This PDF file contains the front matter associated with SPIE Proceedings Volume 11829, including the Title Page, Copyright Information, and Table of Contents.

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project relies on top-of-atmosphere (TOA) broadband fluxes derived from geostationary (GEO) satellite imagery to account for the diurnal flux variations between the CERES observation intervals, and thereby produce a synoptic gridded (SYN1deg) product based on continuous temporal observations. Consistent broadband flux derivation depends on accurate radiative property measurements and cloud retrievals, which largely determine the radiance-to-flux conversion process. Therefore, it is important to ensure a high quality of cloud property input in order to maintain a reliable broadband flux record. In Edition 4 of the CERES SYN1deg product, a robust automated image anomaly detection algorithm based on inter-line and inter-pixel differences, spatial variance, and 2-D Fourier analysis has been successful in identifying imagery with linear artifacts, but the line-by-line inspection and cleaning process must still be performed by a human. Therefore, further automation of this quality assurance process is warranted, especially considering the excessive amount of additional cleaning necessitated by the GOES-17 Advance Baseline Imager (ABI) cooling system anomaly. As such, this article highlights advancement of the CERES GEO image artifact cleaning approach based on a convolutional neural network (CNN) for classification of bad scanlines. Once trained, the CNN approach is a computationally inexpensive means to ensure greater consistency in cloud retrievals, and therefore broadband flux derivation, based on GOES-17 measurements.

Global Warming is causing increasing Heat Waves (HW) that affect human health. In this context, urban heat islands (UHI) increase the effects of heatwaves, representing a serious inconvenience to human health and comfort. For these reasons the study of UHI is of great importance in the context of climate change and global warming. The literature on urban climate has highlighted the singular importance of the nighttime UHI. High nighttime temperatures have a negative effect on human comfort and health. Situation that is aggravated in extreme heat events, such as Heat Waves. For this reason, this work seeks to study the spatial distribution of temperature in a situation of maximum nocturnal thermal stress, in order to know the real impact of UHI in heat wave episodes. Given the practical identity between air temperature (that is, that experienced by humans) and land surface temperature (obtained by remote sensing), the study of the nocturnal LST is of great importance. This paper aims to develop a nighttime land surface temperature (LST) model using Landsat imagery in order to study UHI contribution in HW episodes. Once nighttime LST (supplied by the Landsat 8 TIR sensor) has been obtained, the analysis of the mean temperature of land uses (obtained by Urban Atlas) allows a first approach to the UHI at night. First of all, a "geographic" OLS model is developed, with explanatory variables such as longitude, latitude, altitude, slope, orientation and distance to the sea. Model that allows knowing the impact of the "physical" variables in the spatial distribution of the nocturnal LST, without considering the urban aspects. Next, the real surface temperature of each type of urban and rural landscape is compared with that obtained in the "geographic" model. Comparison that allows knowing not only which landscapes are hotter (or colder), but also their heat balance in relation to their physical-geographical characteristics. Finally, an OLS model is developed integrating, together with the "geographical" variables, "urbanterritorial" variables (such as NDVI, NDBI, albedo, imperviousness, …). "Hybrid" model that allows to know in detail the spatial distribution of the UHI, as well as the contribution of each type of urban landscape to the night UHI. The case study is the Metropolitan Area of Barcelona (636 km², 3.3 million inhabitants).

Cropland is extremely important for the global food security. However, soil erosion caused by water affects crop productivity, and the runoff water pollutes both the fresh water and marine ecosystems, leading to severe economic and environmental issues. While there have been many ground water detection techniques using satellite optical imaging sensors, synthetic aperture radar (SAR) proves to be an important alternative. Because microwave can penetrate through the cloud, SAR data often become the only data available when local cloud cover blocks the optical imaging sensors. However, literature reveals that the detection of ground water using SAR data is often obscured by vegetation, which also appears dark in SAR images. This severely reduces the water detection accuracy. In this study, the freely available Sentenel-1 SAR data from the European Space Agency (ESA) Copernicus missions were used to characterize the water and non-water sites in the cropland in eastern South Dakota, USA. A total number of 159 sites were pre-selected from the SAR images. Field surveys were conducted. Of the 159 sites, 78 were identified as water sites and 81 as non-water sites. The water and non-water site data at both VV and VH polarizations were downloaded and analyzed. The density functions of the shifted Rayleigh distributions were estimated using maximum likelihood estimation (MLE). The residue errors are small. The distribution functions between water and non-water sites will facilitate the development of a more accurate classification algorithm for cropland water detection. Such information is important for precision agriculture.

The paper briefly describes the physical foundations of water and ice characteristics according to microwave data. A review of the work is presented where, based on model calculations and experimental measurements, the emissivity of the ice cover in the microwave range is described, the questions of the development of models for sea ice with strong and moderate absorption and porous structures, the features of the radiation indices of young ice, ice with low salinity and pack ice. Specific examples of the classification of phenomena on the water surface and ice cover are given. The program modules included in the system were used to process data from radiophysical experiments with the Kosmos- 1500 satellite for the Arctic regions. The statistical characteristics of the "spotting" of the radio brightness temperatures, obtained for the most informative thresholds, are analyzed. It is asserted that these characteristics can be used in the detection of abnormal phenomena on the water surface and ice cover.

The paper deals with the construction of a model of "spotting" of the background characteristics of the studied space according to remote sensing data. The most obvious way to identify spots is the threshold setting method. In this case, the area of the spot includes that part of the space in which the indicator of the medium for this channel exceeds (l+- characteristic) or does not exceed (l-- characteristic) the threshold value. The work is carried out modular structure of the statistical modeling system of spotting The structure of the software is proposed. A subsystem for studying the characteristics of "spotting" is considered, as well as a subsystem for qualitative interpretation and visualization of geoinformation monitoring data. The corresponding software modules are considered. The proposed method was used to study the characteristics of the "spotting" of the "ocean-atmosphere" system based on satellite data for some regions of the World Ocean.

