차이에 대한 상대적 비교 분석을 주된 연구 목적으로 하였다. 이를 위해, CS 기반의 대표적 영상융합 기법인 GIHS, GS1, GSA, GSA-CA을 선정하였고, 거의 동일 시기/동일 지역을 촬영한 KOMPSAT-2/3 위성영상을 이용하였다. 또한, 융합 영상의 성능 평가는 정량적/정성적 방법을 혼용하여 진행하였다. 분석 결과, KOMPSAT-2/3 영상 모두에서 GSA 기법과 GSA-CA 기법이 다른 기법들에 비해 상대적으로 우수한 품질을 나타냈다. 이는 다른 위성영상 및 문헌을 통해 제시된 결과들과 일치하는 것으로, 선형 회귀식을 통해 전정색 영상과 intensity 영상 간 스펙트럴 불일치를 최소화한 기존 방법론의 효능을 뒷받침하는 것이다. 그러나 동일한 실험 조건을 적용하였을 때, KOMPSAT-2와 KOMPSAT-3에서 나타내는 융합 성능은 서로 다른 결과를 나타냈다. 이 같은 결과는 두 위성 센서 내 밴드별 분광응답함수가 서로 다른 것에 기인하는 것으로 판단할 수 있다. KOMPSAT-2의 경우, blue 밴드와 green 밴드의 분광 응답비가 전정색 밴드를 초과하는 것으로 알려져 있으며, 이는 영상 융합 과정에서 과도한 저주파 요소를 삽입하여, 최종적으로 제작된 융합 영상에서의 색상 왜곡을 유발하는 원인이 되는 것으로 알려져 있다. 반면, KOMPSAT-3에서는 KOMPSAT-2에서 발생되었던 상당부분의 관련 문제들을 보완 하였으며, 결과적으로 동일한 실험 조건에서도 상대적으로 향상된 융합 결과를 나타냈다. This paper had a purpose on analyzing result data from pan-sharpening, which have applied on the KOMPSAT-2 and -3 image. Particularly, the study focused on comparing each relative spectral response functions, which considers to cause color distortions of fused image. Two images from same time and location have been collected by KOMPSAT-2 and -3 to apply in the experiment. State-of-the-art algorithms of GIHS, GS1, GSA and GSA-CA were employed for analyzing the results in quantitatively and qualitatively. Following analysis of previous studies, GSA and GSA-CA methods resulted excellent quality in both of KOMPSAT-2/3 results, since they minimize spectral discordances between intensity and PAN image by the linear regression algorithm. It is notable that performances from KOMPSAT-2 and- 3 are not equal under same circumstances because of different spectral characteristics. In fact, KOMPSAT-2 is known as over-injection of low spatial resolution components of blue and green band, are greater than that of the PAN band. KOMPSAT-3, however, has been advanced in most of misperformances and weaknesses comparing from the KOMPSAT-2.

Herschel celestial calibration sources

Celestial standards play a major role in observational astrophysics. They are needed to characterise the performance of instruments and are paramount for photometric calibration. During the Herschel Calibration Asteroid Preparatory Programme approximately 50 asteroids have been established as far-IR/sub-mm/mm calibrators for Herschel. The selected asteroids fill the flux gap between the sub-mm/mm calibrators Mars, Uranus and Neptune, and the mid-IR bright calibration stars. All three Herschel instruments observed asteroids for various calibration purposes, including pointing tests, absolute flux calibration, relative spectral response function, observing mode validation, and cross-calibration aspects. Here we present newly established models for the four large and well characterized main-belt asteroids (1) Ceres, (2) Pallas, (4) Vesta, and (21) Lutetia which can be considered as new prime flux calibrators. The relevant object-specific properties (size, shape, spin-properties, albedo, thermal properties) are well established. The seasonal (distance to Sun, distance to observer, phase angle, aspect angle) and daily variations (rotation) are included in a new thermophysical model setup for these targets. The thermophysical model predictions agree within 5% with the available (and independently calibrated) Herschel measurements. The four objects cover the flux regime from just below 1,000 Jy (Ceres at mid-IR N-/Q-band) down to fluxes below 0.1 Jy (Lutetia at the longest wavelengths). Based on the comparison with PACS, SPIRE and HIFI measurements and pre-Herschel experience, the validity of these new prime calibrators ranges from mid-infrared to about 700 micron, connecting nicely the absolute stellar reference system in the mid-IR with the planet-based calibration at sub-mm/mm wavelengths.

