The Spectral Invariant Approximation within Canopy Radiative Transfer to Support the Use of the EPIC/DSCOVR Oxygen B-band for Monitoring Vegetation

EPIC (Earth Polychromatic Imaging Camera) is a 10-channel spectroradiometer onboard DSCOVR (Deep Space Climate Observatory) spacecraft. In addition to the NIR (780 nm) and the ‘red’ (680 nm) channels, EPIC also has the O2 A-band (764±0.2 nm) and B-band (687.75±0.2 nm). The EPIC at-sensor Normalized Difference Vegetation Index (NDVI) is defined as the difference between NIR and ‘red’ channels normalized to their sum. However, the use of the O2 B-band instead of the ‘red’ channel mitigates the effect ofatmosphere on surface reflectance because it reduces contribution from the radiation scattered by theatmosphere. Applying the radiative transfer theory and the spectral invariant approximation to EPIC observations, we provide supportive arguments for using the O2 band instead of the red channel for monitoring the vegetation dynamics

Vegetation hot spot signatures from synergy of DSCOVR EPIC, Terra MISR, MODIS and geostationary sensors

It has been widely recognized that the hotspot region in Bidirectional Reflectance Factors (BRF) of vegetated surfaces represents the most information-rich directions in the directional distribution of canopy reflected radiation. The hotspot effect is strongly correlated with canopy architectural parameters such as foliage size and shape, crown geometry and within-crown foliage arrangement, leaf area index and its sunlit fraction. Here we present a new methodology that synergistically incorporate features of Terra Multi-angle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS, Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR), Advanced Baseline Imager (ABI) carried by the Geostationary Operational Environmental Satellites (GOES) R series and Advanced Himawari Imager (AHI) observation geometries and results in a new type of hot spot signatures that maximally sensitive to vegetation changes. We discuss a physical basis for the synergy of multi-sensor data. Five areas that include Amazonian forests (evergreen broadleaf forest), Mississippi forest (deciduous forest), Heihe River Basin (crops), Genhe forest (coniferous forest) and Australia central grassland were selected to generate time series of hot spot signatures of different land cover types for the period of concurrent Terra/Aqua/DSCOVR and geostationary observations. We demonstrate value of the hot spot signatures for monitoring changes and biophysical processes in vegetated land through analyses of variations in magnitude and shape of angular distribution of canopy reflected radiation and the rigorous use of radiative transfer theory.Accepted manuscrip

EPIC spectral observations of variability in earth’s global reflectance

NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR) satellite observes the entire sunlit Earth every 65 to 110 min from the Sun-Earth Lagrangian L1 point. This paper presents initial EPIC shortwave spectral observations of the sunlit Earth reflectance and analyses of its diurnal and seasonal variations. The results show that the reflectance depends mostly on (1) the ratio between land and ocean areas exposed to the Sun and (2) cloud spatial and temporal distributions over the sunlit side of Earth. In particular, the paper shows that (a) diurnal variations of the Earth's reflectance are determined mostly by periodic changes in the land-ocean fraction of its the sunlit side; (b) the daily reflectance displays clear seasonal variations that are significant even without including the contributions from snow and ice in the polar regions (which can enhance daily mean reflectances by up to 2 to 6% in winter and up to 1 to 4% in summer); (c) the seasonal variations of the sunlit Earth reflectance are mostly determined by the latitudinal distribution of oceanic clouds

DSCOVR EPIC vegetation earth system data record: product analysis and scientific exploration

The NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR) mission was launched on February 11, 2015 to the Sun-Earth Lagrangian L1 point where it began to collect radiance data of the entire sunlit Earth every 65 to 110 min in June 2015. It provides imageries in near backscattering directions at ten ultraviolet to near infrared narrow spectral bands. The DSCOVR EPIC science product suite includes vegetation Earth System Data Record (VESDR) that provides leaf area index (LAI) and diurnal courses of normalized difference vegetation index (NDVI), sunlit LAI (SLAI), fraction of incident photosynthetically active radiation (FPAR) and directional area scattering function (DASF). The parameters at 10 km sinusoidal grid and 65 to 110 minute temporal frequency generated from the upstream DSCOVR EPIC BRF product were released on June-07-2018 and are available from the NASA Langley Atmospheric Science Data Center. This poster provides an overview of the EPIC VESDR research. This includes a description of the algorithm and its performance, details of the product, its initial quality assessment and obtaining new information on the 3D canopy structure for use in ecological models through novel combinations of the VESDR parameters.Accepted manuscrip

Vegetation Earth System Data Record from DSCOVR EPIC Observations: product status and scientific exploration

80NSSC22K0499 - NASA; 80NSSC19K0762 - NASAAccepted manuscrip

Earth reflector type classification based on multispectral remote sensing image

Earth’s reflectivity is one of the key parameters of climate change, Earth’s radiation budget research and so on. It is determined by the characteristic of Earth atmosphere components. Earth atmosphere components vary strongly in both spatially and temporally, thus complete spatial mosaics and/or richer time series information are needed. In this study, we developed an Earth Reflector Type Index (ERTI) to discriminate major Earth atmosphere components: clouds, cloud-free ocean, bare and vegetated land. Results show that the probability of the ERTI method with selected thresholds being able to discriminate between cloudy and cloud-free scenes is about 82%. ERTI can be used to interpret global Earth’s reflectivity and its temporal variation.Accepted manuscrip

Vegetation hot spot signatures from synergy of EPIC/DSCOVR, MISR/Terra, MODIS and geostationary sensors to monitor changes and biophysical processes of global forests

Accepted manuscrip

Vegetation hot spot signatures from synergy of EPIC/DSCOVR and EOS/SUOMI sensors to monitor changes in global forests

Update on "Vegetation Hot Spot Signatures from Synergy of EPIC/DSCOVR and EOS/SUOMI Sensors to Monitor Changes in Global Forests."First author draf

Vegetation earth system data record from DSCOVR EPIC observations

Poster presented at 2017 AGU Fall Meeting, New Orleans, Louisiana. POSTER ID: A33D-238