Aqua-AIRS data have been available now for almost 20 years, Suomi-NPP CrIS for 9 years, and JPSS1 CrIS for 4 years. The three instruments satisfy nearly identical functional requirements, and are in very similar polar orbits. However, there are differences in the design, spatial scan pattern and sun-angle, in addition to absolute calibration differences. We compared AIRS, CrIS SNPP and CrIS JPSS data for the 1148 days between 20180217 and 20210430 for the 900 cm-1 channel under clear and random sampled tropical ocean, tropical land, and Arctic conditions. Although JPSS and SNPP are identical designs, the difference between JPSS and SNPP are as large as the differences relative to AIRS. For the day and night tropical ocean at typically 295K and for night tropical land and under the Arctic conditions (265K typical), averaged over all scan angles, detector elements and time, AIRS, JPSS and SNPP agree within 100 mK. Under Antarctic and Antarctic conditions, the difference between JPSS and AIRS and SNPP and AIRS are on average 200 mK, but with a much larger summer/winter correlated pattern. For clear tropical day (318K), AIRS is 500 mK warmer than JPSS, but 150 mK colder than SNPP. The three instruments find 40% fewer clear cases at night than during the day. With JPSS we find 3% fewer clear cases than with AIRS, 9% more than with SNPP day and night. Differences seen in the past three years in the AIRS, SNPP and JPSS data are calibration artifacts. Unless corrected in the construction of merged AIRS/SNPP/JPSS and follow-on time series, they will complicate the interpretation of trends potentially related to climate change.

We use large datasets from the Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive AIRS spatial response functions and study their potential variations over the mission. The new reconstructed spatial response functions can be used to reduce errors in the radiances in non-uniform scenes and improve products generated using both AIRS and MODIS data. AIRS spatial response functions are distinct for each of its 2378 channels and each of its 90 scan angles. We develop the mathematical model and the optimization framework for deriving spatial response functions for two AIRS channels with low water vapor absorption and various scan angles. We quantify uncertainties in the derived reconstructions and study how they differ from pre-flight spatial response functions. We show that our approach generates reconstructions that agree with the data more accurately compared to pre-flight spatial responses. We derive spatial response functions using data collected during successive dates in order to ascertain the repeatability of the reconstructed spatial response functions. We also compare the derived spatial response functions based on data collected in the beginning, the middle, and at the current state of the mission in order to study changes in reconstructions over time.

Solar radiation-based calibration (SRBC) has been used to assess radiometric calibration of earth remote sensing sensors using a source with the same spectral source that is expected during the instrument's operation. The approach has been shown capable of providing high-accuracy radiance calibration, the merits of which have been proven for laboratory and field instruments, as well as serving as a preflight calibration option for airborne and spaceborne imagers. The current work presents multiple SRBC results for a small portable transfer radiometer that was developed as part of an effort to ensure the quality of upwelling radiance at automated test sites used for vicarious calibration in the solar reflective. The results are used to develop an updated absolute uncertainty budget for the SRBC method. Comparisons of the SRBC method to standard source-based laboratory calibrations and a detector-based method recently developed for the CLARREO Pathfinder mission.

The GOES-16 satellite was launched on 19 Nov 2016, and it became operational as the GOES-East satellite on 18 Dec 2017. The GOES-17 satellite was launched on 1 Mar 2018, and it became the GOES-West operational satellite on 12 Feb 2019. The Advanced Baseline Imager (ABI) is one of six instruments onboard GOES-16 and GOES-17. It has 16 multispectral bands in the 0.45 μm to 13.56 μm wavelength range. This study is focused on bands 1–3, 5, and 6, which reside in the solar-reflective region (0.4 μm to 2.5 μm). The five bands used in this work have spatial resolutions of 500 m (band 2), 1000 m (1, 3, and 5), and 2000 m (band 6). The geosynchronous orbit of GOES provides a unique opportunity for the Radiometric Calibration Test Site (RadCaTS), which is an automated facility at Railroad Valley, Nevada, USA. RadCaTS consists of ground-based instruments that measure the surface reflectance and atmosphere throughout the day. It was developed by the Remote Sensing Group of the Wyant College of Optical Sciences at the University of Arizona (UArizona), and it is currently used to monitor such low-Earth orbit (LEO) sensors as Landsat 7 ETM+, Landsat 8 OLI, Terra and Aqua MODIS, Sentinel-2A and -2B MSI, SNPP and NOAA-20 VIIRS, and others. The improved spectral, spatial, and temporal characteristics of ABI create an excellent opportunity to intercompare results obtained from a geosynchronous sensor to those obtained from typical LEO sensors.

Dome-C is a recommended CEOS invariant target that has been utilized by the calibration community for several decades for monitoring onboard sensor calibration systems as well radiometric inter-comparisons. Dome-C is a highaltitude Earth target located on the East Antarctic interior plateau, which has a permanent bright, flat, and homogeneous snow-covered surface with little aerosol, cloud cover, snowfall, and water vapor burden. This paper describes angular directional models for characterizing the Dome-C top-of-atmosphere (TOA) radiances as a function of cosine solar zenith angle for pre-solstice and post-solstice conditions. The 0.86-μm channel Dome-C reflectance decreases over the summer due to snow metamorphosis is not observed by the visible channels. Coinciding Terra and Aqua MODIS Dome- C reflectance showed occasional inter-annual anomalies when compared against the deep convective cloud and Libya-4 invariant targets observations. Further characterization of the Dome-C reflectances with the Dome-C surface broadband albedo, Antarctic Oscillation (AAO) index, and ozone concentration values were evaluated. A strong correlation with ozone was found for the 0.55-μm and 0.65-μm MODIS channels. The monthly Dome-C reflectances were linearly regressed with ozone to derive the ozone correction coefficients. The uncertainty in the Aqua- and Terra-MODIS Dome- C trends was reduced by half after applying ozone corrections to both the 0.55-μm and 0.65-μm channel TOA observations.

The VIIRS instruments onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 (N20) spacecraft have provided openly-public data for scientific studies and to address global issues for close to one decade and over three years, respectively. VIIRS has 7 Thermal Emissive Bands (TEB) with central wavelengths that range from 3.7 μm to 12.0 μm and are calibrated on-orbit using observations from its onboard blackbody calibrator. The on-orbit performance of the S-NPP and N20 VIIRS TEB has been assessed in the past and shown to be quite stable with marginal degradation. However, in order to maintain VIIRS’ rich, well-calibrated archive of multispectral imagery and data, Earth targets should be regularly monitored to track its on-orbit long-term stability as well as the consistency between VIIRS sensors. This manuscript focuses on evaluating the TEB on-orbit radiometric calibration stability for both VIIRS instruments using an in situ ocean site - moored buoy located in the Pacific Ocean that belongs to the NOAA’s National Data Buoy Center – as reference. Furthermore, it will assess the calibration consistency between VIIRS sensors. Only cloud-free, nighttime VIIRS TEB retrievals are used in this study. A normalization methodology is applied to standardize the VIIRS data to the in situ data. Results indicate excellent S-NPP VIIRS TEB on-orbit performance (all mission-long temperature drifts are within -0.16 K) and good consistency between VIIRS sensors (average S-NPP-to- N20 temperature differences for most bands are within -0.20 K) over ocean temperatures. In the future, similar strategies can be employed to monitor the TEB on-orbit radiometric calibration stability for the VIIRS instruments that will be onboard the Joint Polar Partnership System-2 (JPSS-2), JPSS-3, and JPSS-4 satellite missions.

