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 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: 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

Prototyping of LAI and FPAR retrievals from MODIS multi-angle implementation of atmospheric correction (MAIAC) data

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation are key variables in many global models of climate, hydrology, biogeochemistry, and ecology. These parameters are being operationally produced from Terra and Aqua MODIS bidirectional reflectance factor (BRF) data. The MODIS science team has developed, and plans to release, a new version of the BRF product using the multi-angle implementation of atmospheric correction (MAIAC) algorithm from Terra and Aqua MODIS observations. This paper presents analyses of LAI and FPAR retrievals generated with the MODIS LAI/FPAR operational algorithm using Terra MAIAC BRF data. Direct application of the operational algorithm to MAIAC BRF resulted in an underestimation of the MODIS Collection 6 (C6) LAI standard product by up to 10%. The difference was attributed to the disagreement between MAIAC and MODIS BRFs over the vegetation by −2% to +8% in the red spectral band, suggesting different accuracies in the BRF products. The operational LAI/FPAR algorithm was adjusted for uncertainties in the MAIAC BRF data. Its performance evaluated on a limited set of MAIAC BRF data from North and South America suggests an increase in spatial coverage of the best quality, high-precision LAI retrievals of up to 10%. Overall MAIAC LAI and FPAR are consistent with the standard C6 MODIS LAI/FPAR. The increase in spatial coverage of the best quality LAI retrievals resulted in a better agreement of MAIAC LAI with field data compared to the C6 LAI product, with the RMSE decreasing from 0.80 LAI units (C6) down to 0.67 (MAIAC) and the R2 increasing from 0.69 to 0.80. The slope (intercept) of the satellite-derived vs. field-measured LAI regression line has changed from 0.89 (0.39) to 0.97 (0.25).This work was funded by NASA Earth Science Division to MODIS (NNX14AI71G) and VIIRS (NNX14AP80A) programs through grants to Boston University (Ranga B. Myneni, PI), and HBO contract # 21205-14-036 to Yuri Knyazikhin. (NNX14AI71G - NASA; NNX14AP80A - NASA; 21205-14-036 - HBO contract)http://www.mdpi.com/2072-4292/9/4/370Published versio

Abiotic controls on macroscale variations of humid tropical forest height

Spatial variation of tropical forest tree height is a key indicator of ecological processes associated with forest growth and carbon dynamics. Here we examine the macroscale variations of tree height of humid tropical forests across three continents and quantify the climate and edaphic controls on these variations. Forest tree heights are systematically sampled across global humid tropical forests with more than 2.5 million measurements from Geoscience Laser Altimeter System (GLAS) satellite observations (2004–2008). We used top canopy height (TCH) of GLAS footprints to grid the statistical mean and variance and the 90 percentile height of samples at 0.5 degrees to capture the regional variability of average and large trees globally. We used the spatial regression method (spatial eigenvector mapping-SEVM) to evaluate the contributions of climate, soil and topography in explaining and predicting the regional variations of forest height. Statistical models suggest that climate, soil, topography, and spatial contextual information together can explain more than 60% of the observed forest height variation, while climate and soil jointly explain 30% of the height variations. Soil basics, including physical compositions such as clay and sand contents, chemical properties such as PH values and cation-exchange capacity, as well as biological variables such as the depth of organic matter, all present independent but statistically significant relationships to forest height across three continents. We found significant relations between the precipitation and tree height with shorter trees on the average in areas of higher annual water stress, and large trees occurring in areas with low stress and higher annual precipitation but with significant differences across the continents. Our results confirm other landscape and regional studies by showing that soil fertility, topography and climate may jointly control a significant variation of forest height and influencing patterns of aboveground biomass stocks and dynamics. Other factors such as biotic and disturbance regimes, not included in this study, may have less influence on regional variations but strongly mediate landscape and small-scale forest structure and dynamics.The research was funded by Gabon National Park (ANPN) under the contract of 011-ANPN/2012/SE-LJTW at UCLA. We thank IIASA, FAO, USGS, NASA, Worldclim science teams for making their data available. (011-ANPN/2012/SE-LJTW - Gabon National Park (ANPN) at UCLA

