Geographical gradients in boreal forest albedo and structure in Finland

Correction: DOI:10.1016/j.rse.2014.08.018Land surface albedo is an essential climate variable controlling the planetary radiative energy budget, yet it is still among the main uncertainties of the radiation budget in the current climate modeling. To date, albedo satellite products have not been linked to extensive forest inventory data sets due to the lack of ground reference data. Here, we used comprehensive and detailed maps of forest inventory variables to couple forest structure and MODIS albedo products for both winter and summer conditions. We investigated how the relationships between forest variables and albedo change seasonally and along latitudinal gradients in the forest biomes of Finland between 60° and 70° N. We observed an increase in forest albedo with increasing latitude in winter but not in summer. Also, relationships between forest variables and the black-sky albedo or directional–hemispherical reflectance (DHR) at different latitudes were tighter in winter than in summer, especially for forest biomass. Summer albedo was only weakly correlated with the traditional inventory variables. Our findings suggest that the relationships between forest variables and DHR depend on latitude.Peer reviewe

Implications of whole-disc DSCOVR EPIC spectral observations for estimating Earth's spectral reflectivity based on low-earth-orbiting and geostationary observations

Earth’s reflectivity is among the key parameters of climate research. National Aeronautics and Space Administration (NASA)’s Earth Polychromatic Imaging Camera (EPIC) onboard National Oceanic and Atmospheric Administration (NOAA)’s Deep Space Climate Observatory (DSCOVR) spacecraft provides spectral reflectance of the entire sunlit Earth in the near backscattering direction every 65 to 110 min. Unlike EPIC, sensors onboard the Earth Orbiting Satellites (EOS) sample reflectance over swaths at a specific local solar time (LST) or over a fixed area. Such intrinsic sampling limits result in an apparent Earth’s reflectivity. We generated spectral reflectance over sampling areas using EPIC data. The difference between the EPIC and EOS estimates is an uncertainty in Earth’s reflectivity. We developed an Earth Reflector Type Index (ERTI) to discriminate between major Earth atmosphere components: clouds, cloud-free ocean, bare and vegetated land. Temporal variations in Earth’s reflectivity are mostly determined by clouds. The sampling area of EOS sensors may not be sufficient to represent cloud variability, resulting in biased estimates. Taking EPIC reflectivity as a reference, low-earth-orbiting-measurements at the sensor-specific LST tend to overestimate EPIC values by 0.8% to 8%. Biases in geostationary orbiting approximations due to a limited sampling area are between −0.7% and 12%. Analyses of ERTI-based Earth component reflectivity indicate that the disagreement between EPIC and EOS estimates depends on the sampling area, observation time and vary between −10% and 23%.The NASA/GSFC DSCOVR project is funded by NASA Earth Science Division. W. Song, G. Yan, and X. Mu were also supported by the key program of National Natural Science Foundation of China (NSFC; Grant No. 41331171). This research was conducted and completed during a 13-month research stay of the lead author in the Department of Earth and Environment, Boston University as a joint Ph.D. student, which was supported by the Chinese Scholarship Council (201606040098). DSCOVR EPIC L1B data were obtained from the NASA Langley Research Center Atmospheric Science Data Center. The authors would like to thank the editor who handled this paper and the two anonymous reviewers for providing helpful and constructive comments and suggestions that significantly helped us improve the quality of this paper. (NASA Earth Science Division; 41331171 - key program of National Natural Science Foundation of China (NSFC); 201606040098 - Chinese Scholarship Council)Accepted manuscrip

Photographic measurement of leaf angles in field crops

Peer reviewe

The fluorescence explorer (FLEX) mission:imaging spectroscopy in very high spectral resolution

The FLuorescence EXplorer (FLEX) mission was selected in 2015, by the European Space Agency, as the 8th ESA Earth Explorer, to be launched in 2025. The key scientific objective of the mission is the quantitative mapping of actual photosynthetic activity of terrestrial ecosystems, at a global scale and with a spatial resolution adequate to resolve land processes associated to vegetation dynamics. To accomplish such objective, the FLEX satellite carries the Fluorescence Imaging Spectrometer (FLORIS). FLEX will fly in tandem with Copernicus Sentinel-3 (same orbit at 815 km, 27 days repeat cycle). Together with FLORIS, the OLCI and SLSTR instruments on Sentinel-3 provide all the necessary information to retrieve the emitted vegetation fluorescence, including compensation for atmospheric effects and the derivation of the additional biophysical information needed to map the spatial and temporal dynamics of vegetation photosynthesis from such global measurements

Decoupling Contributions from Canopy Structure and Leaf Optics is Critical for Remote Sensing Leaf Biochemistry (Reply to Townsend, et al.)