The Landsat-8 Thermal Infrared Sensor (TIRS) has been acquiring two-band thermal infrared images of the Earth’s surface since 2013. The calibration of the two-band system has been monitored using the on-board calibrator and validated with vicarious calibration performed by NASA/Jet Propulsion Laboratory and Rochester Institute of Technology since launch. An update to the radiometric calibration was introduced into the Collection-2 processing system in late 2020 to correct for signal-dependent and time-dependent calibration errors. In November 2020, the Landsat-8 spacecraft experienced two safeholds and, while TIRS seemingly recovered nominally, there were slowly developing changes as a result. By December 2020, the TIRS Band 11 responsivity had decreased by as much as 2%. It was determined that a contaminant has been slowly depositing on a component in the optical path since the safehold and continues as of this writing. As of late June 2021, the responsivity is still decreasing in both spectral bands; Band 11 band-average responsivity has dropped by 3.7% and Band 10 band-average responsivity has dropped by 2.0%, though the decrease in responsivity is not uniform across the focal plane. Since March 2021, the TIRS products have been processed with calibration gains that account for the changing responsivity.

The U.S. Geological Survey (USGS) archive of Earth images acquired by Landsat 1-8 sensors is organized in collections of consistently calibrated, geolocated, and processed data products. Such an organization ensures consistent quality of the archived data within a collection over time and across all instruments within the Landsat mission. In December 2020, the USGS completed reprocessing of the archived Landsat data and released a new collection, Collection 2, which introduced surface reflectance and surface temperature Level-2 products, implemented improved ground control and elevation datasets, and brought several geometric and radiometric calibration enhancements. Radiometric enhancements include absolute and relative gain updates for both imaging sensors, Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), aboard Landsat 8 and a change in calculation of bias for OLI. In this paper we present the analysis of Collection 2 Landsat 8 products demonstrating stability over the mission lifetime, the improvement in OLI signal to noise ratio and along-track striping performance resulting from enhanced bias correction, as well as reduction of cross-track striping in TIRS data as a result of relative gain updates. In addition, we discuss effects of two safehold events that Landsat 8 experienced in November 2020 on the radiometric calibration of both sensors and product performance.

In late 2020 the U.S. Geological Survey (USGS) began the distribution of Landsat products associated with their collection 2 reprocessing of the archive. Several changes were implemented within the Landsat Product Generation System (LPGS) and the calibration parameters applied to the Landsat imagery for the collection 2 processing. When comparing between collection 1 and collection 2 products, radiometric and geometric differences will be present. One of the most substantial changes between the two collections was an adjustment to the ground control which made the control more accurate from an absolute and relative perspective. Some of the other changes associated with collection 2 processing were to the Digital Elevation Model (DEM) used in terrain correction, the Thermal Infrared Sensor (TIRS) relative gains, TIRS absolute calibration, Operational Land Imagery (OLI) absolute gain model, OLI relative gain, and OLI bias calculation to name a few. Although these changes also have an effect on the differences between the product associated with these two collections, the change in the ground control, although typically less than one 30-meter multispectral pixel in magnitude, will have the largest effect on the differences between the products. This change in ground control is also a spatially dynamic change that although is low in spatial frequency, is nonlinear and is not a change that can be modeled on a global or even on a local Worldwide Reference System-2 (WRS-2) path and row scale. The effects of these ground control changes will be discussed and demonstrated within this paper along with examples showing their effect on specific datasets. This paper demonstrates some of these geometric differences associated with the ground control through both the registration statistics created during product generation and through an example comparison of a set of collection 1 and collection 2 products.

The U.S. Geological Survey (USGS) has completed processing of the historical Landsat archive to Collection-2 as of December of 2020 and has released it to the public. As part of Collection-2, several geometric changes have been implemented, including changes to the ground control points (GCPs) and elevation datasets. These datasets are used as a geometric reference for all missions. In addition, mission specific improvements were included in Collection-2, such as improvements to the precision correction algorithms and updates of the calibration parameters. This paper discusses a preliminary analysis of a comparison between the Collection-1 and Collection-2 products of the entire Landsat archive. Compared to the Level 1 products in Collection-1, the number of Tier-1 precision- and terrain-corrected (L1TP) Level 1 products in Collection-2 increased by 6.26% across all sensors. Landsat 8 products showed an increase of Tier-1 L1TP products by 5.33%; Landsat 7 products showed an increase of Tier-1 products by 6.94%; and Landsat 5 and Landsat 4 Thematic Mapper (TM) products showed an increase of Tier-1 products by 9.35%. The geometric accuracy of the Tier-1 terrain corrected products also improved by 2 meters or more.

Landsat 9 is in its final preparations for launch from Vandenberg Space Force Base on 16 September 2021. It has completed its environmental testing at Northrop Grumman Space (NGSP) in Gilbert, Arizona and has been transported to its California launch site. It will be launched into a 705 km orbit replacing Landsat 7 to provide 8-day Earth land mass coverage in concert with Landsat 8. Landsat 8 carries the first Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS); Landsat 9 carries the second of each: OLI-2 and TIRS-2. Once launched it will undergo a 90-day activation, checkout, characterization and calibration, a.k.a. commissioning phase before transitioning to operations. For a several-day period during this commissioning phase, Landsat 9 will under-fly Landsat 8, allowing near simultaneous data collection by both sensors of common Earth targets. These data will be used to compare the radiometric calibrations of the instruments and allow for adjustments of processing parameters to provide more consistent data products.