Evaluation of MODIS LAI/FPAR product Collection 6. Part 2: Validation and intercomparison

The aim of this paper is to assess the latest version of the MODIS LAI/FPAR product (MOD15A2H), namely Collection 6 (C6). We comprehensively evaluate this product through three approaches: validation with field measurements, intercomparison with other LAI/FPAR products and comparison with climate variables. Comparisons between ground measurements and C6, as well as C5 LAI/FPAR indicate: (1) MODIS LAI is closer to true LAI than effective LAI; (2) the C6 product is considerably better than C5 with RMSE decreasing from 0.80 down to 0.66; (3) both C5 and C6 products overestimate FPAR over sparsely-vegetated areas. Intercomparisons with three existing global LAI/FPAR products (GLASS, CYCLOPES and GEOV1) are carried out at site, continental and global scales. MODIS and GLASS (CYCLOPES and GEOV1) agree better with each other. This is expected because the surface reflectances, from which these products were derived, were obtained from the same instrument. Considering all biome types, the RMSE of LAI (FPAR) derived from any two products ranges between 0.36 (0.05) and 0.56 (0.09). Temporal comparisons over seven sites for the 2001–2004 period indicate that all products properly capture the seasonality in different biomes, except evergreen broadleaf forests, where infrequent observations due to cloud contamination induce unrealistic variations. Thirteen years of C6 LAI, temperature and precipitation time series data are used to assess the degree of correspondence between their variations. The statistically-significant associations between C6 LAI and climate variables indicate that C6 LAI has the potential to provide reliable biophysical information about the land surface when diagnosing climate-driven vegetation responses.Help from MODIS and VIIRS Science team members is gratefully acknowledged. This work is supported by the MODIS program of NASA and partially funded by the National Basic Research Program of China (Grant No. 2013CB733402) and the key program of NSFC (Grant No. 41331171). Kai Yan gives thanks for the scholarship from the China Scholarship Council. (MODIS program of NASA; 2013CB733402 - National Basic Research Program of China; 41331171 - NSFC; China Scholarship Council

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

Published versio

Evaluation of MODIS LAI/FPAR product Collection 6. Part 1: consistency and improvements

As the latest version of Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products, Collection 6 (C6) has been distributed since August 2015. This collection is evaluated in this two-part series with the goal of assessing product accuracy, uncertainty and consistency with the previous version. In this first paper, we compare C6 (MOD15A2H) with Collection 5 (C5) to check for consistency and discuss the scale effects associated with changing spatial resolution between the two collections and benefits from improvements to algorithm inputs. Compared with C5, C6 benefits from two improved inputs: (1) L2G–lite surface reflectance at 500 m resolution in place of reflectance at 1 km resolution; and (2) new multi-year land-cover product at 500 m resolution in place of the 1 km static land-cover product. Global and seasonal comparison between C5 and C6 indicates good continuity and consistency for all biome types. Moreover, inter-annual LAI anomalies at the regional scale from C5 and C6 agree well. The proportion of main radiative transfer algorithm retrievals in C6 increased slightly in most biome types, notably including a 17% improvement in evergreen broadleaf forests. With same biome input, the mean RMSE of LAI and FPAR between C5 and C6 at global scale are 0.29 and 0.091, respectively, but biome type disagreement worsens the consistency (LAI: 0.39, FPAR: 0.102). By quantifying the impact of input changes, we find that the improvements of both land-cover and reflectance products improve LAI/FPAR products. Moreover, we find that spatial scale effects due to a resolution change from 1 km to 500 m do not cause any significant differences.Help from MODIS & VIIRS Science team members is gratefully acknowledged. This work is supported by the MODIS program of NASA and partially funded by the National Basic Research Program of China (Grant No. 2013CB733402), the key program of NSFC (Grant No. 41331171) and Chinese Scholarship Council. (MODIS program of NASA; 2013CB733402 - National Basic Research Program of China; 41331171 - NSFC; Chinese Scholarship Council