Assessment of consistency in forest-dominated vegetation observations between ASTER and Landsat ETM+ images in subtropical coastal areas of southeastern China

Vegetation is one of the most important components of the global ecosystem. It plays a vital role for global energy balances and climate modulations. Timely and precisely monitoring vegetation changes has become an increasing need. Among various satellite-based earth observation systems, the Landsat and ASTER sensors are most commonly used in mesoscale vegetation observations. Nevertheless, the quantitative relationship between the two sensors in measuring vegetation has not been investigated in detail. Therefore, this paper aims to investigate how well ASTER vegetation measurements replicate ETM+ vegetation measurements, and more important, how much difference there is in the measurements between the two sensors. The study utilised three date-coincident image pairs of the two sensors, covering the subtropical coastal areas of southeastern China. The approach was achieved by evaluating the consistence of the normalised difference vegetation index (NDVI) data and the NDVI-estimated vegetation area between the two sensors. A further inter-type comparison among forest, paddy and grass has also been carried out to examine whether there is any variation in the consistence between different vegetation types. Results suggest that the ASTER sensor produces similar vegetation measurements to ETM+ in the study areas. Nevertheless, differences have also been observed. The study found an overall lower spectral NDVI measurement of ASTER than ETM+ based on their differences in mean NDVI value and estimated vegetation area. This suggests that the ASTER sensor is less sensitive to vegetation characteristics. Compared with ETM+, the ASTER sensor produces lower NDVI values by up to 8.1% and estimates less vegetation area by up to 4.1%. Inter-type analysis indicates that among the three test vegetation types, forest has the highest degree of agreement in NDVI measurements between the two sensors, whereas grass has the greatest difference in mean NDVI value between both sensors. All these differences are worth considering when the vegetation measurement results of ASTER have to be used together with those of ETM+ for a synergistic scientific application. This often occurs when using the ASTER sensor data for Landsat data continuity in scientific applications in order to fill the gaps in observation. As such, a data conversion between the two sensors is recommended. This paper demonstrated that the conversion using the model equations obtained in this study could reduce the root mean square errors and improve data agreement between the two sensor NDVIs. The study shows that the revealed differences in vegetation measurements between both sensors are due largely to the difference in the relative spectral response functions of the NDVI-related near-infrared bands between the two sensors.

Intercomparison of monochromatic source facilities for the determination of the relative spectral response of erythemal broadband filter radiometers.

The relative spectral responses of erythemally weighted broadband radiometers determined at three different laboratories are compared, and the systems are described. The results of measurements of four different broadband radiometers are discussed. Although the common dynamic range of the measured relative spectral responses is approximately 10(4), the differences in the relative spectral response functions are lower than 20%. These differences are related mostly to measurement uncertainties and differences in the spectral response facilities.

Asteroids as calibration standards in the thermal infrared for space observatories

Asteroids have been used extensively as calibration sources for the Infrared Space Observatory (ISO) and are planned to be used by future groundbased, airborne and space-based projects in the thermal infrared (IR) and in the sub-millimetre. We summarize the general IR observational parameters with a focus on space observatories and discuss brightness variations, apparent velocities and background influences. During the ISO mission ten well-studied asteroids were used for the photometric calibration of ISOPHOT, but additionally the bright asteroids turned out to be of great interest for many technical tests and calibration aspects. We evaluated the different applications, like testing the photometry of the spectrometers, validation of relative spectral response functions, determination of beam profiles or colour correction tests. The description of the asteroids' thermal emission has been obtained by a recent thermophysical model (TPM). The important model aspects are size, albedo, shape together with the spin vector, a beaming model, thermal inertia and a wavelength-dependent emissivity. With a large sample of observational data provided by three different ISO instruments we had for the first time the possibility to study the thermal emission of several asteroids in detail. The intercomparison between results from different instruments allowed us to distinguish between observational errors and model shortcomings. It turned out that the accuracy of TPM predictions is in many cases strongly related to the limited knowledge of the asteroid shapes. The concepts of beaming, thermal inertia and wavelength dependent emissivities were nicely confirmed for a wide range of observing and illumination geometries under many aspect angles for different asteroids. The TPM predictions for Ceres, Pallas and Vesta are accurate within 5% over the full wavelength range from 5 to 200 , for Hygiea and a few other asteroids the predictions and observations agree within 10 to 15%. We found similar emissivity behaviour for the four large asteroids over the full ISO wavelength range. Up to now, no clear spectral features have been seen in the asteroid far-IR spectra.