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Comparison between VIIRS Bright Pixel Surface Albedo (BPSA) (green circles), MODIS Collection 5 eight-day standard product (blue squares), and MODIS Collection 6 daily albedo (analogous to the VIIRS Dark Pixel Surface Albedo (DPSA), red circles) over the Sahara site (a stable desert location: 26.450°N, 14.083°E) for 17 January to 4 August 2012. Daily BPSA varies between 0.29 and 0.40 in the Sahara. A recent look-up table (LUT) reduces this somewhat, but view-angle effects still dominate (LUT implemented 18 January 2013). A solution suggested to reduce variability is to simply implement a multiday average.
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[1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observ...
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... investiga- tion of IDPS BPSA outputs (version Mx6.2) over a location in the Sahara (26.450°N, 14.083°E) revealed that this variability continued over what should have been a stable calibration location (cf. Figure 3). A look-up table correction over the summer of 2012 exacerbated the problem. ...
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... The polar-orbiting LEO weather satellites provide global-scale daily or near daily data of Earth conditions (Table 10). Their monitoring capabilities were enhanced with the addition of the VIIRS instrument [182], one of five instruments, to the NASA/NOAA operational bridging mission, the Suomi NPP (National Polar-orbiting Partnership) satellite (Table 10). The Suomi NPP was launched in 2011 into an afternoon orbit ~13:30 at an altitude of 830 km, now extended until 2025. ...
Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth’s status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth’s surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.
... Since VIIRS has spectral bands similar to MODIS, the water quality parameters that MODIS can extract or derive can also be directly monitored by VIIRS. VIIRS can provide watercolor products for highly turbid ICWs, improving the observations capabilities of MODIS, whose watercolor bands tend to saturate [29][30][31][32][33][34]. In December 2018, the United States launched SeaHawk-1, a CubeSat fitted with a low-cost, miniature ocean color sensor called HawkEye that enables fine spatial resolution observations of the ocean [35]. ...
In recent decades, as eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, so has the need for timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), integrated onboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, was the first geostationary ocean color observation satellite and was operational from April 1, 2011 to March 31, 2021. Over ten years, GOCI has observed oceans, coastal waters, and inland waters within its 2,500 km×2,500 km target area centered on the Korean Peninsula. The most attractive feature of GOCI, compared with other commonly used watercolor sensors, was its high temporal resolution (1h, eight times daily from 0 UTC to 7 UTC), providing the opportunity to monitor the quality of ICWs, where optical properties can change rapidly throughout the day. This systematic review aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined GOCI’s strength and performance with different processing methods. These articles reveal that GOCI played an essential role in monitoring the ecological health of ICWs in its observation area in East Asia. GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by geostationary satellites in monitoring water quality and provide suggestions for improvements.
... They producing high-resolution images and/or datasets, characterized by precise geometric accuracy and detailed radiometric information which allow the analisys of biogeophysical parameters [42,45,49] to provide a wide range of products to depict the land, oceans, and beyond [26]. Technological advancements have ushered in superior spatial and temporal resolutions, offering new service opportunities such as ESA's Sentinels [32] and NPP VIIRS [27] products. Despite SRS provides worldwide information, still it needed to be adapted to regional or local characteristics, thus leading to discrepancies between satellite-derived metrics and actual surface parameters [48]. ...
Satellite remote sensing technology has proven effective in monitoring various environmental parameters, but its efficiency in assessing shallow lakes has been limited. This study applies state-of-the-art machine and deep learning algorithms supported by classical statistic methods to analyze remote sensing data to measure chlorophyll-a (Chl-a) concentration levels. Focused on a shallow coastal lagoon, Mar Menor, this work analyzes statistically daily Sentinel 3 information behaviour and compares Machine Learning and Deep Learning techniques to enhance efficiency and accuracy data of this satellite. Convolutional Neural Networks (CNNs) stand out as a robust choice, capable of delivering excellent results even in the presence of anomalous events. Our findings demonstrate that the CNN-based approach directly utilizing satellite data yields promising results in monitoring shallow lakes, offering enhanced efficiency and robustness. This research contributes to optimizing remote sensing data to and produce a continuous information flow addressed to monitoring shallow aquatic ecosystems with potential environmental management and conservation applications.
... (b) The longer period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) of overlapping data used to calibrate the data could alleviate the uncertainties in the long-term NDVI data set, instead of only using a four or seven year overlapping period for calibration (Bao et al., 2014;Mao et al., 2012). Using data covering more overlapping years could provide excellent opportunities and implications for calibrating data from multiple sensors in the future, such as between MODIS and the Visible Infrared Imager Radiometer Suite (VIIRS) data on board the Suomi National Polar-orbiting Partnership (NPP), which was launched in 2012 to replace MODIS when it eventually finishes operations (Justice et al., 2013;Zhang et al., 2018). We note that already there are 10 overlapping years between these instruments, but there are differences in their spectral response functions. ...
