Visible Infrared Imaging Radiometer Suite Data Research Articles

Assessment of cross-sensor vegetation index compatibility between VIIRS and MODIS using near-coincident observations

Cross-sensor compatibility of spectral vegetation indices (VIs) between Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) was investigated using their near-coincident observation pairs obtained along overlapped orbital tracks across the globe for the year 2015. The “top-of-atmosphere (TOA)” and “top-of-canopy (TOC)” normalized difference vegetation indices (NDVIs), TOC-enhanced vegetation index (EVI), and TOC two-band EVI (EVI2) were investigated. For all four VIs, VIIRS and MODIS VIs were subject to systematic differences in which VIIRS VIs were higher than their MODIS counterparts. The overall systematic differences and uncertainties (measured as mean differences and root mean square differences, respectively) were small (0.010 to 0.020 VI units and 0.015 to 0.022 VI units, respectively). TOA NDVI cross-sensor differences were neither seasonally nor view zenith angle dependent, whereas TOC NDVI cross-sensor differences slightly varied seasonally, but were not view zenith angle dependent. TOC EVI and TOC EVI2 cross-sensor differences were view zenith angle dependent, where systematic differences increased with increasing view zenith angle and, for large view zenith angles, they were higher during the summer seasons. These results support the normalization of view zenith angles as a required step to extend the MODIS VI record with VIIRS data.

Intercomparison of MODIS AQUA and VIIRS I-Band Fires and Emissions in an Agricultural Landscape-Implications for Air Pollution Research.

Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19–91.19 Gg compared to 42.0–61.71 Gg, 42.59–58.75 Gg and 93.98–111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003–2016. The results suggested a range of 2.56–63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery

Land surface phenology (LSP) has been widely retrieved from time series of various satellite instruments in order to monitor climate change and ecosystem dynamics. However, any evaluation of the quality of LSP data sets is quite challenging because the in situ observations on a limited number of individual trees, shrubs, or other plants are rarely representative of the landscape sampled in a single satellite pixel. Moreover, vegetation indices detecting biophysical features of vegetation seasonality are different from (but related to) the specific plant life history stages observed by humans at ground level. This study is the first comprehensive evaluation of the LSP product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) data using both MODIS LSP products and observations from the PhenoCam network across the Contiguous United States during 2013 and 2014. PhenoCam observes vegetation canopy over a landscape at very high frequency, providing nearly continuous canopy status and enabling the estimate of discrete phenophase using vegetation indices that are conceptually similar to satellite data. Six phenological dates (greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset) were retrieved separately from daily VIIRS NDVI (normalized difference vegetative index) and EVI2 (two-band enhanced vegetation index) time series. Similarly, the six phenological dates were also extracted from the 30-min time series of PhenoCam data using GCC (green chromatic coordinate) and VCI (vegetation contrast index) separately. Phenological dates derived from VIIRS NDVI and EVI2 and PhenoCam GCC and VCI were generally comparable for the vegetation greenup phase, but differed considerably for the senescence phase. Although all indices captured green leaf development effectively, performance discrepancies arose due to their ability to track the mixture of senescing leaf colors. PhenoCam GCC and VCI phenological observations were in better agreement with the phenological dates from VIIRS EVI2 than from VIIRS NDVI. Further, the VIIRS EVI2 phenological metrics were more similar to those from PhenoCam VCI than from PhenoCam GCC time series. Overall, the average absolute difference between the VIIRS EVI2 and PhenoCam VCI phenological dates was 7–11 days in the greenup phase and 10–13 days in the senescence phase. The difference was smaller in forests, followed by grasslands and croplands, and then savannas. Finally, the phenological dates derived from VIIRS EVI2 were compared with MODIS detections, which showed a good agreement with an average absolute difference less than a week except for the senescence onset. These results for the first time demonstrate the upper boundary of uncertainty in VIIRS LSP detections and the continuity to MODIS LSP product.

