Vegetation Parameters 2

Despite the critical importance of photosynthesis for the Earth system, understanding how it is influenced by factors such as climate variability, disturbance history, and water or nutrient availability remains a challenge because of the complex interactions and the lack of GPP measurements at various temporal and spatial scales.

Space observations of the sun-induced chlorophyll fluorescence (SIF) electromagnetic signal emitted by plants in the 650-850nm spectral range hold the promise of providing a new view of vegetation photosynthesis on a global basis. Global retrievals of SIF from space have recently been achieved from a number of spaceborne spectrometers originally intended for atmospheric research. Despite not having been designed for land applications, such instruments have turned out to provide the necessary spectral and radiometric sensitivity for SIF retrieval from space.

The first global measurements of SIF were achieved in 2011 from spectra acquired by the Japanese GOSAT mission launched in 2009. The retrieval takes advantage of the high spectral resolution provided by GOSAT’s Fourier Transform Spectrometer (FTS) which allows the evaluation of the in-filling of solar Fraunhofer lines by SIF. Unfortunately, GOSAT only provides a sparse spatial sampling with individual soundings separated by several hundred kilometers. Complementary, the Global Ozone Monitoring Experiment-2 (GOME-2) instruments onboard MetOp-A and MetOp-B enable SIF retrievals since 2007 with a continuous and global spatial coverage. GOME-2 measures in the red and near-infrared (NIR) spectral regions with a spectral resolution of 0.5 nm and a pixel size of up to 40x40 km2. Most recently, another global and spatially continuous data set of SIF retrievals at 740 nm spanning the 2003-2012 time frame has been produced from ENVISAT/SCIAMACHY. This observational scenario has been completed by the first fluorescence data from the NASA-JPL OCO-2 mission (launched in July 2014) and the upcoming Copernicus' Sentinel 5-Precursor to be launched in early 2016. OCO-2 and TROPOMI offer the possibility of monitoring SIF globally with a 100-fold improvement in spatial and temporal resolution with respect to the current measurements from the GOSAT, GOME-2 and SCIAMACHY missions.

In this contribution, we will provide an overview of existing global SIF data sets derived from space-based atmospheric spectrometers and will demonstrate the potential of such data to improve our knowledge of vegetation photosynthesis and gross primary production at the synoptic scale. We will show examples of ongoing research exploiting SIF data for an improved monitoring of photosynthetic activity in different ecosystems, including large crop belts worldwide, the Amazon rainforest and boreal evergreen forests.

Long (more than 30 years) time series of satellite-derived products over land are now available. They concern Essential Climate Variables (ECV) such as LAI, FAPAR, surface albedo, and soil moisture. The direct validation of such Climate Data Records (CDR) is not easy, as in situ observations are limited in space and time. Therefore, indirect validation has a key role. It consists in comparing the products with similar pre-existing products derived from satellite observations or from land surface model (LSM) simulations.

The most advanced indirect validation technique consists in integrating the products into a LSM using a data assimilation scheme. The obtained reanalysis accounts for the synergies of the various upstream products and provides statistics which can be used to monitor the quality of the assimilated observations. Meteo-France develops the ISBA-A-gs generic LSM able to represent the diurnal cycle of the surface fluxes together with the seasonal, interannual and decadal variability of the vegetation biomass. The LSM is embedded in the SURFEX modeling platform together with a simplified extended Kalman filter. These tools form a Land Data Assimilation System (LDAS). The current version of the LDAS assimilates PROBA-V LAI and ASCAT surface soil moisture (SSM) products over France (8km x 8km), and a passive monitoring of albedo, FAPAR and Land Surface temperature (LST) is performed (i.e., the simulated values are compared with the satellite products). The LDAS-France system is used in the European Copernicus Global Land Service (http://land.copernicus.eu/global/) to monitor the quality of upstream products.In situ SSM data over France are used to verify the outcomes of the analysis.