Like Landsat 8, Landsat 9 will carry an Operational Land Imager (OLI), a visible to short-wave infrared pushbroom sensor. Referred to as OLI-2, this sensor has 8 spectral bands with about 7000 detectors and one panchromatic band with about 14000 detectors. Maintaining uniformity of the data products is a challenge, particularly at the low end of the response’s dynamic range, as the on-board diffuser provides a high signal level. Water color analyses in coastal and inland areas imaged by Landsat, are one of the more demanding science applications of the OLI data, in part due to the low signal levels. OLI data are serving such demanding science applications due to its excellent noise, stability and calibration characteristics. A new set of on-orbit calibration collects planned for OLI-2 are aimed at enhancing the linearity characterization; these collects should help in keeping uniform imagery through a wide dynamic range. Improved pre-launch characterization of the OLI-2 radiometric response non-linearity led to the idea of using two sets of on-orbit collects to confirm the stability of non-linearity correction across a wider range of response. Collects will be taken where the detector integration time is varied while looking at a constant target: a bright solar diffuser panel and a dimmer internal lamp. Both objects are integral parts of the existing OLI calibration assembly subsystem, but only the diffuser was used in this manner on Landsat 8 OLI. Using pre-launch test data, we demonstrate how the combination of these two sets of data collects will assist in confirming the non-linearity correction is maintained to be under 0.5% across much of the dynamic range. If the on-orbit results show changes, then with such data we should be able to refine the calibration parameters until they minimize the residual errors in the non-linearity throughout the dynamic range.

The Joint Polar Satellite System 3 (JPSS-3) Visible Infrared Imaging Radiometer Suite (VIIRS) instrument is the fourth in a series (S-NPP VIIRS launched in October 2011, JPSS-1 VIIRS launched in November 2017, and JPSS-2 VIIRS currently undergoing spacecraft integration) of highly advanced polar-orbiting environmental satellites. JPSS- 3 VIIRS underwent a comprehensive sensor-level Thermal Vacuum (TV) testing at the Raytheon Technologies El Segundo facility in the fall of 2020. While the test program provided characterization for many spatial, spectral, and radiometric aspects of the VIIRS sensor performance, this paper focuses on the radiometric performance of the 14 reflective solar bands (RSB) that cover the wavelength range from 0.41 to 2.3 μm. Key calibration parameters, such as the instrument gain, signal-to-noise ratio (SNR), dynamic range and radiometric uniformity, were derived in a TV environment for both the primary and redundant electronics at three instrument temperature plateaus: cold, nominal, and hot. This paper shows that all the JPSS-3 VIIRS RSB detectors have been well characterized, with the key performance metrics being comparable to those of the previous VIIRS instruments. Comparison of radiometric performance to sensor requirements, as well as a summary of key sensor testing and performance issues, will also be presented.

The Joint Polar Orbiting Satellite System (JPSS) is a partnership between NASA and NOAA to build new generation polar-orbiting operational environmental satellite system which started with the launch of the Suomi NPP in 2011. JPSS- 3 will be the 4th satellite in JPSS program and is scheduled to launch in 2026. The flagship instrument on the JPSS satellites is the Visible Infrared Imaging Radiometer Suite (VIIRS) which provides visible and infrared imagery in support of a variety of land, ocean and atmospheric products. JPSS-3 VIIRS much like the first three VIIRS instrument will feature the panchromatic Day-Night Band (DNB), which is capable of obtaining high quality imagery over a wide range of illumination conditions from daytime to reflected moonlight using a wide bandpass in combination with time delay integration. JPSS-3 VIIRS underwent a rigorous prelaunch test program carried out by the sensor vendor, Raytheon, El Segundo in ambient configuration in 2019 and flight-like conditions in thermal vacuum in late 2020 into early 2021. As part of this test program a series of tests to characterize its spatial, radiometric, spectral, and functional performance at the instrument level were completed. The DNB radiometric measurements were subsequently analyzed by both vendor and government teams. These analyses are critical to show compliance with the sensor design specifications as well as the ability of the instrument to produce high quality imagery and radiometry similar to the two instruments currently in operation. Presented in this work is the radiometric and spectral performance of the DNB including dynamic range, sensitivity, radiometric uncertainty and non-linearity along with a discussion of the performance in comparison with its predecessors S-NPP, JPSS-1 and JPSS-2 VIIRS.

The Joint Polar-orbiting Satellite System (JPSS)-3 Visible Infrared Imaging Radiometer Suite (VIIRS) has completed a large portion of its ground test program. The VIIRS series includes two sensors successfully operating on-orbit onboard the Suomi National Polar-orbiting Partnership (SNPP) and JPSS-1 satellites and a third sensor, currently integrated into the JPSS-2 satellite, is undergoing observatory level testing. VIIRS was designed to take high quality measurements of the Earths surface from low Earth orbit which are (or will be) used to generate a large set of environmental data products important in science. To ensure that the VIIRS instruments generate these high quality measurements once on orbit, each sensor undergoes an extensive ground test program at the component, sensor, and observatory levels. One of the important aspects of the instrument performance that needs to be characterized prior to launch is the response versus scan angle (RVS). This is the scan angle dependent change in reflectance of the rotating optics. On VIIRS, the only component of the rotating optics for which the angle of incidence changes with scan angle is the half angle mirror (HAM). The RVS was characterized for all bands, including reflective and thermal channels; results indicate that the sensor is behaving within expectations. Uncertainty estimates and atmospheric corrections for water vapor were also included in the analysis. Characterization of the RVS is critical to the retrieval of the TOA radiance and hence to ensure that the sensor performs with sufficient fidelity to provide the level of data quality that the science community demands. This work focuses on the performance of the JPSS-3 VIIRS RVS derived from instrument level ground testing, by the independent government team and compares that performance to previous builds.

The NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) is a key instrument in the JPSS missions (SNPP, JPSS-1-4). Being part of the calibration and validation process, JPSS-3 VIIRS prelaunch geometric performance assessment focuses on the sensor’s spatial response, band-to-band co-registration(BBR), and pointing knowledge. In general, JPSS-3 VIIRS’ prelaunch geometric performance is very good, and consistent with SNPP and JPSS-1-2 VIIRS sensors. This paper highlights some specific key findings from the JPSS-3 VIIRS’ prelaunch tests. We firs t s how that with timing adjustments, JPSS-3 VIIRS scan BBR error between VisNIR and S/M/LWIR bands has been reduced from 0.12/0.03 M sampling intervals to 0.005/0.002 M sampling intervals, respectively. Focal Plane Assembly (FPA) rotations have beenchangedfrom0.1 degree to 0.04 degree in VisVIR, from 0.18 degree to 0.09 degree in S/MWIR, and 0.16 degree to 0.08 degree in LWIR. Another finding from the pre launch test is that JPSS-3 VIIRS has relatively large band-to-band co-registration errors between VisNIR and S/M/LWIR bands in the track direction. This BBR mis - registration increases when aft‐optics assembly (AOA) temperature decreases. The BBRmis-registration is about 0.10 M sampling intervals between VisNIR and S/MWIR bands, and 0.08 M sampling intervals between Vis NIR and LW IR bands. By comparison, the track BBR offset in JPSS-2 is less than 0.02 M sampling intervals between VisNIR and S/M/LWIR bands. We also notice that the unsymmetrical Day Night Band (DNB) scan-direction Line Spread Function (LSF) of JPSS-2 VIIRS at different gain stages and aggregation modes have been corrected in J3 VIIRS.