Estimation of High Spatial-Resolution Clear-Sky Land Surface-Upwelling Longwave Radiation from VIIRS/S-NPP Data

Surface-upwelling longwave radiation (LWUP) is an important component of the surface radiation budget. Under the general framework of the hybrid method, the linear models and the multivariate adaptive regression spline (MARS) models are developed to estimate the 750 m instantaneous clear-sky LWUP from the top-of-atmosphere (TOA) radiance of the Visible Infrared Imaging Radiometer Suite (VIIRS) channels M14, M15, and M16. Comprehensive radiative transfer simulations are conducted to generate a huge amount of representative samples, from which the linear model and the MARS model are derived. The two models developed are validated by the field measurements collected from seven sites in the Surface Radiation Budget Network (SURFRAD). The bias and root-mean-square error (RMSE) of the linear models are −4.59 W/m2 and 16.15 W/m2, whereas those of the MARS models are −5.23 W/m2 and 16.38 W/m2, respectively. The linear models are preferable for the production of the operational LWUP product due to its higher computational efficiency and acceptable accuracy. The LWUP estimated by the linear models developed from VIIRS is compared to that retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS). They agree well with each other with bias and RMSE of −0.15 W/m2 and 25.24 W/m2 respectively. This is the first time that the hybrid method has been applied to globally estimate clear-sky LWUP from VIIRS data. The good performance of the developed hybrid method and consistency between VIIRS LWUP and MODIS LWUP indicate that the hybrid method is promising for producing the long-term high spatial resolution environmental data record (EDR) of LWUP.

Estimation of land surface heat fluxes based on visible infrared imaging radiometer suite data: case study in northern China

Evapotranspiration (ET) plays an important role in surface–atmosphere interactions and can be monitored using remote sensing data. The visible infrared imaging radiometer suite (VIIRS) sensor is a generation of optical satellite sensors that provide daily global coverage at 375- to 750-m spatial resolutions with 22 spectral channels (0.412 to 12.05 μm) and capable of monitoring ET from regional to global scales. However, few studies have focused on methods of acquiring ET from VIIRS images. The objective of this study is to introduce an algorithm that uses the VIIRS data and meteorological variables to estimate the energy budgets of land surfaces, including the net radiation, soil heat flux, sensible heat flux, and latent heat fluxes. A single-source model that based on surface energy balance equation is used to obtain surface heat fluxes within the Zhangye oasis in China. The results were validated using observations collected during the HiWATER (Heihe Watershed Allied Telemetry Experimental Research) project. To facilitate comparison, we also use moderate resolution imaging spectrometer (MODIS) data to retrieve the regional surface heat fluxes. The validation results show that it is feasible to estimate the turbulent heat flux based on the VIIRS sensor and that these data have certain advantages (i.e., the mean bias error of sensible heat flux is 15.23 W m−2) compared with MODIS data (i.e., the mean bias error of sensible heat flux is −29.36 W m−2). Error analysis indicates that, in our model, the accuracies of the estimated sensible heat fluxes rely on the errors in the retrieved surface temperatures and the canopy heights.

Comparisons of global land surface seasonality and phenology derived from AVHRR, MODIS, and VIIRS data

AbstractLand surface seasonality has been widely investigated from satellite observations for monitoring the dynamics of terrestrial ecosystems in response to climate change. A great deal of efforts has focused on the characterization of interannual variation and long‐term trends of vegetation phenological metrics derived from the advanced very high resolution radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data across regional and global scales. Recently, Visible Infrared Imaging Radiometer Suite (VIIRS) data have become available for the detections of global land surface phenology. These data sets provide us a potential opportunity to generate a consistent long‐term climate data record of land surface phenology. However, to bridge the multiple‐sensor‐based phenology measurements, we need to understand their consistency and discrepancy across various geographical and ecological regions. To this end, we collected daily AVHRR, MODIS, and VIIRS data (~5 km) globally, compared their temporal trajectories of two band enhanced vegetation index (EVI2) during 2013 and 2014, examined their discrepancy of phenology retrievals, and explored the influence of EVI2 data quality from the three satellite sensors on phenology detections. The results revealed the similarity and discrepancy of EVI2 time series and land surface phenology retrievals globally. Specifically, EVI2 time series from VIIRS and MODIS observations were similar with some discrepancies mainly arising from unsystematic impact factors. In contrast, VIIRS EVI2 was systematically higher than AVHRR EVI2, in which their differences could be greatly reduced by intersensor calibration. Further, the quality of EVI2 time series among AVHRR, MODIS, and VIIRS varied largely across the globe, which was generally better in Northern Hemisphere than in Southern Hemisphere. Their differences in EVI2 data quality led to the inconsistencies in the detections of phenological dates. On average, the absolute difference of phenological transition dates among the three products was larger than 10 days in about 30% of pixels in Northern Hemisphere and more than 40% in Southern Hemisphere.

