The physics of spectral invariants

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Scaling PRI between coniferous canopy structures

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Keskkonnakaugseire

BeSt programmi toetusel loodud e-kursuse "Keskkonnakaugseire" õppematerjalid

Improved parametrisation of a physically-based forest reflectance model for retrieval of boreal forest structural properties

Physically-based reflectance models offer a robust and transferable method to assess biophysical characteristics of vegetation in remote sensing. Forests exhibit explicit structure at many scales, from shoots and branches to landscape patches, and hence present a specific challenge to vegetation reflectance modellers. To relate forest reflectance with its structure, the complexity must be parametrised leading to an increase in the number of reflectance model inputs. The parametrisations link reflectance simulations to measurable forest variables, but at the same time rely on abstractions (e.g. a geometric surface forming a tree crown) and physically-based simplifications that are difficult to quantify robustly. As high-quality data on basic forest structure (e.g. tree height and stand density) and optical properties (e.g. leaf and forest floor reflectance) are becoming increasingly available, we used the well-validated forest reflectance and transmittance model FRT to investigate the effect of the values of the “uncertain” input parameters on the accuracy of modelled forest reflectance. With the state-of-the-art structural and spectral forest information, and Sentinel-2 Multispectral Instrument imagery, we identified that the input parameters influencing the most the modelled reflectance, given that the basic forestry variables are set to their true values and leaf mass is determined from reliable allometric models, are the regularity of the tree distribution and the amount of woody elements. When these parameters were set to their new adjusted values, the model performance improved considerably, reaching in the near infrared spectral region (740–950 nm) nearly zero bias, a relative RMSE of 13% and a correlation coefficient of 0.81. In the visible part of the spectrum, the model performance was not as consistent indicating room for improvement

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

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

Inversion of true leaf reflectance from very high spatial resolution hyperspectral images

The spectral reflectance of vegetation obtained from optical sensors provides information on their biophysical and biochemical properties. However, in remote sensing, reflectance is typically computed with respect to the top-of canopy (TOC) surface, resulting in an apparent reflectance due to the differences between the illumination conditions between the observed vegetation elements and the TOC surface. While the TOC reflectance is useful for data with coarse spatial resolution, it leads to erroneous estimates of the vegetation properties when applied to very high spatial resolution (VHR) data where individual leaves are visible. An illumination correction is required to retrieve the true leaf reflectance from the TOC reflectance. The present work investigates an illumination correction method for retrieving the true leaf reflectance from VHR hyperspectral TOC reflectance images based on the spectral invariant theory and a simple mathematical model for the leaf reflectance. The method is tested on simulated and measured data. The results show that the leaf reflectance can be accurately estimated from both data (average RMSD between 0.02 and < 0.12)

Photon recollision probability in heterogeneous forest canopies: : compatibility with a hybrid GO model

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Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest

Three-quarters of Finland’s land surface area is filled with forests, which compose a great part of the country’s biomass, carbon pools and carbon sinks. In order to acquire up-to-date information on the forests, optical remote sensing techniques are commonly used. Moreover, in the future hyperspectral satellite missions will start providing data to support the needs of natural resource management practices, such as forestry. It is, however, unclear what would be the additional value from using hyperspectral data compared to multispectral in quantifying forest variables of Finnish boreal forest. In this study, we used the remote sensing data by hyperspectral AISA imager (128 bands, 400–1000 nm, resolution 0.7 m) and Sentinel-2 (10 bands, resolution 10 m) to assess the possible benefits of higher spectral resolution. As reference data, we used a new nationwide forest resource dataset (stand-level data), which has a high potential in further remote sensing applications. In addition, we used a set of independent in situ measurements (plot-level data) for validation. We applied two kernel-based machine learning regression algorithms (Gaussian process and support vector regression) to relate boreal forest variables with the remote sensing data. The variables of interest were mean height, basal area, leaf area index (LAI), stem biomass and main tree species. The regression algorithms were trained with stand-level data and estimations were evaluated with stand- and plot-level holdout sets. The estimation accuracies were examined with absolute and relative root-mean-square errors. Successful variable estimations showed that kernel-based regression algorithms are suitable tools for forest structure estimation. Based on the results, the additional value of hyperspectral remote sensing data in forest variable estimation in Finnish boreal forest is mainly related to variables with species-specific information, such as main tree species and LAI. The more interesting variables for forestry industry, such as mean height, basal area and stem biomass, can also be estimated accurately with more traditional multispectral remote sensing data.Peer reviewe