Geostationary Satellite Sensor Research Articles

Turning Night into Day: The Creation and Validation of Synthetic Nighttime Visible Imagery Using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) and Machine Learning

Abstract Meteorological satellite imagery is a critical asset for observing and forecasting weather phenomena. The Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) sensor collects measurements from moonlight, airglow, and artificial lights. DNB radiances are then manipulated and scaled with a focus on digital display. DNB imagery performance is tied to the lunar cycle, with the best performance during the full moon and the worst with the new moon. We propose using feed-forward neural network models to transform brightness temperatures and wavelength differences in the infrared spectrum to a pseudo-lunar reflectance value based on lunar reflectance values derived from observed DNB radiances. JPSS NOAA-20 and Suomi National Polar-Orbiting Partnership (SNPP) satellite data over the North Pacific Ocean at night for full moon periods from December 2018 to November 2020 were used to design the models. The pseudo-lunar reflectance values are quantitatively compared to DNB lunar reflectance, providing the first-ever lunar reflectance baseline metrics. The resulting imagery product, Machine Learning Nighttime Visible Imagery (ML-NVI), is qualitatively compared to DNB lunar reflectance and infrared imagery across the lunar cycle. The imagery goal is not only to improve upon the consistent performance of DNB imagery products across the lunar cycle, but ultimately to lay the foundation for transitioning the algorithm to geostationary sensors, making global continuous nighttime imagery possible. ML-NVI demonstrates its ability to provide DNB-derived imagery with consistent contrast and representation of clouds across the full lunar cycle for nighttime cloud detection. Significance Statement This study explores the creation and evaluation of a feed-forward neural network to generate synthetic lunar reflectance values and imagery from VIIRS infrared channels. The model creates lunar reflectance values typical of full moon scenes, enabling quantifiable comparisons for nighttime imagery evaluations. Additionally, it creates imagery that highlights low clouds better than its infrared counterparts. Results indicate the ability to create visually consistent nighttime visible imagery across the full lunar cycle for the improved nighttime detection of low clouds. Wavelengths chosen are available on both polar and geostationary satellite sensors to support the utilization of the algorithm on multiple sensor platforms for improved temporal resolution and greater simultaneous geographic coverage over polar orbiters alone.

Automatic mapping of 500 m daily open water body fraction in the American continent using GOES-16 ABI imagery

Timely and large-area monitoring of terrestrial water bodies using remote sensing data is essential to protect water resource security for humans and ecosystems. Although efforts have been made to monitor inter-monthly water bodies at the regional, continental and global scales, surface open water bodies may experience rapid changes within a short duration. At the same time, the data gaps caused by the long revisit frequencies of satellite sensor imaging systems and the extremely cloudy and rainy weather in certain areas mean that a suitable operational method for recording such rapid changes over large areas does not yet exist. The recently launched operational geostationary satellite sensor GOES-16 ABI can provide multi-scale optical images every 10 min in the Americas, and it has great potential to provide a new solution for timely open surface water monitoring. In this research, a framework based on a multi-scale non-linear mixture model (NLMM) was proposed for the automatic estimation of 500 m daily open water body fraction maps across the full extent of the American continent from GOES-16 ABI imagery. Specifically, an exterior spectral library was created firstly from many pairs of training GOES-16 ABI images and 500 m water body fraction maps derived from Landsat images, and an inherited interior pure spectral library was then derived from a pair of the testing GOES-16 ABI image and the corresponding pure water and non-water (i.e., vegetation and soil/rock substrate) fractions extracted from the monthly water recurrence dataset. Furthermore, a synthetic interior mixed spectral library was built finally based on the linear and non-linear mixtures of randomly selected pure endmembers of water and non-water (i.e., vegetation and soil/rock substrate) in the testing image. All the exterior and interior pure and synthetic mixed spectral libraries were employed jointly in the NLMM machine learning method for estimating daily water body fractions with GOES-16 ABI images. Experiments show that the integration of all three different spectral libraries produced 500 m open water body fraction maps with the greatest accuracies. Compared to traditional linear spectral unmixing and a water index thresholding method, the proposed method produced more accurate results both quantitatively and visually. Moreover, the proposed method can produce time-series daily 500 m open water body fraction maps with minimal cloud coverage for the full extent of the Americas by combining dense GOES-16 ABI images at half-hour intervals, opening up great possibilities for near real-time rapid open water body changes monitoring, such as flooding and drought, over large areas, even with a high frequency of cloud cover.

