Visible Infrared Imaging Radiometer Suite Observations Research Articles

Analyzing GOES-R ABI BRDF-adjusted EVI2 time series by comparing with VIIRS observations over the CONUS

The Advanced Baseline Imager (ABI) instruments onboard the Geostationary Operational Environmental Satellite-R series (GOES-R) provide new opportunities to expand their applications in global terrestrial ecology and environmental sciences. It offers a 5–15 min temporal resolution and 0.5–1 km spatial resolution that greatly benefit those environmental applications where spatial resolution can be compromised in favor of more frequent imagery. However, ABI captures reflected radiation at varying sun positions throughout the day, causing significant diurnal variations. The varying off-nadir view across ABI full disk presents further challenges for generating comparable and consistent vegetation observations, such as the two-band Enhanced Vegetation Index (EVI2). To address these challenges, three Bidirectional Reflectance Distribution Function (BRDF) models (RossThick-LiSparse-Reciprocal (RTLSR), Walthall, and Tian models) are examined to generate high-quality BRDF-adjusted EVI2 based on GeoNEX GOES-16 ABI surface reflectance. The ABI BRDF retrievals are then systematically compared to the corresponding VIIRS (Visible Infrared Imaging Radiometer Suite) Nadir BRDF-Adjusted Reflectance (NBAR) products (VNP43 V001) retrieved from RTLSR model. The viewing geometry effects are further analyzed over various land cover types using time series analysis, Pearson Correlation Coefficients (PCC), and Relative Difference (RD) between ABI and VIIRS high-quality time series inversions. The results indicate that the RTLSR model fits well the diurnal ABI observed reflectance and EVI2 values (even for the hot-spot phenomena), and outperforms the Walthall and Tian models for generating ABI high-quality BRDF retrievals with consistently low Root Mean Square Error (RMSE<0.02) and Weight of Determination (WoD < 0.5) across the CONUS. The annual proportion of ABI high-quality BRDF retrievals with a 3-day moving window using the RTLSR model is spatially and statistically comparable to that of VIIRS NBAR with a 16-day retrieval period. Compared to the time series of high-quality VIIRS NBAR EVI2, the BRDF adjusted ABI EVI2 time series still show considerable impacts of view zenith angles over evergreen forest, shrubland, and savanna pixels. However, such impacts are not significant over spatially homogeneous pixels such as cropland and deciduous forests. These results emphasize the need for BRDF correction when considering the impact of off-nadir observations from geostationary satellites on vegetation monitoring. They also demonstrate the advantages of geostationary satellites in capturing rapid changes in greenness trajectories.

A decade-long chlorophyll-a data record in lakes across China from VIIRS observations

Chlorophyll-a (Chl-a) is one of the optically active constituents in waters, and its concentration is frequently utilized as a proxy for lake trophic levels. However, generating a large-scale, long-term, and consistent data record of Chl-a in lakes from satellite images has been a challenging undertaking due to the limitations of conventional algorithms in monitoring inland waters spanning various optical properties. Here, we develop a practical deep neural network (DNN) model to generate a long-term Chl-a series (2012−2021) in 217 large lakes (> 50 km2) across China from the Visible Infrared Imaging Radiometer Suite (VIIRS) imagery. The assessment showed that the NOAA operational VIIRS remote sensing reflectance (Rrs(λ)) products were reliable over 28 of China's examined lakes (N = 340, bias = −12%, mean absolute percentage error [MAPE] = 38%), particularly at bands ranging from the green to near-infrared domain. The DNN model performed satisfactorily on Chl-a retrievals (bias = 5%, MAPE = 32%) in 79 lakes over three orders of magnitude (0.1–300 μg L−1) spanning clear/deep to turbid/shallow waters, with significant improvements compared with the existing algorithms and other machine learning algorithms. The algorithm was applied to VIIRS images to produce a data record of spatial and temporal variations in Chl-a for China's large lakes over the past decade. The VIIRS-derived data record showed that China's lakes have an average Chl-a of 9.5 μg L−1 and are to 45.5% eutrophic. The results revealed a spatial trend of lower Chl-a in the western deep lakes than that in the eastern shallow lakes. In addition, we observed a significant increase in Chl-a in the lakes of the China East Plain but a decreasing trend of Chl-a in the Tibetan Plateau. This study highlights the feasibility of a machine learning approach based on synchronous matchups to derive Chl-a data in various lakes from satellite images. Our results provide a comprehensive understanding of overall changes to the optical conditions of China's lakes and enable scientists to elucidate the roles of climate and human activities in regulating lake productivity.