Satellite retrievals of Karenia brevis harmful algal blooms in the West Florida shelf using neural networks and impacts of temporal variabilities

We apply a neural network (NN) technique to detect/track Karenia brevis harmful algal blooms (KB HABs) plaguing West Florida shelf (WFS) coasts from Visible-Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previously KB HABs detection primarily relied on the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite, depending on its remote sensing reflectance signal at the 678-nm chlorophyll fluorescence band (Rrs678) needed for normalized fluorescence height and related red band difference retrieval algorithms. VIIRS, MODIS-A’s successor, does not have a 678-nm channel. Instead, our NN uses Rrs at 486-, 551-, and 671-nm VIIRS channels to retrieve phytoplankton absorption at 443 nm (aph443). The retrieved aph443 images are next filtered by applying limits, defined by (i) low Rrs551-nm backscatter and (ii) a minimum aph443 value associated with KB HABs. The filtered residual images are then converted to show chlorophyll-a concentrations [Chla] and KB cell counts. VIIRS retrievals using our NN and five other retrieval algorithms were compared and evaluated against numerous in situ measurements made over the four-year 2012 to 2016 period, for which VIIRS data are available. These comparisons confirm the viability and higher retrieval accuracies of the NN technique, when combined with the filtering constraints, for effective detection of KB HABs. Analysis of these results as well as sequential satellite observations and recent field measurements underline the importance of short-term temporal variabilities on retrieval accuracies.

Evaluation of aerosol optical depth and aerosol models from VIIRS retrieval algorithms over North China Plain.

The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical depth (AOD). The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. The "MODIS-like" VIIRS data (VIIRS_ML) are being produced experimentally at NASA, from a version of the "dark-target" algorithm that is applied to MODIS. In this study, the AOD and aerosol model types from these two VIIRS retrieval algorithms over the North China Plain (NCP) are evaluated using the ground-based CE318 Sunphotometer (CE318) measurements during 2 May 2012 - 31 March 2014 at three sites. These sites represent three different surface types: urban (Beijing), suburban (XiangHe) and rural (Xinglong). Firstly, we evaluate the retrieved spectral AOD. For the three sites, VIIRS_EDR AOD at 550 nm shows a positive mean bias (MB) of 0.04-0.06 and the correlation of 0.83-0.86, with the largest MB (0.10-0.15) observed in Beijing. In contrast, VIIRS_ML AOD at 550 nm has overall higher positive MB of 0.13-0.14 and a higher correlation (0.93-0.94) with CE318 AOD. Secondly, we evaluate the aerosol model types assumed by each algorithm, as well as the aerosol optical properties used in the AOD retrievals. The aerosol model used in VIIRS_EDR algorithm shows that dust and clean urban models were the dominant model types during the evaluation period. The overall accuracy rate of the aerosol model used in VIIRS_ML over NCP three sites (0.48) is higher than that of VIIRS_EDR (0.27). The differences in Single Scattering Albedo (SSA) at 670 nm between VIIRS_ML and CE318 are mostly less than 0.015, but high seasonal differences are found especially over the Xinglong site. The values of SSA from VIIRS_EDR are higher than that observed by CE318 over all sites and all assumed aerosol modes, with a positive bias of 0.02-0.04 for fine mode, 0.06-0.12 for coarse mode and 0.03-0.05 for bi-mode at 440nm. The overestimation of SSA but positive AOD MB of VIIRS_EDR indicate that other factors (e.g. surface reflectance characterization or cloud contamination) are important sources of error in the VIIRS_EDR algorithm, and their effects on aerosol retrievals may override the effects from non-ideality in these aerosol models.