Contributions of meteorology and nutrient to the surface cyanobacterial blooms at different timescales in the shallow eutrophic Lake Taihu

Quantitative assessments of the contributions of various environmental factors to cyanobacterial blooms at different timescales are lacking. Here, the hourly cyanobacterial bloom intensity (CBI) index, a proxy for the intensity of surface cyanobacterial biomass, was obtained from the geostationary satellite sensor Geostationary Ocean Color Imager (GOCI) over the years 2011–2018. Generalized additive model was applied to determine the responses of monthly and hourly CBI to the perturbations of meteorological factors, water stability and nutrients, with variation partitioning analysis used to analyze the relative importance of the three groups of variables to the inter-monthly variation of diurnal CBI in each season. The effects of environmental factors on surface cyanobacterial blooms varied at different timescales. Hourly CBI increased with increasing air temperature up to 18 °C but decreased sharply above 18 °C, whereas monthly CBI increased with increasing air temperature up to 30 °C and stabilized thereafter. Among all the environmental factors, air temperature had the largest contribution to the intra-daily variation in CBI; water stability had the highest explanation rate for the inter-monthly variation of diurnal CBI during summer (42.3 %) and autumn (56.9 %); total phosphorus explained the most variation in monthly CBI (18.5 %). Compared with cyanobacterial biomass (CB) in the water column, high light and low wind speed caused significantly lower CBI in July and higher CBI in November respectively. Interestingly, cyanobacterial blooms at the hourly scale were aggravated by climate warming during winter and spring but inhibited during summer and autumn. Collectively, this study reveals the effects of environmental factors on surface cyanobacterial blooms at different timescales and suggests the consideration of the hourly effect of air temperature in short-term predictions of cyanobacterial blooms.

Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability

Concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations.

A practical method for angular normalization of global MODIS land surface temperature over vegetated surfaces

Land surface temperature (LST) is an essential physical quantity in surface energy balance and a good indicator of exchange of heat and water between the land and atmosphere at regional and global scales. However, the thermal-infrared remotely sensed LST from large swath-width polar-orbiting and geostationary satellite sensors is usually subject to the angular anisotropy, bringing great uncertainties in the application of LST products in many fields such as evapotranspiration estimation, urban thermal environment monitoring, and climate change study. In this study, a practical angular normalization method was developed for the first time for correcting global MODIS off-nadir LST to nadir over vegetated surfaces, using MODIS (Terra + Aqua) products from 2000 to 2020, simultaneous and collocated observations at 213 sites worldwide from the AmeriFlux, FLUXNET, and National Tibetan Plateau/Third Pole Environment Data Center (TPDC), and ERA5-Land reanalysis datasets. Results showed that the angular normalization method could well correct the angular anisotropy of MODIS LST products. Validated against datasets at 213 sites, the angular normalization model showed root mean square errors (RMSE) of 1.57 K, mean bias errors (MBE) of 0 K, and coefficient of determination (R2) of 0.99. In the cross-validation of nadir LST by correcting global MODIS/Terra off-nadir LST products against Sentinel-3A nadir LST products over four typical days in 2020 (February 26, June 1, August 12 and November 16), the angular normalization model presented the RMSE of 2.26 K and the MBE of −1.20 K, in contrast to the RMSE of 3.01 K and the MBE of −2.15 K when MODIS off-nadir LST was compared to the Sentinel-3A nadir LST. The angular anisotropy of global MODIS/Terra LST over vegetated surfaces for the four days of interest ranged between −1.79 to −1.30 K (1% quantile) and 3.64 to 4.37 K (99% quantile), with a median of 0.33 to 0.74 K, and a mean of 0.46 to 0.89 K. The developed angular normalization model is a promising approach to operationally correcting all time-series of global MODIS/Terra and MODIS/Aqua LST products with high accuracies, which is significant to provide a benchmark of angle-consistent MODIS LST product for its further application.