Detection of Karenia brevis red tides on the West Florida Shelf using VIIRS observations: Accounting for spatial coherence with artificial intelligence

Harmful algal blooms (HABs) of the toxic dinoflagellate Karenia brevis (K. brevis) occur annually on the West Florida Shelf (WFS). Detection of these blooms using satellite observations often suffers from two problems: lack of accurate algorithms to identify phytoplankton blooms in optically complex waters and patchiness (i.e., heterogeneity) of K. brevis during blooms. Here, using data collected by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) between 2017 and 2019, we develop a practical approach to overcome these difficulties despite the lack of a chlorophyll-a fluorescence band on VIIRS. The approach is based on artificial intelligence (specifically, a deep-learning (DL) convolutional neural network model), which uses spatial coherence of bloom patches to account for the patchiness of K. brevis concentrations. After proper training, the overall performance (i.e., F1 score) of the deep learning model is 89%. Extracted K. brevis patches were consistent with those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, which has a fluorescence band. Furthermore, the wider swath of VIIRS over MODIS (3040-km versus 2330-km) led to more valid observations of bloom extent, enabling improved near-real-time applications. The results not only demonstrate the capacity of VIIRS in HABs monitoring, but also show the value of the DL model in extracting K. brevis bloom patches for both near real-time applications and retrospective analysis.

Phytoplankton biomass dynamics in the Arabian Sea from VIIRS observations

Using chlorophyll-a (Chl-a) as an indicator for phytoplankton biomass, observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2019 in combination with wind, various ocean hydrographic data, and nutrient data are used to conduct a comprehensive study to characterize and quantify the phytoplankton biomass dynamics in the Arabian Sea, and assess the mechanisms that drive the summer and winter phytoplankton blooms. Phytoplankton biomass dynamics in the Arabian Sea are driven by the seasonal reversal of the monsoon wind. The summer phytoplankton bloom located mainly in the western and central Arabian Sea is stronger than the winter phytoplankton bloom in the northern Arabian Sea in terms of both Chl - a values and the extent of phytoplankton bloom coverage. Interannual variability of Chl - a is less significant in comparison to the seasonal variability. Chl-a data measured by the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) from 1997 to 2010 are also used. The comparison of Ch-a climatology data between SeaWiFS and VIIRS shows no long-term trend of Chl-a change in the Arabian Sea. This study provides an improved understanding (with new knowledge) of (1) phytoplankton biomass dynamics, (2) physical, biological, and biogeochemical processes, and (3) nutrient dynamics in the Arabian Sea. The hydrographic and nutrient data reveal two different driving mechanisms for phytoplankton blooms in the summer and winter monsoons. During the summer monsoon, the cold high-nutrient waters are brought to the surface from the thermocline depth through strong coastal upwelling and Ekman pumping, leading to increased Chl-a at the surface-layer. The vertical velocity reaches ~1 m/day at 70 m depth. In comparison, the winter phytoplankton bloom is driven by the moderate boost of the nutrient level due to the mixed-layer deepening and strong vertical mixing caused by the surface cooling in the northern Arabian Sea. The nutrient dynamics in the Arabian Sea also suggests that the nitrate concentration is the major nutrient driver for its seasonal phytoplankton biomass dynamics. • The phytoplankton biomass dynamics was characterized and quantified. • There is no long-term trend of Chl-a change in the Arabian Sea in the last two decades. • Two different driving mechanisms for phytoplankton blooms in the summer and winter

Machine Learning Based Algorithms for Global Dust Aerosol Detection from Satellite Images: Inter-Comparisons and Evaluation

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicate that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81%, 89%, and 85% over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML algorithms to NOAA’s Aerosol Detection Product (ADP), which is a product that classifies dust, smoke, and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.