Townsend et al. (1) agree that we explained that the apparent relationship (2) between foliar nitrogen (%N) and near-infrared (NIR) canopy reflectance was largely attributable to structure (which is in turn caused by variation in fraction of broadleaf canopy). Our conclusion that the observed correlation with %N was spurious (i.e., lacking a causal basis) is, thus, clearly justified: we demonstrated that structure explained the great majority of observed correlation, where the structural influence was derived precisely via reconciling the observed correlation with radiative-transfer theory. What this also suggests is that such correlations, although observed, do not uniquely provide information on canopy biochemical constituents

A geometric model for scaling between needle and shoot spectral albedos

Abstract of presentation at the 7th International Conference on Functional-Structural Plant Models, 9 -14 June 2013, Sarriselka, Finland

Ecological applications of physically based remote sensing methods

Global monitoring of vegetation using optical remote sensing has undergone rapid technological and methodological development during the past decade. Physically based methods generally apply reflectance models for interpreting remotely sensed data sets. These methods have become increasingly important in the assessment of terrestrial variables from satellite-borne and airborne images. Products based on satellite images currently include various ecological variables that are needed for monitoring changes in forest cover, structure and functioning, including biophysical variables such as the amount of photosynthesizing leaf area. This paper reviews variables and global products estimated from optical satellite sensors describing, for example, the amount and functioning of green biomass and forest carbon exchange. Continuous validation work as new vegetation products are released continues to be important. More emphasis is needed on the collection of field data equivalent to satellite retrievals, data harmonization and continuous measurements of seasonal forest dynamics

TAIGA: a novel dataset for multitask learning of continuous and categorical forest variables from hyperspectral imagery

Publisher Copyright: AuthorThe spectral and spatial resolutions of modern optical Earth observation data are continuously increasing. To fully utilize the data, integrate them with other information sources, and create applications relevant to real-world problems, extensive training data are required. We present TAIGA, an open dataset including continuous and categorical forestry data, accompanied by airborne hyperspectral imagery with a pixel size of 0.7 m. The dataset contains over 70 million labeled pixels belonging to more than 600 forest stands. To establish a baseline on TAIGA dataset for multitask learning, we trained and validated a convolutional neural network to simultaneously retrieve 13 forest variables. Due to the size of the imagery, the training and testing sets were independent, with strictly no overlap for patches up to 45 x 45 pixels. Our retrieval results show that including both spectral and textural information improves the accuracy of mapping key boreal forest structural characteristics, compared with an earlier study including only spectral information from the same image. TAIGA responds to the increased availability of hyperspectral and very high resolution imagery, and includes the forestry variables relevant for forestry and environmental applications. We propose the dataset as a new benchmark for spatial-spectral methods that overcomes the limitations of widely used small-scale hyperspectral datasets.Peer reviewe

A note on upscaling coniferous needle spectra to shoot spectral albedo

Mutual shading of needles in coniferous shoots and small-scale variations in needle area density both within and between shoots violate conventional assumptions used in the definition of the elementary volume in radiative transfer models. In this paper, we test the hypothesis if it is possible to scale needle spectral albedo up to shoot spectral albedo using only one structural parameter: the spherically averaged shoot silhouette to total area ratio (STAR). To test the hypothesis, we measured both structural and spectral properties of ten Scots pine (Pinus sylvestris) shoots and their needles. Our results indicate that it is possible to upscale from needle to shoot spectral albedo using STAR. The upscaling model performed best in the VIS and SWIR regions, and for shoots with high STAR values. As STAR is linearly related to photon recollision probability, it is also possible to apply the upscaling model as integral part of radiative transfer models

Diurnal Changes in Leaf Photochemical Reflectance Index in Two Evergreen Forest Canopies

The spectral properties of plant leaves relate to the state of their photosynthetic apparatus and the surrounding environment. An example is the well known photosynthetic downregulation, active on the time scale from minutes to hours, caused by reversible changes in the xanthophyll cycle pigments. These changes affect leaf spectral absorption and are frequently quantified using the photochemical reflectance index (PRI). This index can be used to remotely monitor the photosynthetic status of vegetation, and allows for a global satellite-based measurement of photosynthesis. Such earth observation satellites in near-polar orbits usually cover the same geographical location at the same local solar time at regular intervals. To facilitate the interpretation of these instantaneous remote PRI measurements and upscale them temporally, we measured the daily course of leaf PRI in two evergreen biomes&#x2014;a European boreal forest and an Amazon rainforest. The daily course of PRI was different for the two locations: At the Amazonian forest, the PRI of Manilkara elata leaves was correlated with the average photosynthetic photon flux density (PPFD) ($R^{2}=0.59$, p< 0.01) of the 40 minutes preceding the leaf measurement. In the boreal location, the variations in Pinus sylvestris needle PRI were only weakly ($R^{2}=0.27$, p< 0.05) correlated with mean PPFD of the preceding two hours; for Betula pendula, the correlation was insignificant (p>0.5) regardless of the averaging period. The measured daily PRI curves were specific to species and/or environmental conditions. Hence, for a proper interpretation of satellite-measured instantaneous photosynthesis, the scaling of PRI measurements should be supported with information on its correlation with PPFD