High-frequency monitoring of Secchi-disk depth in Taihu Lake using Himawari-8/AHI data.

Diurnal monitoring of the Secchi-disk depth (SDD) of eutrophic lakes is the basic requirement to ensure domestic, industrial, and agricultural water use in surrounding cities. The retrieval of SDD in high frequency and longer observation period is the basic monitoring requirement to guarantee water environmental quality. Taking Lake Taihu as an example, the diurnal high-frequency observation (10 mins) data of the geostationary meteorological satellite sensor AHI/Himawari-8 were examined in this study. The results showed that the AHI normalized water-leaving radiance (Lwn) product derived by the Shortwave-infrared atmospheric correction (SWIR-AC) algorithm was consistent with the in situ data, with determination coefficient (R2) all larger than 0.86 and the mean absolute percentage deviation (MAPD) of 19.76%, 12.83%, 19.03% and 36.46% for the 460 nm, 510 nm, 640 nm and 860 nm bands, respectively. 510 nm and 640 nm bands showed more better consistency with in situ data in Lake Taihu. Therefore, an empirical SDD algorithm was established based on the AHI green (510 nm) and red (640 nm) bands. The SDD algorithm was verified by in situ data showed good performance with R2 of 0.81, RMSE of 5.91 cm, and MAPD of 20.67%. Based on the AHI data and established algorithm, diurnal high-frequency variation of the SDD in the Lake Taihu was investigated and the environmental factor (wind speed, turbidity degree, and photosynthetically active radiance) corresponding to diurnal SDD variation were discussed. This study should be helpful for studying diurnal high-dynamics physical-biogeochemical processes in eutrophication lake waters.

Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II

To produce real-time ground-level information on particulate matter with a diameter equal to or less than 2.5 μm (PM2.5), many studies have explored the applicability of satellite data, particularly aerosol optical depth (AOD). However, many of the techniques used are computationally demanding; to overcome these challenges, machine learning(ML)-based research has been on the rise. Here, we used ML techniques to directly estimate ground-level PM2.5 concentrations over South Korea using top-of-atmosphere (TOA) reflectance from the Geostationary Ocean Color Imager I (GOCI-I) and its next generation GOCI-II with improved spatial, spectral, and temporal resolutions. Three ML techniques were used to estimate ground-level PM2.5 concentrations: random forest, light gradient boosting machine (LGBM), and artificial neural network. Three schemes were examined based on the input feature composition of the GOCI spectral bands: scheme 1 using all GOCI-I bands, scheme 2 using only GOCI-II bands that overlap with GOCI-I bands, and scheme 3 using all GOCI-II bands. The results showed that LGBM performed better than the other ML models. GOCI–II–based schemes 2 and 3 (determination of coefficient (R2) = 0.85 and 0.85 and root-mean-square-error (RMSE) = 7.69 and 7.82 μg/m3, respectively) performed slightly better than GOCI-I-based scheme 1 (R2 = 0.83 and RMSE = 8.49 μg/m3). In particular, TOA reflectance at a new channel (380 nm) of GOCI-II was identified as the most contributing variable, given its high sensitivity to aerosols. The long-term estimation of PM2.5 concentrations using the proposed models was examined for ground stations located in two major cities. GOCI–II–based models produced a more detailed spatial distribution of PM2.5 concentrations owing to their higher spatial resolution (i.e., 250 m). The use of TOA reflectance data, instead of AOD and other aerosol products commonly used in previous studies, reduced the missing rate of the estimated ground-level PM2.5 concentrations by up to 50%. Our results indicate that the proposed approach using TOA reflectance data from geostationary satellite sensors has great potential for estimating ground-level PM2.5 concentrations for operational purposes.