Water properties in the La Plata River Estuary from VIIRS observations

In this study, observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2018 are used to characterize and quantify water optical properties in the La Plata River Estuary (or Río de la Plata Estuary). Satellite-measured normalized water-leaving radiance spectra nLw(λ) are found to be similar with the peak at the 671 nm red band in the inner and mid estuaries, while nLw(λ) spectra peak at the 551 nm green band in the outer estuary. Remarkable seasonal and inter-annual variability are also observed in the La Plata River Estuary. The austral autumn (March–May) is found to be the most turbid season. An enhanced radiance ratio nLw(745)/nLw(671) is also found to occur during the austral autumn season in the inner and mid estuaries. The La Plata River Estuary had the most turbid water in 2012 in terms of the satellite-measured water diffuse attenuation coefficient at 490 nm Kd(490) in the inner and mid estuaries. The low Kd(490) in the inner and mid estuaries during 2015–2016 coincided with the strong 2015–2016 El Niño event. The first and second empirical orthogonal function (EOF) modes account for about 34.9% and 27.0% of the total Kd(490) variance in the La Plata River Estuary, respectively. In addition, the Kd(490) variation in the inner and mid estuaries is out of phase with that in the outer estuary. The winds, rainfall, and river discharge are attributed to the first and second EOF modes of the Kd(490) in the La Plata River Estuary.

An absorption-specific approach to examining dynamics of particulate organic carbon from VIIRS observations in inland and coastal waters

An absorption-based approach was used to determine surface particulate organic carbon (POC) concentrations in both inland and coastal waters. The improved performance of this approach was based on the specification of local POC absorption characteristics based on dominant POC sources; phytoplankton or detritus based. This specification was made using a new POC-Index (PI), developed and tested across a range of POC (300–10,000 mg/m3) conditions in temporally and spatially heterogeneous inland and coastal waterbodies. The POC model was based on remote sensing reflectance (Rrs, sr−1) in four wavebands: Rrs(751), Rrs(488) and Rrs(R/G), where R is the red band [Rrs(672)] for detritus dominated waters and G is the green band [Rrs(555)] in the phytoplankton dominated waters. The model provided a high R2 (0.74) and relatively low rRMSE (42.0%, N = 136, p < 0.01). Validation with independent datasets from Chaohu Lake and the Yangtze River Estuary produced a larger positive bias (R2 = 0.59, rRMSE = 83%, δ = 634 mg/m3, S = 0.63, I = 1439 mg/m3); nevertheless, the bias was reduced when tuned with local data (R2 = 0.80, rRMSE = 45%, δ = 72 mg/m3, S = 0.81, I = 327 mg/m3). Additionally, HydroLight simulations presented an independent correlation between PI and CDOM conditions and reasonable POC estimates from the new approach developed in this study. The approach was tested using data from Visible Infrared Imaging Radiometer Suite (VIIRS) in a range of optically complex conditions to quantify carbon dynamics. We indicate the advantages and challenges of using this approach in ecosystems where multiple organic carbon sources are present.

Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations

Real-time monitoring of crop phenology is critical for assisting farmers managing crop growth and yield estimation. In this study, we presented an approach to monitor in real time crop phenology using timely available daily Visible Infrared Imaging Radiometer Suite (VIIRS) observations and historical Moderate Resolution Imaging Spectroradiometer (MODIS) datasets in the Midwestern United States. MODIS data at a spatial resolution of 500 m from 2003 to 2012 were used to generate the climatology of vegetation phenology. By integrating climatological phenology and timely available VIIRS observations in 2014 and 2015, a set of temporal trajectories of crop growth development at a given time for each pixel were then simulated using a logistic model. The simulated temporal trajectories were used to identify spring green leaf development and predict the occurrences of greenup onset, mid-greenup phase, and maximum greenness onset using curvature change rate. Finally, the accuracy of real-time monitoring from VIIRS observations was evaluated by comparing with summary crop progress (CP) reports of ground observations from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA). The results suggest that real-time monitoring of crop phenology from VIIRS observations is a robust tool in tracing the crop progress across regional areas. In particular, the date of mid-greenup phase from VIIRS was significantly correlated to the planting dates reported in NASS CP for both corn and soybean with a consistent lag of 37 days and 27 days on average (p &lt; 0.01), as well as the emergence dates in CP with a lag of 24 days and 16 days on average (p &lt; 0.01), respectively. The real-time monitoring of maximum greenness onset from VIIRS was able to predict the corn silking dates with an advance of 9 days (p &lt; 0.01) and the soybean blooming dates with a lag of 7 days on average (p &lt; 0.01). These findings demonstrate the capability of VIIRS observations to effectively monitor temporal dynamics of crop progress in real time at a regional scale.

Prelaunch Radiometric Characterization and Calibration of the S-NPP VIIRS Sensor

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key instrument onboard the Suomi National Polar-orbiting Partnership (S-NPP) spacecraft that was launched on October 28, 2011. VIIRS is designed to provide top of the atmosphere radiometric measurements and imaging of the entire planet Earth twice daily. It is a wide-swath (3040 km) cross-track scanning radiometer with spatial resolutions of 375 and 750 m at nadir for imaging and moderate bands, respectively. It has 22 spectral bands covering the spectrum between 0.4 and 12.5 μm, including 15 reflective solar bands and 7 thermal emissive bands. VIIRS observations are used to generate 22 environmental data records used by various operational applications and for climate research. This paper describes the prelaunch radiometric calibration and characterization methodologies used by the NASA VIIRS Characterization Support Team, including performance assessments for the reflective and emissive band radiometric calibration, the signal-to-noise ratios, dual gain transition, and dynamic range. Other aspects of the sensor performance such as scattered light response, response versus scan angle, polarization sensitivity, relative spectral response, and crosstalk will also be briefly described. A comprehensive set of performance metrics generated during the prelaunch testing program will be compared to the sensor requirements, and a list of lessons learned will be presented to enhance testing and performance assessment for future Joint Polar-Orbiting Satellite System VIIRS sensors.

A fast Visible Infrared Imaging Radiometer Suite simulator for cloudy atmospheres

AbstractA fast instrument simulator is developed to simulate the observations made in cloudy atmospheres by the Visible Infrared Imaging Radiometer Suite (VIIRS). The correlated k distribution technique is used to compute the transmissivities associated with absorbing atmospheric gases. The bulk scattering properties of ice clouds are based on the ice model used for the Moderate Resolution Imaging Spectroradiometer Collection 6 ice cloud products, and those of water clouds are computed with the Lorenz‐Mie theory. Two fast radiative transfer models based on precomputed ice cloud look‐up tables are used for the VIIRS solar and infrared channels. The accuracy and efficiency of the fast simulator are quantified in comparison with a combination of the rigorous line‐by‐line (LBLRTM) and discrete ordinate radiative transfer (DISORT) models. The maximum relative errors of the simulator are less than 2% for simulated top of atmosphere reflectances at the solar channels, and the brightness temperature differences for the infrared channels are less than 0.2 K. The simulator is over 3 orders of magnitude faster than the benchmark LBLRTM + DISORT model. Furthermore, the cloudy atmosphere reflectances and brightness temperatures from the fast VIIRS simulator compare favorably with those from VIIRS observations.