Quantifying the mass loading of particles in an ash cloud remobilized from tephra deposits on Iceland

Abstract. On 16–17 September 2013 strong surface winds over tephra deposits in southern Iceland led to the resuspension and subsequent advection of significant quantities of volcanic ash. The resulting resuspended ash cloud was transported to the south-east over the North Atlantic Ocean and, due to clear skies at the time, was exceptionally well observed in satellite imagery. We use satellite-based measurements in combination with radiative transfer and dispersion modelling to quantify the total mass of ash resuspended during this event. Typically ash clouds from explosive eruptions are identified in satellite measurements from a negative brightness temperature difference (BTD) signal; however this technique assumes that the ash resides at high levels in the atmosphere. Due to a temperature inversion in the troposphere over southern Iceland during 16 September 2013, the resuspended ash cloud was constrained to altitudes of < 2 km a.s.l. We show that a positive BTD signal can instead be used to identify ash-containing pixels from satellite measurements. The timing and location of the ash cloud identified using this technique from measurements made by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite agree well with model predictions using the dispersion model NAME (Numerical Atmospheric-dispersion Modelling Environment). Total column mass loadings are determined from the VIIRS data using an optimal estimation technique which accounts for the low altitude of the resuspended ash cloud and are used to calibrate the emission rate in the resuspended ash scheme in NAME. Considering the tephra deposits from the recent eruptions of Eyjafjallajökull and Grímsvötn as the potential source area for resuspension for this event, we estimate that ∼ 0.2 Tg of ash was remobilized during 16–17 September 2013.

Real-time and short-term predictions of spring phenology in North America from VIIRS data

Real-time prediction of vegetation phenology is critical for assisting crop monitoring, natural resource management, and land modeling in weather prediction systems. However, due to the lack of timely available satellite datasets and the inherent noise in time series, little attention has been paid to real-time and short-term predictions of vegetation phenology. The successful launch of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard operational Suomi National Polar-orbiting Partnership (Suomi NPP) satellite makes this research possible because it can provide land surface observations in a timely fashion. This study introduces an operational system that provides real-time and short-term predictions of vegetation phenology. Specifically, the system integrates timely available VIIRS observations and the climatology (expectation and standard deviation) of vegetation phenology from long-term MODIS data to simulate a set of potential temporal trajectories of greenness development at a given time for each pixel. These potential trajectories are then applied to identify spring green leaf development in real time, predict the occurrence of greenup onset, mid-greenup phase and maturity onset, and analyze the uncertainty of the prediction across a variety of ecosystems in North America. The accuracy of real-time and short-term predictions was evaluated by comparing with standard VIIRS detections and near-surface PhenoCam observations in both 2014 and 2015 across North America. The results showed that the real-time prediction of spring phenological metrics from VIIRS were all significantly correlated with those derived from PhenoCam datasets (R2>0.96, P<0.01) and closely comparable to the standard VIIRS detections with a mean absolute difference of <10days, 5days and 5days in greenup onset, mid-greenup phase and maturity onset, respectively. The mean absolute difference in the northern region for all three events was relatively smaller than that in the southern region. These findings demonstrate the capability of VIIRS observations to effectively predict temporal dynamics of vegetation phenology in real time at a continental scale.