Early Stage Forest Fire Detection from Himawari-8 AHI Images Using a Modified MOD14 Algorithm Combined with Machine Learning

The early detection and rapid extinguishing of forest fires are effective in reducing their spread. Based on the MODIS Thermal Anomaly (MOD14) algorithm, we propose an early stage fire detection method from low-spatial-resolution but high-temporal-resolution images, observed by the Advanced Himawari Imager (AHI) onboard the geostationary meteorological satellite Himawari-8. In order to not miss early stage forest fire pixels with low temperature, we omit the potential fire pixel detection from the MOD14 algorithm and parameterize four contextual conditions included in the MOD14 algorithm as features. The proposed method detects fire pixels from forest areas using a random forest classifier taking these contextual parameters, nine AHI band values, solar zenith angle, and five meteorological values as inputs. To evaluate the proposed method, we trained the random forest classifier using an early stage forest fire data set generated by a time-reversal approach with MOD14 products and time-series AHI images in Australia. The results demonstrate that the proposed method with all parameters can detect fire pixels with about 90% precision and recall, and that the contribution of contextual parameters is particularly significant in the random forest classifier. The proposed method is applicable to other geostationary and polar-orbiting satellite sensors, and it is expected to be used as an effective method for forest fire detection.

SPATIOTEMPORAL RECOVERY OF HIMAWARI-8 HOURLY AEROSOL OPTICAL DEPTH PRODUCTS VIA THE NESTED BAYESIAN MAXIMUM ENTROPY METHOD

Abstract. Satellite-derived aerosol optical depth (AOD) is an indispensable parameter when conducting studies related to atmospheric environment, climate change, and biogeochemical cycle. However, current satellite-derived AOD products are limited in related applications due to the large proportion of missing data, and the existed methods mainly concentrate on recovering AOD from polar-orbit satellite sensors. In order to solve these issues and take full use of the preponderance of geostationary satellite sensors in high frequency observation, we propose a spatiotemporal AOD recovery framework integrating multi-time scale AOD products based on the nested Bayesian maximum entropy methodology (NBME), aimed to obtain satellite-derived AOD datasets with low data missing and high accuracy. The experiment results show that the spatial coverage of AOD datasets increases from 20.5% to 70.0%, and the R2 and RMSE of the recovered AOD against ground-based AERONET AOD are approximately 0.62 and 0.19, respectively. Moreover, the further simulated experiments indicate that the proposed method also performs better relatively when comparing with other popular recovery methods. Therefore, the proposed NBME recovery method can obtain a more convincing product both in applicable accuracy and visual quality.

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM10) and <2.5 μm (PM2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM10 and PM2.5 were R2 = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM10 and PM2.5 were R2 = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Comparing geostationary and polar-orbiting satellite sensor estimates of Fire Radiative Power (FRP) during the Black Summer Fires (2019–2020) in south-eastern Australia

Background We compared estimates of Fire Radiative Power (FRP) from sensors onboard geostationary Himawari-8 (BRIGHT_AHI) and polar-orbiting TERRA/AQUA (MOD14/MYD14) satellites during the 2019/2020 Black Summer Fires in South-Eastern Australia. Aim/methods Analysis was performed on a pixel, bioregion, and wildfire event basis to assess the utility of the new BRIGHT_AHI FRP product. Key results Results show a high agreement between the products (r = 0.74, P &lt; 0.01) on a pixel level, with BRIGHT_AHI generally underestimating FRP compared to MOD14/MYD14. Regional spatiotemporal trends were captured in more detail by BRIGHT_AHI due to its higher temporal resolution, with MOD14/MYD14 systematically underestimating the total and sub-diurnal FRP values. Nevertheless, both datasets captured similar fire ignition and spread patterns for the study region. On the event level, the correlation between the datasets was moderate (r = 0.49, r = 0.67), when considering different temporal constraints for hotspot matching. Conclusions The results of this study indicate that BRIGHT_AHI approximates the well-established MOD14/MYD14 product during concurrent observations, while revealing additional temporal information for FRP trends. Implications This gives confidence in the reliability of BRIGHT_AHI FRP estimates, opening the way for a denser observation record (10-min intervals) that will provide new opportunities for fire activity reporting, some of which are presented here.