The Ocean Color Instrument (OCI) is the primary sensor on the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) mission. OCI is a new type of sensor compared to NASA’s ocean color heritage sensors VIIRS, MODIS, and SeaWiFs. Unlike its heritage sensors, OCI has two slit grating hyperspectral spectrographs in addition to a fiber-coupled multiband filter spectrograph. The two hyperspectral spectrographs provide continuous coverage from 340nm to 885nm in the UV to NIR range. These spectrographs use programmable CCD detectors that aggregate multiple CCD pixels that can effectively provide various spectral resolutions. The fiber-coupled multiband filter spectrograph provides seven discrete spectral bands from 940nm to 2260nm. OCI completed system level testing of the Engineering Test Unit (ETU) in June 2021 at the Goddard Space Flight Center (GSFC). The ETU contains one of the slit grating spectrographs (600nm to 885nm) and the fiber-coupled spectrograph. The ETU was tested in thermal vacuum (TVAC) in February 2020 and January 2021 to assess characterization and performance compliance to design requirements. This paper presents an overview of the spectral performance of the OCI ETU for relative spectral response (RSR), integrated Out-of-Band response (IOOB), system gain, band centers, and bandwidths.

The Ocean Color Instrument (OCI) is the primary instrument on NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. OCI flight model will be a hyper-spectral scanning (HSS) radiometer designed to measure spectral radiances from the ultraviolet to shortwave infrared (SWIR) currently in development at the Goddard Space Flight Center (GSFC). The OCI engineering model provides hyperspectral coverage from 600nm to 885nm and 7 discrete spectral bands from 940nm to 2260nm. The engineering model’s radiometric response and sensitivity to polarized light has been measured as a function of scan angle in ambient, and in thermal vacuum for nadir viewing. This paper will state the polarization requirements, describe the various polarization measurements and present and discuss the polarization measurement results.

The Plankton Aerosol Cloud ocean Ecosystem (PACE) Ocean Color Instrument (OCI) has completed the ground test program for its engineering unit (ETU) and testing of the flight unit will begin in the near future. OCI is a grating spectrometer with hyperspectral coverage from about 340 nm to 885 nm with 9 additional filtered channels in the SWIR. Two CCDs are used as detectors for the hyperspectral channels. One important operating mode of the CCDs on OCI is progressive time delay integration (or PTDI). In this mode, the charge in the CCD can be held for multiples of the nominal integration times. A series of these measurements can be made with progressively increasing multiples of the nominal integration time as the instrument scans across a uniform source. Ground testing with this operating mode on OCI ETU has shown promising results. This work will present measurements taken with the PTDI mode and the analysis of OCI ETU linearity and dynamic range.

The Multi-angle Imaging SpectroRadiometer (MISR) was launched in 1999 as part of NASA’s Terra satellite suite of instruments. Still operational, it makes use of observations from multiple view angles to retrieve aerosol abundance and particle properties. The Multi-Angle Imager for Aerosols (MAIA) instrument improves on this heritage by incorporating polarimetry and an expanded spectral range. Combining these data with surface measurements, the relationship between pollution and human health will be explored. MAIA has just completed camera testing, building on the experience from MISR. Spectral calibration now makes use of a double subtractive monochromator, built with the intent to allow the exit slit output to be uniform in spectral content. For radiometric testing, hardware upgrades have included adding UV lamps to the 1.65 cm (65") integrating sphere, use of a NIST-traceable sphere to establish absolute radiances, and the addition of a UV transfer spectrometer to support characterization of the sphere output from 300 to 2500 nm. During camera build, Newton’s rings were observed in the detector Quantum Efficiency (QE) measurements. This is due to etaloning within the detector itself. Etaloning was also evident in the spectral and radiometric characterizations performed on the completed camera. Spectral metrics, including center wavelength and width, are presented here using a moment’s analysis. This better represents the band properties, particularly in bands where fringing is observed, as compared to a full-width at halfmaximum determination. The MAIA camera has been carefully characterized, and meets its spectral and radiometric requirements.

The Multi-Angle Imager for Aerosols (MAIA) instrument is currently scheduled for launch into polar Earth orbit in 2023. MAIA uses a single camera on a 2-axis gimbal for multi-angle viewing of Earth scenes, with the objective of making radiometric and polarimetric measurements. The data will then be used to determine aerosol size, type, and density. Health records will be collected in parallel as part of the MAIA investigation to enable correlation of adverse health effects with the aerosol data. The MAIA camera includes one visible and one SWIR detector, comprising a total of 14 wavelength bands from the UV to SWIR. Three of the bands are polarimetric. The MAIA telescope is a four-mirror anastigmat and has significant distortion of the field-of-view. Each channel in the camera comprises one or two rows of pixels, and each row has a total of 1216 pixels dedicated to imaging. Different channels must be co-registered to each other and each pixel must be geolocated to a fraction of the pixel size prior to determination of polarimetry and subsequent derivation of aerosol data. This requires, among other items, an accurate model of the camera internal geometry. This paper summarizes the geometric calibration performed during pre-flight testing to measure the transformation from pixel positions in the focal plane to angles in object space, i.e., the pointing angle of each pixel, and vice-versa.

Increased striping was observed in Suomi-NPP VIIRS DNB nighttime imagery after a flight software update on January 8, 2020. It occurred after an onboard computer reboot, resulting in a sudden change in the HGS dark offset. The sudden change made the HGS DN0 (dark offset) LUT outdated, eventually degrading the nighttime imagery. Analysis of the DNB monthly calibration data collected before and after the flight software update indicated that the sudden change in the HGS dark offset was induced by an unexpected change of the HGS electronic bias after an onboard computer reboot during the flight software update. An algorithm capable of correcting the sudden change while also eliminating star light contamination has been adopted for determining the DN0 LUT since the DNB monthly calibration starting in late January 2020. Nighttime image quality affected by the electronic bias change has been restored after the LUT update.