Downscaling land surface temperature data by fusing Suomi NPP-VIIRS and landsat-8 TIR data

ABSTRACTLand surface temperature (LST) is a key parameter of great interest in many remote sensing applications. However, no single satellite system can produce thermal infrared (TIR) images at both high spatial and temporal resolution to retrieve LST. Various algorithms have been developed to enhance the spatial or temporal resolution of TIR data in the past decades. Among them, the Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT) model is one of the most widely used algorithms for fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. To our knowledge, Visible Infrared Imaging Radiometer Suite (VIIRS) TIR data have not yet been used in thermal downscaling with Landsat-8 TIR data. This study aims to generate daily LST images at Landsat-8 resolution (100 m) by fusing VIIRS and Landsat-8 TIR data for the first time with the SADFAT algorithm. The results indicate that the prediction accuracy for the study area ranged from 1.1 K to 1.4 K, which suggests that VIIRS data can be used as a good alternative for MODIS data for generating daily LST images by fusing Landsat TIR data.

Seasonal variations in aerosol optical thickness over eastern China determined from VIIRS data and ground measurements

ABSTRACTTwo-year records of Visible Infrared Imaging Radiometer Suite (VIIRS) Intermediate Product (IP) data on aerosol optical thickness (AOT) at 550 nm were evaluated by comparing them to sun-sky radiometer measurements from the Chinese Sun Hazemeter Network (CSHNET) and the Aerosol Robotic Network (AERONET). The monthly and seasonal variations in the aerosol optical properties over eastern China were then investigated using collocated VIIRS IP data and CSHNET and AERONET measurements. Between 59.2 and 62.1% of the retrieved VIIR IP values fell within the range defined for MODIS (Moderate Resolution Imaging Spectroradiometer) and had similar values for the correlation coefficient (0.880–0.909). The performances of the current VIIRS IP AOT retrievals at the provisional stage were consistent with ground measurements. However, over urban areas, the VIIRS exhibits more scatter and therefore less precision than observed over marine and rural areas. Similar characteristics of seasonal and monthly variations were found among the measurements, though the observational methodologies were different, showing maxima in the summer and spring and minima in the winter and autumn. Although the intensities vary from season to season, the spatial AOT distribution patterns did not change. High-AOT centres were situated in the industrialized coastal regions of China and were related to the distribution of urban areas.

Radiometric Inter-Calibration between Himawari-8 AHI and S-NPP VIIRS for the Solar Reflective Bands

The Advanced Himawari Imager (AHI) on-board Himawari-8, which was launched on 7 October 2014, is the first geostationary instrument housed with a solar diffuser to provide accurate onboard calibrated data for the visible and near-infrared (VNIR) bands. In this study, the Ray-matching and collocated Deep Convective Cloud (DCC) methods, both of which are based on incidently collocated homogeneous pairs between AHI and Suomi NPP (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), are used to evaluate the calibration difference between these two instruments. While the Ray-matching method is used to examine the reflectance difference over the all-sky collocations with similar viewing and illumination geometries, the near lambertian collocated DCC pxiels are used to examine the difference for the median or high reflectance scenes. Strong linear relationships between AHI and VIIRS can be found at all the paired AHI and VIIRS bands. Results of both methods indicate that AHI radiometric calibration accuracy agrees well with VIIRS data within 5% for B1-4 and B6 at mid and high reflectance scenes, while AHI B5 is generally brighter than VIIRS by ~6%–8%. No apparent East-West viewing angle dependent calibration difference can be found at all the VNIR bands. Compared to the Ray-matching method, the collocated DCC method provides less uncertainty of inter-calibration results at near-infrared (NIR) bands. As AHI has similar optics and calibration designs to the GOES-R Advanced Baseline Imager (ABI), which is currently scheduled to launch in fall 2016, the on-orbit AHI data provides a unique opportunity to develop, test and examine the cal/val tools developed for ABI.