Synergy between Low Earth Orbit (LEO)—MODIS and Geostationary Earth Orbit (GEO)—GOES Sensors for Sargassum Monitoring in the Atlantic Ocean

Since 2011, massive stranding of the brown algae Sargassum has regularly affected the coastal waters of the West Caribbean, Brazil, and West Africa, leading to heavy environmental and socio-economic impacts. Ocean color remote sensing observations as performed by sun-synchronous satellite sensors such as MODIS (NASA), MERIS (ESA), or OLCI (ESA/Copernicus) are used to provide quantitative assessments of Sargassum coverage through the calculation of indices as the Alternative Floating Algae Index (AFAI). Sun-synchronous sensors usually provide at best one daytime observation per day of a given oceanic area. However, such a daily temporal revisit rate is not fully satisfactory to monitor the dynamics of Sargassum aggregation due to their potentially significant drift over the course of the day as a result of oceanic currents and sea surface wind stress. In addition, the sun glint and the presence of clouds limit the use of low earth orbit observations, especially in tropical zones. The high frequency sampling provided by geostationary sensors can be a relevant alternative approach in synergy with ocean color sun-synchronous sensors to increase the temporal resolution of the observations, thus allowing efficient monitoring of Sargassum dynamics. In this study, data acquired by a geostationary satellite sensor located at 36,000 km from Earth, namely GOES-16 (NASA/NOAA), which was primarily designed for meteorology applications, are analyzed to investigate the Sargassum dynamics. The results demonstrate that a GOES-16 hourly composite product is appropriate to identify Sargassum aggregations using an index commonly used for vegetation monitoring, namely NDVI (Normalized Difference Vegetation Index). It is also shown that GOES hourly observations can significantly improve the simulated drift obtained with a transport circulation model, which uses geostrophic current, wind, and waves. This study thus highlights the significant relevance of the effective synergy between sun-synchronous and geostationary satellite sensors for characterizing the Sargassum dynamics. Such a synergy could be summarized as follows: (i) A sun-synchronous sensor enables accurate Sargassum detection and quantitative estimates (e.g., fractional coverage) through AFAI Level-2 products while (ii) a geostationary sensor enables the determination of the displacement features of Sargassum aggregations (velocity, direction).

High-frequency and tidal period observations of suspended particulate matter in coastal waters by AHI/Himawari-8.

Driven by tidal forcing and terrestrial inputs, suspended particulate matter (SPM) in shallow coastal waters usually shows high-frequency dynamics. Although specific geostationary satellite ocean color sensors such as the geostationary ocean color imager (GOCI) can observe SPM hourly eight times in a day from morning to afternoon, it cannot cover the whole semi-diurnal tidal period (∼12 h), and an hourly frequency may be insufficient to witness rapid changes in SPM in highly dynamic coastal waters. In this study, taking the Yangtze River Estuary as an example, we examined the ability of the geostationary meteorological satellite sensor AHI/Himawari-8 to monitor tidal period SPM dynamics with 10-min frequency. Results showed that the normalized water-leaving radiance (Lwn) retrieved by the AHI was consistent with the in-situ data from both cruise- and tower-based measurements. Specifically, AHI-retrieved Lwn was consistent with the in-situ cruise values, with mean relative errors (MREs) of 19.58%, 16.43%, 18.74%, and 26.64% for the 460, 510, 640, and 860 nm bands, respectively, and determination coefficients (R2) larger than 0.89. Both AHI-retrieved and tower-measured Lwn also showed good agreement, with R2 values larger than 0.75 and MERs of 14.38%, 12.42%, 18.16%, and 18.89% for 460, 510, 640, and 860 nm, respectively. Moreover, AHI-retrieved Lwn values were consistent with the GOCI hourly results in both magnitude and spatial distribution patterns, indicating that the AHI can monitor ocean color in coastal waters, despite not being a dedicated ocean color sensor. Compared to the 8 h of SPM observations by the GOCI, the AHI was able to monitor SPM dynamics for up to 12 h from early morning to late afternoon covering the whole semi-diurnal tidal period. In addition, the high-frequency 10-min monitoring by the AHI revealed the minute-level dynamics of SPM in the Yangtze River Estuary (with SPM variation amplitude found to double over 1 h), which were impossible to capture based on the hourly GOCI observations.

Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection

AbstractCloud-affected radiances from geostationary satellite sensors provide the first area-wide observable signal of convection with high spatial resolution in the range of kilometers and high temporal resolution in the range of minutes. However, these observations are not yet assimilated in operational convection-resolving weather prediction models as the rapid, nonlinear evolution of clouds makes the assimilation of related observations very challenging. To address these challenges, we investigate the assimilation of satellite radiances from visible and infrared channels in idealized observing system simulation experiments (OSSEs) for a day with summertime deep convection in central Europe. This constitutes the first study assimilating a combination of all-sky observations from infrared and visible satellite channels, and the experiments provide the opportunity to test various assimilation settings in an environment where the observation forward operator and the numerical model exhibit no systematic errors. The experiments provide insights into appropriate settings for the assimilation of cloud-affected satellite radiances in an ensemble data assimilation system and demonstrate the potential of these observations for convective-scale weather prediction. Both infrared and visible radiances individually lead to an overall forecast improvement, but best results are achieved with a combination of both observation types that provide complementary information on atmospheric clouds. This combination strongly improves the forecast of precipitation and other quantities throughout the whole range of 8-h lead time.

A Simple Algorithm for Deriving an NDVI-Based Index Compatible between GEO and LEO Sensors: Capabilities and Limitations in Japan

Geostationary (GEO) satellite sensors provide earth observation data with a high temporal frequency and can complement low earth orbit (LEO) sensors in monitoring terrestrial vegetation. Consistency between GEO and LEO observation data is thus critical to the synergistic use of the sensors; however, mismatch between the sun–target–sensor viewing geometries in the middle-to-high latitude region and the sensor-specific spectral response functions (SRFs) introduce systematic errors into GEO–LEO products such as the Normalized Difference Vegetation Index (NDVI). If one can find a parameter in which the value is less influenced by geometric conditions and SRFs, it would be invaluable for the synergistic use of the multiple sensors. This study attempts to develop an algorithm to obtain such parameters (NDVI-based indices), which are equivalent to fraction of vegetation cover (FVC) computed from NDVI and endmember spectra. The algorithm was based on a linear mixture model (LMM) with automated computation of the parameters, i.e., endmember spectra. The algorithm was evaluated through inter-comparison between NDVI-based indices using off-nadir GEO observation data from the Himawari 8 Advanced Himawari Imager (AHI) and near-nadir LEO observation data from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) as a reference over land surfaces in Japan at middle latitudes. Results showed that scene-dependent biases between the NDVI-based indices of sensors were −0.0004±0.018 (mean ± standard deviation). Small biases were observed in areas in which the fractional abundances of vegetation were likely less sensitive to the view zenith angle. Agreement between the NDVI-based indices of the sensors was, in general, better than the agreement between the NDVI values. Importantly, the developed algorithm does not require regression analysis for reducing biases between the indices. The algorithm should assist in the development of algorithms for performing inter-sensor translations of vegetation indices using the NDVI-based index as a parameter.