Solar Diffuser (SD) has been used for on-orbit imaging sensors such as historical Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and next generation NOAA’s Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. The SD provides radiometric calibration reference for Reflective Solar Bands (RSB) and it also carries ground calibration to on-orbit. There have been slight discrepancies in the long-term radiometric calibration using SD and moon. Even though there have been some studies and publications to explain these differences, the differences were assumed to be the viewing angle differences to the SD. Using a Surface Roughness-induced Rayleigh Scattering (SRRS), we tested the current SD degradation estimation algorithms and found that the center wavelength based current SD estimation algorithm over-estimated SD degradations up to one percent level in the shortest wavelength band after two years of operation. Instead of using the center wavelength approach, the correct SD degradation was estimated by using the Relative Spectral Response (RSR) function of the SDSM detectors. After applying the correct SD degradation, the on-orbit calibration coefficients were very consistent with the moon based calibration.

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the National Oceanic and Atmospheric Administration (NOAA) NOAA-20 satellite was launched on November 18, 2017. Its on-orbit radiometric calibration is based on the Solar Diffuser (SD) observations for the Reflective Solar Bands (RSB). Due to the ultra-violet portion of the Sun light, the roughness of the SD surface is gradually increased which degraded the reflectance property of the SD surface. The SD degradation needs to be accurately measured as on-orbit radiometric calibration reference. For this purpose, Solar Diffuser Stability Monitor (SDSM) frequently measures the SD surface reflectance changes from the 8 SDSM detectors. The SDSM has two view ports called Sun and SD view ports. The Sun view with attenuation screen provides the reference Sun illumination at the time of SD view from the same detectors. The ratio between the Sun and SD observation provides the SD degradation (or called H-factor) at the 8 selected wavelengths. The prelaunch SDSM Sun view transmission function had significant problems that caused more than one percent oscillations in the H-factors. Previously, the SDSM Sun view transmittance function was updated by using the 15 yaw maneuvers on January 25 and January 26, 2018 as a part of post-launch tests. In addition to the yaw data, the SDSM Sun view transmittance function was updated using on-year on-orbit SDSM data to fill up the non-yaw angles. In this study, the NOAA VIIRS radiance team updated the SDSM Sun view transmittance function using 3 year on-orbit SDSM data. The H-factor with the updated Sun transmittance function is stabilized especially in the short wavelengths providing better on-orbit calibration for the on-orbit NOAA-20 VIIRS RSB calibration.

The Control Point Matching(CPM) program and a set of over 1200 globally distributed ground control points (GCPs ) have been successfully used to develop more than 20 years of MODIS geolocation products. In this research, we refresh current GCP library with more than 2500 new GCPs using the latest Landsat 8 Collection 2 images. The refreshed GCPs are distributedfrom56 S to 80 N latitude, with more than 2000 shoreline and 500 inland GCP chips. The size of these GCPs are extended from 800*800 to 1400*1400 Landsat pixels and the CPM program correspondingly increases the searching distance from0.8 pixels to 2.5 pixels, which also extends the geolocation error measurement from+/-45 to the edge of scan at +/-55 degree in scan angle. This will allow the algorithm to catch geolocation errors that are larger than one MODIS pixel. The geolocation errors measured with the refreshed GCP library are comparable to the previous results, yet with 2-3 more times of matched GCPs. The daytime Aqua ascending orbits and Terra descending orbits enable us to identify a few GCP outliers which might be due to the quality of the original Landsat images. Most importantly, the refreshed GCP library will include images from both Landsat band 4 to match with VII RS I1, and Landsat band 6 to match with VIIRS I3. This will allow us to measure and correct on orbit band-to-band registration at both track and scan directions, which will help understanding and improving future JPSS mission’s prelaunch geometric performance.

To provide the best science data quality, an accurate characterization of the response versus scan angle (RVS) function is critical for the MODIS reflective solar bands (RSB) on-orbit calibration. In every MODIS operational scan, the Earth’s surface, referred to here as Earth view (EV), the space view (SV) port, and the onboard calibrators are viewed via a twosided scan mirror. The RVS is defined as the sensor’s relative response as a function the angle of incidence (AOI) to the scan mirror. Many different approaches have been developed to derive the time-dependent RVS and its look-up table (LUT) applied to MODIS Level 1B (L1B) products since calibration Collection 4. For most MODIS RSB, the on-board calibrators can reasonably track the RVS change with time. In practice, their RVS is derived using data from on-board calibrators and the EV mirror side ratio (for mirror side 2). For Terra bands 1-4, 8-10 and Aqua bands 1-4, 8-9, an enhancement has been employed in Collections 6 and 6.1 (C6/C6.1) by using Earth scene response trending from pseudoinvariant desert sites in addition to the onboard calibrators. The current C6/C6/1 RVS algorithm is focused on fitting the EV data at each AOI over time and then deriving the relative change at different AOI. The EV response trending is currently fitted with multiple segments over time. Alternatively, the EV responses can be fit first as a function of AOI before fitting temporally in order to reduce the dependence on the stability of the desert site. These pre-treatment methods on the EV data provide improvement in the derived calibration coefficients. However, evidence of insufficient calibration is still observed in the MODIS L1B reflectance data, especially in the form of differences between the mirror sides. In this paper, we review the current methodologies that utilize the EV response trends from the pseudo-invariant Libyan desert targets to supplement the gain derived from the onboard calibrators. An improvement is then proposed and investigated such that a sliding window average (SWA) is used to pre-process the raw EV data. The SWA parameters are carefully selected using trade-off studies to accurately track the Earth scene response trending in multiple cases to overcome the reflectance differences between two mirror sides. Calibration results show improvements for both Aqua and Terra MODIS RSB L1B data products. This new adjustment has been included in the recently delivered Collection 7 LUT that will be evident in the L1B products expected to be released in late 2021.

MODIS instruments have been operating over 21 and 19 years on the Terra and Aqua spacecraft, respectively. As both instruments continue to operate beyond their design lifetime of 6 years, relying on the on-board calibrators alone is proving a challenge to accurately characterize their response at all scan angles, especially for the reflective solar bands. This study presents an alternative approach that relies on Earth view responses at multiple scan angles from a single site over Dome Concordia (Dome C), Antarctica, one of the most homogeneous land surfaces uniformly covered by snow. This approach has advantages of more frequent overpasses over the entire scan angle range than the desert sites.