Methods for Global Survey of Natural Gas Flaring from Visible Infrared Imaging Radiometer Suite Data

A set of methods are presented for the global survey of natural gas flaring using data collected by the National Aeronautics and Space Administration/National Oceanic and Atmospheric Administration NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The accuracy of the flared gas volume estimates is rated at ±9.5%. VIIRS is particularly well suited for detecting and measuring the radiant emissions from gas flares through the collection of shortwave and near-infrared data at night, recording the peak radiant emissions from flares. In 2012, a total of 7467 individual flare sites were identified. The total flared gas volume is estimated at 143 (±13.6) billion cubic meters (BCM), corresponding to 3.5% of global production. While the USA has the largest number of flares, Russia leads in terms of flared gas volume. Ninety percent of the flared gas volume was found in upstream production areas, 8% at refineries and 2% at liquified natural gas (LNG) terminals. The results confirm that the bulk of natural gas flaring occurs in upstream production areas. VIIRS data can provide site-specific tracking of natural gas flaring for use in evaluating efforts to reduce and eliminate routine flaring.

Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March–April–May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ~ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

An algorithm for retrieving land surface temperatures using VIIRS data in combination with multi-sensors.

A practical algorithm was proposed to retrieve land surface temperature (LST) from Visible Infrared Imager Radiometer Suite (VIIRS) data in mid-latitude regions. The key parameter transmittance is generally computed from water vapor content, while water vapor channel is absent in VIIRS data. In order to overcome this shortcoming, the water vapor content was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The analyses on the estimation errors of vapor content and emissivity indicate that when the water vapor errors are within the range of ±0.5 g/cm2, the mean retrieval error of the present algorithm is 0.634 K; while the land surface emissivity errors range from −0.005 to +0.005, the mean retrieval error is less than 1.0 K. Validation with the standard atmospheric simulation shows the average LST retrieval error for the twenty-three land types is 0.734 K, with a standard deviation value of 0.575 K. The comparison between the ground station LST data indicates the retrieval mean accuracy is −0.395 K, and the standard deviation value is 1.490 K in the regions with vegetation and water cover. Besides, the retrieval results of the test data have also been compared with the results measured by the National Oceanic and Atmospheric Administration (NOAA) VIIRS LST products, and the results indicate that 82.63% of the difference values are within the range of −1 to 1 K, and 17.37% of the difference values are within the range of ±2 to ±1 K. In a conclusion, with the advantages of multi-sensors taken fully exploited, more accurate results can be achieved in the retrieval of land surface temperature.

Method for detecting cloud at night from VIIRS data based on DNB

Validating the cloud detection result at night and distinguishing low cloud from fog through satellite data are difficult in southern mountain areas of China. In this paper,a method is presented by analyzing the new features of the visible infrared imaging radiometer suite( VIIRS) sensor data and the theory of the cloud detection.The viability of VIIRS day and night( DNB) data in night cloud detection is discussed in detail and the result shows that the DNB data can be used to validate the result when lunar zenith angle is less than 60°. The application and validation show that the method is effective,and the estimation accuracy is higher than 91% when scan angle is less than 15°,and the BTM12- BTM13 and BTM12- BTM15 can be used to effectively distinguish low clouds and fog.In addition,the detection thresholds are sensitive to the sensor zenith angle,and the detection accuracy is higher when the sensor zenith angle is small.

Direct estimation of land surface albedo from VIIRS data: Algorithm improvement and preliminary validation

AbstractLand surface albedo (LSA), part of the Visible Infrared Imaging Radiometer Suite (VIIRS) surface albedo environmental data record (EDR), is an essential variable regulating shortwave energy exchange between the land surface and the atmosphere. Two sub‐algorithms, the dark pixel sub‐algorithm (DPSA) and the bright pixel sub‐algorithm (BPSA), were proposed for retrieving LSA from VIIRS data. The BPSA estimates LSA directly from VIIRS top‐of‐atmosphere (TOA) reflectance through simulation of atmospheric radiative transfer. Several changes have been made to improve the BPSA since the deployment of VIIRS. A database of the Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) is collected and converted to bidirectional reflectance at VIIRS bands. The converted reflectance is then used as input to the atmospheric radiative transfer model to generate a look‐up table (LUT) of regression coefficients with consideration of surface BRDF. Before its implementation in the operational system, the new BPSA is tested on the local infrastructure. The incorporation of the surface BRDF improves the accuracy of LSA estimation and reduces the temporal variation of LSA over stable surfaces. VIIRS LSA retrievals agree well with the MODIS albedo products. Comparison with field measurements at seven Surface Radiation (SURFRAD) Network sites shows that VIIRS LSA retrieved from the LUT with surface BRDF has an R2 value of 0.80 and root mean square error of 0.049, better than MODIS albedo products. The VIIRS results have a slight negative bias of 0.004, whereas the MODIS albedo is underestimated with a larger negative bias of 0.026.