An algorithm for hyperspectral remote sensing of aerosols: 3. Application to the GEO-TASO data in KORUS-AQ field campaign

This paper describes the third part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface spectral reflectance from GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO) instrument. Since the algorithm is designed for future hyperspectral and geostationary satellite sensors, such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), it is applied to GEO-TASO data measured over the same area by different flights as part of the Korea-United Stated Air Quality Study (KORUS-AQ) field campaign in 2016. While GEO-TASO has a spectral sampling interval of ~0.28 nm in the visible, its data is thinned through a band selection approach with consideration of atmospheric transmittance and different surface types, which yields 20 common spectral bands to be used by the algorithm. The algorithm starts with 4 common principal components (PCs) for surface spectral reflectance extracted from various spectral libraries; constraints of surface reflectance and aerosol model parameters are obtained respectively from k-means clustering analysis of the Rayleigh-corrected GEO-TASO spectra and AERONET data. The algorithm then proceeds iteratively with an optimal estimation approach to update PCs and retrieve aerosol optical depth (AOD) from GEO-TASO measured spectra until state vector converges. The comparison of AODs between GEO-TASO retrievals (y) and 7 AERONET (x) sites reveals that the iterative updates of surface PCs (and so surface reflectance) improve the inversions of fine-mode AOD, fine-mode fraction of AOD, Ångström exponent, and AOD at all (440, 550, 550, 675 nm) wavelengths. At 440 nm, the linear fitting equation, the Pearson correlation coefficient (R2), and mean absolute error are improved respectively from y = 0.72x + 0.11, 0.53, and 0.05 (without update of PCs) to y = 1.055x + 0.01, 0.76, and 0.033. Future work is to prepare the algorithm for TEMPO that carries an enhanced version of GEO-TASO instrument.

Landslide Detection with Himawari-8 Geostationary Satellite Data: A Case Study of a Torrential Rain Event in Kyushu, Japan

In this study, we investigated the utility of Himawari-8 Advanced Himawari Imager (AHI), one of third-generation geostationary satellite sensors, for mapping landslides caused by torrential rain that hit the northern Kyushu area in Japan in the summer of 2017. AHI normalized difference vegetation index (NDVI) time series data had distinctive temporal signatures over landslide areas where the NDVI abruptly decreased after the rain event. The observed changes in the NDVI were linearly correlated with the percent landslide area, the percentage of landslide areas within the AHI pixel footprint, obtained with aerial survey (r = 0.78). AHI 10 min resolution data obtained near cloud-free coverage of the landslide region by the 8th day after the disaster event. This was comparable to the amount of time it took to obtain near cloud-free image coverage with aerial survey, and better than those with the polar-orbiting satellite sensors of Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite, Landsat-8 Operational Land Imager, and Sentinel-2A/B MultiSpectral Instrument. These results suggest that third-generation geostationary satellite data can serve as another useful resource for post-event, region-wide initial assessment of landslide areas after a heavy rain event.

Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation

The new generation of geostationary satellite sensors is producing an unprecedented amount of Earth observations with high temporal, spatial and spectral resolutions, which enable us to detect and assess abrupt surface changes. In this study, we developed the land surface directional reflectance and albedo products from Geostationary Operational Environment Satellite-R (GOES-R) Advanced Baseline Imager (ABI) data using a method that was prototyped with the Moderate Resolution Imaging Spectroradiometer (MODIS) data in a previous study, and was also tested with data from the Advanced Himawari Imager (AHI) onboard Himawari-8. Surface reflectance is usually retrieved through atmospheric correction that requires the input of aerosol optical depth (AOD). We first estimated AOD and the surface bidirectional reflectance factor (BRF) model parameters simultaneously based on an atmospheric radiative transfer formulation with surface anisotropy, and then calculated the “blue-sky” surface broadband albedo and directional reflectance. This algorithm was implemented operationally by the National Oceanic and Atmospheric Administration (NOAA) to generate the GOES-R land surface albedo product suite with a daily updated clear-sky satellite observation database. The “operational” land surface albedo estimation from ABI and AHI data was validated against ground measurements at the SURFRAD sites and OzFlux sites and compared with the existing satellite products, including MODIS, Visible infrared Imaging Radiometer (VIIRS), and Global Land Surface Satellites (GLASS) albedo products, where good agreement was found with bias values of −0.001 (ABI) and 0.020 (AHI) and root-mean-square-errors (RMSEs) less than 0.065 for the hourly albedo estimation. Directional surface reflectance estimation, evaluated at more than 74 sites from the Aerosol Robotic Network (AERONET), was proven to be reliable as well, with an overall bias very close to zero and RMSEs within 0.042 (ABI) and 0.039 (AHI). Results show that the albedo and reflectance estimation can satisfy the NOAA accuracy requirements for operational climate and meteorological applications.