The Joint Polar Satellite System 3 (JPSS-3) is the follow-on to the Suomi-National Polar-orbiting Partnership (S-NPP), National Oceanic and Atmospheric Administration 20 (NOAA-20) and JPSS-2 satellites. A primary sensor on the S-NPP, NOAA-20 and JPSS satellites, the Visible-Infrared Imaging Radiometer Suite (VIIRS) has 22 bands covering a spectral range of 0.412-12.0 µmwith spatial resolutions of 742mand 371m for the moderate and imaging bands, respectively. VIIRS provides radiometrically calibrated Sensor Data Records (SDRs) using a combination of pre -launch characterization and on-orbit calibration sources, such as the Solar Diffuser (SD) for the Reflective Solar Bands (RSBs) and an On-Board Calibrator BlackBody (OBCBB) for the Thermal Emissive Bands (TEBs), for gain correction. Each VIIRS sensor build goes through extensive pre-launch characterization at vendor’s testing facility. This includes ambient, vibration, electro-magnetic interference and thermal vacuum (TVAC) environments. Each test environment is used to characterize different performance parameters for sensor functionality and on-orbit applications. This paper will focus on the JPSS-3 VIIRS pre-launch TEB calibration measured during the sensor level TVAC testing in late 2020. This will include the dynamic range, noise equivalent delta temperature, gain characterization and radiometric retrievals as well as a brief comparison with heritage VIIRS sensor TVAC results.

The Visible-Infrared Imaging Radiometer Suite (VIIRS) is a primary sensor on-board the Suomi-National Polar-orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration 20 (NOAA-20) spacecrafts, launched in October 2011 and November 2017, respectively. VIIRS has 22 bands: 7 thermal emissive bands (TEBs), 14 reflective solar bands (RSBs) and a Day Night Band (DNB). Both the RSBs and TEBs use a combination of pre-launch and on-orbit calibration information to provide accurate radiance, reflectance, and brightness temperature Sensor Data Records (SDRs). An on-orbit comparison of the S-NPP and NOAA-20 RSB SDR radiances, using ground calibration sites and vicarious methods such as deep convective clouds, show that there is a ~2% bias between the two sensors for all the reflective solar bands. Additionally, the M5 (0.672 μm) and M7 (0.865 μm) bands of Suomi NPP VIIRS have larger biases. An investigation of each sensor’s pre-launch and on-orbit calibrations was performed to ascertain the root cause of the sensor-to-sensor bias. This paper will discuss the VIIRS methodologies for both pre-launch and on-orbit calibration and how they affect the SDR product performance, as well as the most likely cause for the bias . In particular, the characterization of the reflectance of the Solar Diffuser and its reflectance variation versus view geometry was performed with different methodologies for S-NPP and NOAA-20 VIIRS. It is anticipated that J2, J3 and J4 VIIRS sensors will behave more similarly to NOAA-20 than S-NPP VIIRS due to the methodology for the SD characterization being consistent with NOAA-20.

Realizing the rapid detection of ship targets is of great significance both in commercial shipping activities and in modern warfare. Compared with visible light remote sensing images, synthetic aperture radar (SAR) has good resolution characteristics for metal targets, making ship targets more clearly visible in high-resolution SAR images. However, there are often obvious coherent bright spots and sea clutter noise in high-resolution SAR images used for ship detection, which seriously affects the detection accuracy. This paper proposed a detection method that uses fast non-local means (FNLM) filter and deep learning methods. Using high-resolution SAR images as the data source, FNLM and deep learning methods are used to realize the detection of ships quickly and accurately. First, the FNLM filter was used to preprocess highresolution SAR images to reduce overall image noise while enhancing target definition and feature details; Then, the Faster R-CNN algorithm was used to conduct model training on large-scale SAR data sets, to extract detection features, to improve the convergence accuracy of the network, and to achieve automatic and rapid detection of ships on the sea. Experimental results showed that this method had good robust characteristics and target detection accuracy, and the average accuracy of ship target detection reaches more than 90%. In the case of severe sea clutter interference, it still had a better detection effect.

With the continuous development of remote sensing technology, the resolution of remote sensing image is gradually improving, and the information contained in high resolution remote sensing image is also more rich, which is widely used in various fields. The extraction of buildings using high resolution remote sensing image has also become one of the research hotspots. In order to solve the problems of incomplete or missing buildings detected by traditional building detection algorithms in high resolution remote sensing images, and the similarity of spectral features between buildings and roads and bright soil is difficult to distinguish. The purpose of this study is to develop an effective building extraction algorithm based on improved adaptive MBI index based on GF-2 satellite data. First radiation calibration in remote sensing image preprocessing, such as the original image from the RGB space transformation to do after YCbCr space information, and to adaptive image enhancement processing, feature extraction with adaptive body mass index, and using vegetation index set threshold value of buildings and shadow region, the preliminary extraction result post-processing of the building, So as to get the final extraction result. Compared with the traditional building morphological index MBI algorithm, the error of error and error of missing classification are reduced, and the overall accuracy is improved by about 20%.

Remote sensing images have the characteristics of multiple data sources and complex data. How to integrate remote sensing image information more efficiently has always been the focus of research. In this paper, Changchun City, Jilin Province, China was selected as the experimental area, Sentinel-1 and Sentinel-2 images were used as experimental data, and a fusion method of SAR image and multispectral image using texture feature information was proposed. First, perform HIS transformation on the multi-spectral image to obtain the intensity image. After that, wavelet transform was used to extract the high-frequency and low-frequency detail components of the intensity image. At the same time, the principal component analysis method and the deep learning network VGG-19 were used to extract the texture features of the SAR image. The SAR texture image was used to enhance the high-frequency detail component of the intensity image, and combined with the original low-frequency detail component to perform inverse wavelet transform, then a new intensity image was obtained. Finally, the modulated intensity image was used to replace the original intensity image, and the inverse transformation (I-HIS) was performed to obtain an enhanced image fused from the multispectral image and the SAR image. Compared with the original image, the detailed features and boundary distinction were significantly improved. The fusion image was input into the support vector machine for feature classification, and the comprehensive classification accuracy reached 94.74%, which was 3.5% higher than the classification accuracy of the unfused image.

With the application of agriculture in remote sensing technology, it has become a development trend to extract accurate farmland boundary information from high-resolution remote sensing images. Farmland plot is the smallest unit of crop planting, so accurate boundary extraction of farmland plot is of great significance for crop classification. Jilin Province in Northeast of China is selected as the experimental area. Taking GF-2 data as the data source, the effects of UNet, HED algorithm based on deep learning, CNN color edge extraction algorithm and traditional morphological edge detection operator in farmland boundary extraction are compared, and then a farmland boundary extraction method based on the combination of deep learning and morphological edge detection operator is proposed in this paper. Compared with using deep learning network or morphological edge detection operators alone, this method has higher peak signal-to-noise ratio and structural similarity, and better farmland edge extraction effect.

