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

Vegetation Earth System Data Record (VESDR)

https://eosweb.larc.nasa.gov/project/dscovr/DSCOVR_VESDR_SDRG.pdfFirst author draftFirst author draf

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

80NSSC22K0499 - NASA; 80NSSC19K0762 - NASAAccepted manuscrip

Inconsistencies of interannual variability and trends in long-term satellite leaf area index products

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products

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

Assessment of the biophysical characteristics of rangeland community using scatterometer and optical measurements

Research activities for the following study areas are summarized: single scattering of parallel direct and axially symmetric diffuse solar radiation in vegetative canopies; the use of successive orders of scattering approximations (SOSA) for treating multiple scattering in a plant canopy; reflectance of a soybean canopy using the SOSA method; and C-band scatterometer measurements of the Konza tallgrass prairie

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: new parameters in version 2 VESDR product

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). The first version of the product provided 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). Five new parameters have been developed and added in Version 2 VESDR product: Earth Reflector Type Index (ERTI) and Canopy Scattering Coefficient (CSC) at 443 nm, 551 nm, 680 nm and 779 nm. The parameters are at 10 km regional sinusoidal grids and 65 to 110 minute temporal frequency generated from the upstream DSCOVR EPIC BRF product and 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 VESDR product, its initial quality assessment, showcasing the value of the product for monitoring changes of the equatorial forests and obtaining new parameters from on canopy structure from the VESDR parameters.First author draf

Modelling vegetation angular signatures from DSCOVR/EPIC and MISR Observations

The angular signatures of reflectance are rich sources of diagnostic information about vegetation canopies, because the geometric structure and foliage optics determine their magnitude and angular distribution. This poster presents angular signatures of Bidirectional Reflectance Factors (BRF) in different biome types for the period of concurrent DSCOVR/EPIC (Earth Polychromatic Imaging Camera onboard the Deep Space Climate Observatory) and MISR (Terra Multi-angle Imaging SpectroRadiometer) observations. We developed a BRF model, which could approximate DSCOVR/EPIC and MISR observations, through analyses of variations in magnitude and shape of angular distribution of canopy reflected radiation and the rigorous use of radiative transfer theory. In this model, the correlation coefficient, visible fraction of leaf area in the direction Ω from the sunlit areas of leaves, is an important parameter that allows us to extend conventional radiative transfer equation to media with finite dimensional scatters and consequently accurately discriminate between sunlit and shaded leaves. Our model was able to capture seasonal variations of reflectance in amazon rain forest, which resulted from changes in both leaf area and solar zenith angle.Published versio

Monitoring Crop Yield in USA Using a Satellite-Based Climate-Variability Impact Index

A quantitative index is applied to monitor crop growth and predict agricultural yield in continental USA. The Climate-Variability Impact Index (CVII), defined as the monthly contribution to overall anomalies in growth during a given year, is derived from 1-km MODIS Leaf Area Index. The growing-season integrated CVII can provide an estimate of the fractional change in overall growth during a given year. In turn these estimates can provide fine-scale and aggregated information on yield for various crops. Trained from historical records of crop production, a statistical model is used to produce crop yield during the growing season based upon the strong positive relationship between crop yield and the CVII. By examining the model prediction as a function of time, it is possible to determine when the in-season predictive capability plateaus and which months provide the greatest predictive capacity