Improved Characterisation of Vegetation and Land Surface Seasonal Dynamics in Central Japan with Himawari-8 Hypertemporal Data

Spectral vegetation index time series data, such as the normalized difference vegetation index (NDVI), from moderate resolution, polar-orbiting satellite sensors have widely been used for analysis of vegetation seasonal dynamics from regional to global scales. The utility of these datasets is often limited as frequent/persistent cloud occurrences reduce their effective temporal resolution. In this study, we evaluated improvements in capturing vegetation seasonal changes with 10-min resolution NDVI data derived from Advanced Himawari Imager (AHI), one of new-generation geostationary satellite sensors. Our analysis was focused on continuous monitoring sites, representing three major ecosystems in Central Japan, where in situ time-lapse digital images documenting sky and surface vegetation conditions were available. The very large number of observations available with AHI resulted in improved NDVI temporal signatures that were remarkably similar to those acquired with in situ spectrometers and captured seasonal changes in vegetation and snow cover conditions in finer detail with more certainty than those obtained from Visible Infrared Imaging Radiometer Suite (VIIRS), one of the latest polar-orbiting satellite sensors. With the ability to capture in situ-quality NDVI temporal signatures, AHI “hypertemporal” data have the potential to improve spring and autumn phenology characterisation as well as the classification of vegetation formations.

Validation of GOES-16 ABI and MSG SEVIRI active fire products

The recent deployment of the next generation of geostationary weather satellites provides an opportunity for the establishment of a robust global network of geostationary fire data that can greatly complement existing polar-orbiting satellite fire products. Among other benefits, geostationary satellites provide frequent sampling of diurnal variations in fire activity. Building on established satellite active fire data validation protocols, we used Landsat-8 Operational Land Imager (OLI) as reference fire data to validate the fire products derived from two geostationary satellite sensors: the Advanced Baseline Imager (ABI) on board the National Oceanic and Atmospheric Administration (NOAA) GOES-16 satellite (launched November 2016), and the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI), on board the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Second Generation (MSG) satellite series (multiple launch dates). The two primary algorithms associated with these geostationary active fire data sets are the Fire Detection and Characterization (FDC) product based on the heritage Wildfire Automated Biomass-Burning Algorithm (WF-ABBA) for the GOES-series, and the Fire Radiative Power (FRP-PIXEL) product based on the Fire Thermal Anomaly algorithm (FTA) for the MSG series. Our standardized validation method allowed for a direct inter-comparison between the complementary active fire datasets. Specifically, we present an error assessment of the detection probability (omission error) and false alarm rate (commission error) for two periods in 2017 and 2018 that include extensive fire activity in the respective full-disk sectors covered by each product. The results highlight (i) the restrictiveness of the FRP-PIXEL product (98% omission error) compared to the FDC product (84% omission error), and (ii) the elevated false alarm rate of FDC (88% commission error) compared to FRP-PIXEL (8% commission error). These validation results will be used to help support the development of a harmonized global multi-sensor active fire dataset to be integrated into the Global Wildfire Information System (GWIS).