The LDAS is operated over France, and generates statistics whose trends can be analyzed in order to detect possible drifts in the quality of the products: (1) for LAI and SSM, metrics derived from the active monitoring (i.e. assimilation) such as innovations (observations vs. model forecast), residuals (observations vs. analysis), and increments (analysis vs. model forecast) ; (2) for albedo, LST, and FAPAR, metrics derived from the passive monitoring such as the Pearson correlation coefficient, z-score, RMSD, SDD, mean bias. The results obtained over the 2007-2015 period are presented. It is shown that these metrics can be used to detect inconsistencies in the satellite-derived SSM caused by changes in the SSM algorithm. The spatial and temporal variability of the SSM error is assessed.

This work shows that data assimilation is a key component of the validation of satellite-derived products. The LDAS is being extended to a global scale. At the same time, the coupling to hydrological models (now in a testing phase) will be consolidated, and this will allow the use of in situ river discharge observations for the validation of the whole system.

In-situ measurements of surface albedo are often used for the validation of satellite-based retrievals of land surface albedo products, which are in turn important datasets for climate and environmental modeling communities that need to estimate the land surface energy balance, and atmosphere-plant-soil fluxes. Validation using in-situ measurements is crucial for evaluating the accuracy of products, compliance with accuracy requirements and also improving retrieval algorithms. Validation identifies differences between ground and satellite-based measurements, which is determined by the apparent error between the two measurements and it’s associated uncertainty. The uncertainty of the apparent error is determined by the uncertainty of both measurements, however in reality it is difficult to quantify the individual contributions of uncertainty separately. This study introduces a novel model-based framework for assessing the individual contribution of uncertainty from the ground measurement protocols for land surface albedo.

A 3D Monte Carlo Ray Tracing (MCRT) radiative transfer model is used to simulate the ground measurement protocol in a range of complex 3D canopies, based on the main land cover types found on the earth’s surface. Contributions to uncertainty were identified, with a particular emphasis on the impact of ecosystem type, canopy structure, tree height, seasonal changes and the impact of fire on uncertainty of the in-situ measurement. In addition, compliance with World Meterological Organisation (WMO) accuracy requirement of 3% for in-situ measurement was tested under all scenarios. Results show that this unbiased model-based framework is able to quantify the uncertainty of in-situ measurements of albedo in various ecosystem types, and can also identify the contributions to this uncertainty.

This study aims to demonstrate the use of an unbiased model-based quality assurance framework that is able to identify individual contributions of uncertainty from in-situ measurements of albedo in numerous 3D canopies. Such a model-based framework can be used to start moving towards the development of a protocol for the quality assessment of in-situ measurements.

We present a generic inversion package (the JRC-TIP) enabling us to optimally retrieve land surface fluxes and associated land parameters based on a two-stream radiation transfer model. This  package implements the minimization of a cost function balancing 1) the misfit between observed and modeled remote sensing fluxes and 2) the deviation from prior knowledge on the RT model parameter values. This procedure delivers a Gaussian approximation of the probability density function (PDF) of the retrieved model parameters which characterize the radiative status of the vegetation-soil system. Propagation of this PDF to the flux space provides a Gaussian approximation of the posterior PDFs of the transmitted, absorbed and scattered fluxes within the vegetation layer, included those that are not measured, for example, the absorbed fraction by the background below vegetation or the fraction of photosynthetically active radiation (FAPAR).
 
The system is highly flexible and can assimilate any combination of narrowband, broadband or hyperspectral radiation flux observations. Another definite asset of the JRC-TIP lies in its capability to control and ultimately relax a number of assumptions that are often implicit in traditional approaches. Through a series of selected examples, the inverse procedure implemented in the JRC-TIP is shown to be robust, reliable and compliant with large scale processing requirements. This is demonstrated through application of the JRC-TIP to the MODIS collection 5 broadband albedo product at one km spatial resolution on global scale over a period of 14 years. The detailed analyses of the retrieval uncertainties highlight the central role and contribution of the effective LAI, the main process parameter to interpret radiation transfer observations over vegetated surfaces.

Newly derived TIP products from MODIS collection 5 albedos in 500 m resolution are compared to the 1 km version of the products. We also present JRC-TIP FAPAR and LAI data sets based on the broadband albedo product derived by the Globalbedo project, which are available for the period 2002-2011 globally in 1km resolution and aggregated to several lower resolutions. Finally, results from a comparison study of the JRC-TIP products, including the effective single scattering albedo of the canopy, against in-situ information available from a Fluxnet site will also be presented.