Generating global products of LAI and FPAR from SNPP-VIIRS data: theoretical background and implementation

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation have been successfully generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) data since early 2000. As the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard, the Suomi National Polar-orbiting Partnership (SNPP) has inherited the scientific role of MODIS, and the development of a continuous, consistent, and well-characterized VIIRS LAI/FPAR data set is critical to continue the MODIS time series. In this paper, we build the radiative transfer-based VIIRS-specific lookup tables by achieving minimal difference with the MODIS data set and maximal spatial coverage of retrievals from the main algorithm. The theory of spectral invariants provides the configurable physical parameters, i.e., single scattering albedos (SSAs) that are optimized for VIIRS-specific characteristics. The effort finds a set of smaller red-band SSA and larger near-infraredband SSA for VIIRS compared with the MODIS heritage. The VIIRS LAI/FPAR is evaluated through comparisons with one year of MODIS product in terms of both spatial and temporal patterns. Further validation efforts are still necessary to ensure the product quality. Current results, however, imbue confidence in the VIIRS data set and suggest that the efforts described here meet the goal of achieving the operationally consistent multisensor LAI/FPAR data sets. Moreover, the strategies of parametric adjustment and LAI/FPAR evaluation applied to SNPP-VIIRS can also be employed to the subsequent Joint Polar Satellite System VIIRS or other instruments.Accepted manuscrip

Prediction of skiing time by structured regression algorithm

In this paper, the application of Gaussian conditional random fields (GCRF) in the case of prediction skiing time between ski gates in ski center Kopaonik, is presented. Gaussian conditional random fields is well-known structured regression method that exploits advantages of unstructured predictors and combines them with the information concerning correlation between outputs. Four different unstructured predictors were used: ridge regression, LASSO regression, Random forest regression and support vector machine regression. Even thought, only 18 features are used for prediction of skiing time, GCRF achieved better results, concerning R2 and mean absolute error, compared to unstructured predictors

Modelling optical properties of morphologically complex aerosols

The interpretation of remote sensing data of atmospheric aerosol particles requires a thorough understanding of the links between microphysical and optical properties. Morphologically complex aerosol models describe the particles’ morphology in detail. Based on the calculations with realistic particle models, simplified models can be devised, which incorporate essential microphysical properties for reproducing the optical properties. In this thesis, such models are developed and tested for soot aerosols, for mineral dust, and for dried and partially dissolved sea salt aerosol.A tunable model for coated soot aggregates is presented, and corresponding uncertainty estimates are performed. One of the main sources of uncertainty for thickly coated soot is the chemical composition of the coating, as represented by its refractive index. These uncertainties are so substantial, they are investigated as a potential source of information. The calculated lidar-measurable (spectral) quantities are distinct for two coating materials.The non-sphericity of a particle is identified as an essential morphological property affecting the linear depolarisation ratio. For coated soot another important property is the amount of carbon interacting with the incident wave, as it affects the absorption cross section. Combining these two insights resulted in the core grey shell dimer (CGS2) model, which is introduced in this thesis.For dry sea salt aerosol different random geometries are investigated, to simultaneously calculate linear depolarisation and extinction-to-backscatter ratio of dried sea salt aerosol particles. The results indicate that convex polyhedra are best suited to represent dried sea salt aerosol particles. Thus, the coated convex polyhedra model is proposed as the basis for modelling dissolving sea salt in a further study. For dissolving sea salt three simplified, equally well-performing models are presented, which identify the change in particle sphericity as a key morphological feature.A spheroidal model with a single refractive index and a single aspect ratio is fitted to laboratory measurements of 131 different dust samples. The scattering of the measurements about the model can mainly be explained by changes in morphology and dielectric properties, and to a lesser degree by the width of the particle size distribution.These results are expected to significantly advance our capacity to exploit and interpret polarimetric remote sensing observations of morphologically complex and chemically heterogeneous aerosol. This will be important for constraining Earth-system climate and air-quality forecasting models, and for evaluating and improving parameterisations of aerosol processes in these environmental modelling system

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Satellite observations of atmospheric methane and their value for quantifying methane emissions

Methane is a greenhouse gas emitted by a range of natural and anthropogenic sources. Atmospheric methane has been measured continuously from space since 2003, and new instruments are planned for launch in the near future that will greatly expand the capabilities of space-based observations. We review the value of current, future, and proposed satellite observations to better quantify and understand methane emissions through inverse analyses, from the global scale down to the scale of point sources and in combination with suborbital (surface and aircraft) data. Current global observations from Greenhouse Gases Observing Satellite (GOSAT) are of high quality but have sparse spatial coverage. They can quantify methane emissions on a regional scale (100–1000 km) through multiyear averaging. The Tropospheric Monitoring Instrument (TROPOMI), to be launched in 2017, is expected to quantify daily emissions on the regional scale and will also effectively detect large point sources. A different observing strategy by GHGSat (launched in June 2016) is to target limited viewing domains with very fine pixel resolution in order to detect a wide range of methane point sources. Geostationary observation of methane, still in the proposal stage, will have the unique capability of mapping source regions with high resolution, detecting transient "super-emitter" point sources and resolving diurnal variation of emissions from sources such as wetlands and manure. Exploiting these rapidly expanding satellite measurement capabilities to quantify methane emissions requires a parallel effort to construct high-quality spatially and sectorally resolved emission inventories. Partnership between top-down inverse analyses of atmospheric data and bottom-up construction of emission inventories is crucial to better understanding methane emission processes and subsequently informing climate policy

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018