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the NOAA-20 (N20) satellite was launched on November 18, 2017. The N20 VIIRS reflective solar bands (RSBs) are calibrated on-orbit using a solar diffuser. An accurate on-orbit calibration is crucial to the high-quality downstream products facilitating atmosphere, ocean and land applications. In this study, the stability of the Level 1B (L1B) reflectance product is investigated using measurements over deep convective clouds (DCCs) for M-bands M1-M5, M7-M11, and I-bands I1-I3. The methodologies developed previously for Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and Suomi National Polar-orbiting Partnership (SNPP) VIIRS are extended and applied to the N20 RSB to derive DCC-based trends. The Collection 2 L1B data produced by NASA Land Science Investigator-led Processing Systems (SIPS) is used to evaluate the performance of the N20 VIIRS RSB calibration. At nadir, the reflectance trends for M1, M5, M8-M11, and I3 are insignificant compared to their corresponding variations (STDs) except for bands M2-M4, M7, and I1-I2, whose trends are larger than or equivalent to their STDs. The reflectance is relatively stable compared to their STDs for all the study RSBs at six aggregation zones across the entire scan angle range. Also discussed in this paper are the detector-to-detector differences and half-angle mirror side differences using the DCCs. Future applications using DCCs, which include an intercomparison with SNPP VIIRS, are also discussed.

The VIIRS instruments onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 (N20) spacecraft have been operating and providing public data records to the scientific and research communities for close to one decade and 3 years, respectively. VIIRS has 7 Thermal Emissive Bands (TEB), calibrated on-orbit using observations from its on-board blackbody, with central wavelengths that range from 3.7 μm to 12.0 μm. While the on-orbit performance of the VIIRS TEB for both instruments have been evaluated in the past and shown to be quite stable (maximum gain changes of 3.0 % and 0.5 % for S-NPP and N20 VIIRS, respectively, and noise equivalent difference temperature values well within specification), diurnal F-factor (measure of gain change) oscillations associated with orbital changes are common. This manuscript focuses on evaluating the VIIRS TEB F-factor diurnal oscillations impact on the Earth view (EV) retrievals. Daily VIIRS F-factor data is fitted using a sinusoidal regression to find the F-factor oscillation amplitude for each TEB. Afterwards, this amplitude is added to typical F-factor values and used in an analytical model to compute its impact on the EV brightness temperature (BT) for all the VIIRS TEB. Results show that the diurnal F-factor oscillations cause EV BT changes of up to 0.070 K (bands I5 and M16) and 0.045 K (M16) for S-NPP and N20 VIIRS, correspondingly. The findings and methodology used in this study present a possible mitigation strategy for the correction of EV BT changes caused by diurnal gain oscillations in the Level 1B data.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the Terra and Aqua satellites, launched in 1999 and 2002, respectively. Relying on a two-sided paddle-wheel cross-track scanning mirror, the sensor provides Earth view measurements in 36 spectral bands from 0.4 to 14.4 μm. The swath of data collected by MODIS is over 2300 km wide and covers the entire surface of the Earth every one to two days. A set of onboard calibrators include a solar diffuser (SD) and a solar diffuser stability monitor, a v-grooved flat panel blackbody (BB), and a spectro-radiometric calibration assembly. A deep space view (SV) is used as a background reference. The thermal emissive bands (TEBs) on‐orbit calibration is performed using a quadratic algorithm by reference to a temperature controlled BB on a scan-by-scan basis. This study examines the consistency of the Terra MODIS TEB responses between the two mirror sides for measurements from the BB, SV, SD, and Earth view sectors. It was found that there is a mirror side correlated noise (MSCN) during the early mission of Terra MODIS, particularly after the electronic configuration changes or instrument resets. In this work, the impact of MSCN on radiometric calibration is assessed using measured brightness temperatures obtained from the deep convective clouds, ice/snow surface at Dome C, Antarctica and the Atlantic Ocean. Results of this study provide a further assessment of the newly implemented Collection 7 calibration algorithm in reducing the MSCN induced striping in the MODIS L1B imagery.

The multispectral imaging sensors on the Terra platform have been operating for over two decades facilitating a variety of scientific applications. The MODIS sensor provides the largest spectral coverage of 0.41 to 14.2 μm, acquiring data at three different spatial resolutions, 250 m, 500 m, and 1 km. The MISR instrument views the Earth using nine discrete cameras pointed at fixed angles including viewing the nadir direction at a spatial resolution of 275 m and covering a wavelength range from 0.44 to 0.86 μm. Being on the same platform, the two sensors complement each other in terms of spatial coverage (and target viewing geometry) and facilitate synergistic applications using multispectral data. A consistent radiometric calibration between these sensors is a prerequisite for creating high quality science products from their observations. Both instruments underwent intensive prelaunch characterization, with calibration monitored on-orbit using their onboard calibrators. In this paper, we perform a calibration inter-comparison of the spectrally matching bands of the two instruments using vicarious techniques. Vicarious techniques include multiyear simultaneous views of the North African desert, North Atlantic Ocean and Dome Concordia, thereby covering the different parts of the dynamic range. Also included in this work are the near-simultaneous top-of-atmosphere (TOA) reflectance measurements from Railroad Valley, USA, as provided by the RadCalNet (converted to TOA), that are used as a calibration reference to compare the on-orbit observations between MODIS and MISR.

Remote sensing of the Earth allows receiving medium, high spatial resolution, and hyperspectral measurements from spacecraft. This study presents a remote sensing application using time-series satellite images to monitor solid waste disposal facilities (WDF) of northern and high mountainous areas with landfills. This study develops a mathematical model describing the physicochemical process of changing the natural state of the snowy regions of the Earth. This technique is directly related to the landfill's physicochemical and biological processes, which can be assessed using space monitoring data. This is relevant for the implementation of geological monitoring of territories covered by snow cover, particularly for the regions of the North Caucasus. It also studies the consequences of climate change on forming the biomedical component of the process under study. The paper proposes a methodology and a mathematical model of the impact of solid domestic and industrial waste on the snow cover using remote sensing technologies. The work uses the methods of fractal-percolation, chemical, biological, and regression analysis. The algorithm results are shown on the example of satellite images of the mountainous territories of the North Caucasus.