The start of vegetation growing season (SOS) plays an important role in the energy cycle between the land and atmosphere. Due to the limited temporal span of a single satellite sensor through time, the continuous variation of the SOS over 40 years has not been adequately quantified. Using the overlapping periods (2001–2015) between the Global Inventory Modeling and Mapping Studies (GIMMS) (1982–2015) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) (2001–2021) data sets, we construct an NDVI data set covering the period 1982–2021 on the Mongolian Plateau and further construct a map of relative climatic constraint on the SOS (divided into “temperature‐constrained,” “precipitation‐constrained,” and “other” regions) for quantifying SOS variability. We show that the constructed NDVI data set has high consistency and continuity with earlier GIMMS NDVI data. Regions with the SOS constrained by temperature account for 55.3% of the plateau and are located in northwestern and northeastern cold areas, while regions with the SOS constrained by precipitation constitute over 34.7% and are located in central and southwestern drier regions. Importantly, the temperature‐constrained SOS has continuously and significantly advanced, with a total advance of 4.8 days over 40 years. In contrast, the precipitation‐constrained SOS reversed from advancing to delaying in 2005. This suggests that differentiating the climatic constraint on the SOS might be a practical treatment for reducing the uncertainties in the SOS trends in previous studies. Interestingly, the precipitation‐constrained SOS does not significantly correlate to both the chilling and forcing temperatures, indicating less dependency of the SOS on chilling, which may not have been well considered previously.
... Moreover, the MODIS sensor can be used in various subdomains of fire research, i.e. vegetation indices (VARI [5], LFM [95], NDVI [96], NMDI [97] etc.), fuel type characterization [98], fire risk evaluation [99], fire detection [100], smoke detection [26], burned area mapping [55], time series analysis of fuel characteristics [96] etc. "VIIRS" sensor gains more and more attention in Application of Remote Sensing Technology in Wildfire Research recent years. The first document related to "VIIRS" in our study is published in 2013, which includes the evaluation of VIIRS land product such as Land Surface Temperature, Vegetation Indices, Surface Type, Active Fires, etc. [101]. The VIIRS products are mainly used for active fire detection [102] and burned area mapping [103]. ...
Applications of Remote Sensing (RS) has recently attracted increasing attention in wildfire research. In this study, a total of 2842 documents related to remote sensing-based wildfire research (RS-based wildfire research) have been collected from Science Citation Index Expanded database in the Web of Science Core Collection (WOS CC) for analyzing its development and currently popular concerns by using VOSviewer. Results show that the publications exhibit an exponential increase on the whole since 2000. It is identified that the most productive journal is Remote Sensing, with 235 published articles, accounting for 8.27% of the total research publications. The United States is the most prolific country with 1200 documents. NASA, USDA Forest Service and University of Maryland that affiliated to USA are the top three institutions with more than 100 publications. The co-authorship network shows that the application of remote sensing to wildfire attracts a large number of researchers’ attention. The topic analysis demonstrates that the hot topics cover the whole process of wildfire. The reference analysis and co-citation network also confirm this finding.
... Among these, the LAI/FPAR products from MODIS on the Terra platform have been widely used since 2000 and represent a milestone in operational generation of vegetation parameters from satellite observations (Knyazikhin, 1999;Yan et al., 2016Yan et al., , 2021c. The LAI/FPAR are also available from MODIS on the Aqua platform and VIIRS on the Suomi National Polar-Orbiting Partnership (S-NPP) and the Joint Polar Satellite System (JPSS) satellites since 2002 (2012), ensuring the extension of the Terra MODIS long-term data record (Justice et al., 2013). The MODIS/VIIRS LAI/FPAR datasets have contributed significantly to many studies, such as terrestrial carbon sinks, understanding seasonal and interannual variations in equatorial forests, analyses of spatial patterns of drought, and climate and energy flux dynamics (Tang et al., 2013;Mariano et al., 2018;Chen et al., 2019Chen et al., , 2022Sun et al., 2022). ...
Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as the moderate resolution imaging spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial–temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a sensor-independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500 m/5 km/0.05∘, 8 d/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high-quality retrievals, (ii) application of the spatial–temporal tensor (ST-tensor) completion model to extrapolate LAI and FPAR beyond areas with high-quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections and various spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons with ground data and indirectly through reproducing results of LAI/FPAR trends documented in the literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-tensor completion model. Comparisons of SI LAI (FPAR) CDR with ground truth data suggest an RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which outperform the standard Terra/Aqua/VIIRS LAI (FPAR) products. The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy dataset than sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy to high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agree with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap filling modeling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modeling, and land policy development for informed decision-making and sustainable environmental management. The SI LAI/FPAR CDR is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8076540 (Pu et al., 2023a).
... In recent years, remote sensing technology has led to significant advancements in LST. Over the past twenty years, various satellite-derived, LST products have been released, including the Moderate Resolution Imaging Spectroradiometer (MODIS) [3], [4], the Global Land Surface Satellite (GLASS) [5], [6], and the Visible Infrared Imaging Radiometer Suite (VIIRS) [7], [8]. The use of LST products is increasingly prevalent in fields like global climate change [9], [10], food safety [11], regional urban planning [12], [13], and vegetation monitoring [14], [15]. ...
... The VIIRS instrument, known as VNP on the Suomi NPP satellite and as VJ1 on the JPSS-1 satellite, was launched in 2011 and 2017 respectively. VIIRS aims to improve the operational capabilities of the Advanced Very High-Resolution Radiometer (AVHRR) and provide overlap and continuity with MODIS observations [7]. The Level-1B products of VIIRS are categorized into six types; in the current study, the VNP/VJ103MOD product was selected to acquire brightness temperature data. ...
Land surface temperature (LST) is an important variable in Earth science research and can be measured using various thermal infrared (TIR) observations. Due to variations in data sources and inversion algorithms, LST products yield inconsistent results, further affecting subsequent applications (e.g., drought and vegetation monitoring). Although many evaluation studies have been conducted, most of them have focused on product validation or differences between inversion algorithms. There is a lack of analysis of the data sources, which is important for the data fusion. Therefore, a consistency analysis was conducted herein. Mainstream polar-orbiting satellite data were selected, including data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Sea and Land Surface Temperature Radiometer (SLSTR), and Visible Infrared Imaging Radiometer Suite (VIIRS). The same inversion algorithm (split-window) for LST was employed across all datasets, thereby ensuring that differences in satellite data were the primary factor. Following validation based on in situ measurements, the polar-orbiting LST results were intercompared and analyzed with the LST results derived from the Himawari-8 Advanced Himawari Imager (AHI) observations. The results indicated that 1) similar conclusions were obtained from the intercomparison results and the ground-based validation results, with root mean square errors (RMSEs) for intercomparison results ranging from 2.903 K to 3.353 K; 2) based on the intercomparison results, regression analysis revealed that surface temperature status, land cover and vegetation information, and angular factors had a significant impact on the evaluation results, with t tests yielding p values less than 0.05 for all of these factors; and 3) based on a decision tree analysis, the contributions of angular factor, surface temperature status, and land surface structure were 50.9%, 31.3%, and 17.8%, respectively. These findings enhance knowledge regarding the impact of different satellite data on LST inversion results and emphasize the necessity of preprocessing before the joint application of satellite data, such as angle normalization and radiometric calibration.
... The earlier interface data processing segment (IDPS) version of the VIIRS albedo EDR, operational from 2014 through 2019, provides instantaneous blue-sky-albedo, that is, the albedo measured under natural outdoor illumination (including direct solar radiation and diffuse sky irradiance) at the overpass time only over clear-sky pixels with the cloudy pixel value invalid. Many validation attempts have confirmed the validity of the VIIRS albedo EDR although regression varies with surface type (Wu et al., 2017;Zhou et al., 2016;Justice et al., 2013). A larger noise in desert albedo retrieval was reported based on the comparison with in-situ measurements (Justice et al., 2013). ...
... Many validation attempts have confirmed the validity of the VIIRS albedo EDR although regression varies with surface type (Wu et al., 2017;Zhou et al., 2016;Justice et al., 2013). A larger noise in desert albedo retrieval was reported based on the comparison with in-situ measurements (Justice et al., 2013). The application of a single BRDF for bare soil and snow surfaces has determined the inherent uncertainty in the result generated from a generic look-up table (LUT). ...