Evaluation of the VIIRS ocean color monitoring performance in coastal regions

Ocean color (OC) remote sensing has entered a new phase with the successful deployment of the Visible Infrared Imager Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The representativeness and accuracy of the VIIRS geophysical products need to be assessed before a wide use of these data by the scientific community. As an integral part of the VIIRS sensor calibration and validation efforts, our group has been continuously monitoring the validity of the VIIRS's OC and atmospheric data stream through time-series in-situ data acquired at the observatory sites which are part of the AERONET-OC network. This paper addresses the preliminary evaluations of the VIIRS sensor's performance for retrieving OC data of typical coastal water environments, by carrying out time-series, as well as qualitative and quantitative match-up comparisons analysis between in-situ and satellite retrieved OC data. Initial time-series match-up comparisons carried out for a year period (January to December, 2012) show that VIIRS data exhibits strong temporal and statistical agreements with AERONET-OC data demonstrating a potential in enhanced coastal water monitoring from space. VIIRS data of two NASA-OBPG processing schemes which apply different vicarious calibration gains and NOAA-IDPS system are analyzed based on in-situ data of LISCO and WaveCIS AERONET-OC sites which are located in Long Island Sound and Gulf of Mexico respectively as well as OC retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Aqua satellite. The underlying cause of the discrepancies observed in VIIRS retrieved normalized water-leaving radiances is also investigated. Finally, as the NASA-OBPG and NOAA-IDPS processing schemes for ocean color data of the VIIRS sensor continue to evolve, the results underline the necessity for monitoring and assessing the validity and consistency of VIIRS' ocean color products, especially for coastal waters.

Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect

Grasslands and savannas form a heterogeneous and patchy mosaic of spectral properties that are challenging for characterization of vegetation states. This study examines the potential for use of suites of vegetation indices (VIs) from the proposed Sentinel 2 sensor to describe vegetation states in grasslands and savannas for a North American transect. Hyperion hyperspectral data from the EO-1 satellite were used to simulate Sentinel 2, MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) images for field sites in Alberta, North Dakota and Texas that represent the continuum from short grass prairie to oak savanna and are intermingled with agriculture. Indices representing photosynthetic pigments (Normalized Difference Vegetation Index, Carotenoid Reflectance Index, Anthocyanin Reflectance Index and Red-Green Ratio), vegetation and landscape water content (Normalized Difference Infrared Index), senescent vegetation and soil (Short Wave Infrared Ratio and Plant Senescence Reflectance Index) and herbaceous biomass (Soil Adjusted Total Vegetation Index) were used. There were distinct differences among sites in the relative sensitivity of different VIs depending upon moisture status, tree cover and type of grassland. Simple multi-variate models based on mean values of VIs showed limited ability to predict land cover classes and nominal vegetation states. However, analysis of sample areas using pixels as individual observations within a statistical distribution indicated that subtle variation and gradients within management or land units could be used to characterize fine differences in selected nominal states at each site. Despite some differences in band locations, all VIs except the anthocyanin reflectance index were scalable between Sentinel 2 and MODIS and VIIRS data. A framework for using suites of VIs as indicators of vegetation states that could be applied to the state and transition model approach applied by the US Natural Resource Conservation Service is described. Land types can be effectively characterized by pixel value distribution histograms, and statistical metrics may be used as indicators of status and change. However, time series are needed to fully capture states and state changes, since grasslands and savannas have such high levels of spectral and phenological variation.