The land surface albedo product of visible infrared imaging radiometer suite (VIIRS) in National Oceanic and Atmospheric Administration (NOAA)'s operational system provides real-time, global daily mean surface albedo, which is a required parameter in the estimation of the daily shortwave net radiation budget. The global gridded VIIRS albedo product is derived from the level-2 granule surface albedo product, which is generated using a Direct Estimation Method. Special gridding and compositing algorithms were developed for aggregating the granular albedo data into the gridded albedo product. This paper describes the design and evaluation of the NOAA VIIRS gridded daily surface albedo product. The cloudy condition, retrieval path, retrieval method, and observing geometry are the criteria in deciding the priority order in the composition processing. The proposed albedo product possesses a complete spatial coverage over global land and ice surface and provides a timely response to surface dynamics. The validation of satellite retrieved daily mean albedo against ground counterparts over a series of well-maintained networks demonstrates the reliability of the composed albedo considering the interference of the seasonal surface heterogeneity conditions around each site. The inter-comparison between the S-NPP and NOAA-20 VIIRS albedo shows good agreement, except for a minor bias related to solar/view angle differences. The cross-comparison between VIIRS albedo and MODIS albedo shows good consistency with some deviations related to the controversy between their upstream snow mask.
... The VIIRS instrument on the Suomi NPP (National Polar Partnership) satellite (Justice et al., 2013) was launched in 2011. It provides a BRDF product over land. ...
The short-wave infrared (SWIR) module of the Tropospheric Monitoring Instrument (TROPOMI) on board the ESA's Sentinel-5 precursor (S5p) satellite has been very stable during its 5 years in orbit. Calibration was performed on the ground, complemented by measurements during in-flight instrument commissioning. The radiometric response and general performance of the SWIR module are monitored by on-board calibration sources. We show that after 5 years in orbit, TROPOMI-SWIR has continued to show excellent performance with degradation of at most 0.1 % in transmission and having lost less than 0.3 % of the detector pixels. Independent validation of the instrument calibration, via vicarious calibration, can be done through comparisons with ground-based reflectance data. In this work, ground measurements at the Railroad Valley Playa, a valley in central Nevada that is often used as a reference for satellite measurements, are used to perform vicarious calibration of the TROPOMI-SWIR measurements. This is done using dedicated measurement campaigns as well as automated reflectance measurements within the RADCALNET programme. As such, TROPOMI-SWIR is an excellent test case to explore the methodology of vicarious calibration applied to infrared spectroscopy. Using methodology developed for the vicarious calibration of the OCO-2 and GOSAT missions, the absolute radiometry of TROPOMI-SWIR performance is independently verified to be stable down to ∼ 6 %–10 % using the Railroad Valley when both the absolute and relative radiometric calibrations are applied. Differences with the on-board calibration originate from the bidirectional reflection distribution function (BRDF) effects of the desert surface, the large variety in viewing angles, and the different sizes of footprints of the TROPOMI pixels. Vicarious calibration is shown to be an additional valuable tool in validating radiance-level performances of infrared instruments such as TROPOMI-SWIR in the field of atmospheric composition. It remains clear that for instruments of similar design and resolution to TROPOMI-SWIR, on-board calibration sources will continue to provide superior results due to the limitations of the vicarious calibration method.
... Among these, the LAI/FPAR products from MODIS on the Terra platform have been widely used since 2000 and represent a milestone in operational generation of vegetation parameters from satellite observations (Knyazikhin, 1999;Myneni and Park, 2015;Yan et al., 2016;Yan et al., 2021c). The LAI/FPAR are also available from MODIS on the Aqua platform and VIIRS on the Suomi 50 National Polar-Orbiting Partnership (S-NPP) and the Joint Polar Satellite System (JPSS) satellites since 2002 (2012), ensuring the extension of the Terra MODIS long-term data record (Justice et al., 2013). The MODIS&VIIRS LAI/FPAR datasets have contributed significantly to many studies, such as terrestrial carbon sinks, understanding seasonal and interannual variations in equatorial forests, analyses of spatial patterns of drought, and climate and energy flux dynamics (Tang et al., 2013;Mariano et al., 2018;Chen et al., 2019;Chen et al., 2022;Sun et al., 2022). ...
Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as MODIS&VIIRS, provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial-temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a Sensor-Independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500m/5km/0.05 degrees, 8-day/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high quality retrievals, (ii) application of the spatial-temporal tensor (ST-Tensor) completion model to extrapolate LAI and FPAR beyond areas with high quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections, spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons to ground data and indirectly through reproducing results of LAI/FPAR trends documented in literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-Tensor completion model. Comparisons of SI LAI (FPAR) with ground truth data suggest a RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which are improvements of the standard Terra/Aqua/VIIRS LAI (FPAR) products by 0.02~0.19 LAI (0.01~0.02 FPAR) units with the R2 decreased by 0.02~0.16 (0.05~0.09). The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy data set than their sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy with high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agrees with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap-filling modelling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modelling and land policy development for informed decision-making and sustainable environmental management.
