Estimation of the aerosol radiative forcing at ground level, over land, and in cloudless atmosphere, from METEOSAT-7 observation: method and case study

International audienc

Observation and assessment of model retrievals of surface exchange components over a row canopy using directional thermal data

Land surface temperature is an essential climate variable that can serve as a proxy for detecting water deficiencies in croplands and wooded areas. Its measurement can however be influenced by anisotropic properties of surface targets leading to occurrence of directional effects on the signal. This may lead to an incorrect interpretation of thermal measurements. In this study, we perform model assessments and check the influence of thermal radiation directionality using data over a vineyard. To derive the overall directional surface temperatures, elemental values measured by individual cameras were aggregated according to the respective cover fractions/weights in viewing direction. Aggregated temperatures from the turbid model were compared to corresponding temperatures simulated by the 3D DART radiative transfer model. The reconstructed temperatures were then used in surface-energy-balance (SEB) simulations to assess the impact of the Sun-target-sensor geometry on retrievals. Here, the pseudo-isotropic Soil-Plant-Atmosphere-Remote-Sensing-of-Evapotranspiration (SPARSE) dual-source model together with the non-isotropic version (SPARSE4), were used. Both schemes were able to retrieve overall fluxes satisfactorily, confirming a previous study. However, the sensitivity (of flux and component temperature estimates) of the schemes to viewing direction was tested for the first time using reconstructed sets of directional thermal data to force the models. Degradation (relative to nadir) in flux retrieval cross-row was observed, with better consistency along rows. Overall, it was nevertheless shown that SPARSE4 is less influenced by the viewing direction of the temperature than SPARSE, particularly for strongly off-nadir viewing. Some directional/asymmetrical artefacts are however not well reproduced by the simple Radiative Transfer Methods (RTM), which can then manifest in and influence the subsequent thermal-infrared-driven SEB modelling.This work was supported by the ALTOS project (PRIMA 2018 - Section 2), with grants provided by ANR via the agreement n°ANR-18-PRIM-0011-02 as well as the CNES/TOSCA program for the TRISHNA project. First author acknowledges the financial support of his PhD from CNES and Région Occitanie. The field experiments were carried out in the context of the HiLiaise and ESA WineEO projects. Joan Boldu (proprietor) and David Tous (SafSampling) are also acknowledged for allowing/providing access to the site and other site related data. Nicolas Lauret’s help with preparation of the DART mock-ups is appreciated.info:eu-repo/semantics/publishedVersio

Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France

International audienceA Land Data Assimilation System (LDAS) able to ingest surface soil moisture (SSM) and Leaf Area Index (LAI) observations is tested at local scale to increase prediction accuracy for water and carbon fluxes. The ISBAA-gs Land Surface Model (LSM) is used together with LAI and the soil water content observations of a grassland at the SMOSREX experimental site in southwestern France for a seven-year period (2001-2007). Three configurations corresponding to contrasted model errors are considered: (1) best case (BC) simulation with locally observed atmospheric variables and model parameters, and locally observed SSM and LAI used in the assimilation, (2) same as (1) but with the precipitation forcing set to zero, (3) real case (RC)simulation with atmospheric variables and model parameters derived from regional atmospheric analyses and from climatological soil and vegetation properties, respectively. In configuration (3) two SSM time series are considered: the observed SSM using Thetaprobes, and SSM derived from the LEWIS L-band radiometer located 15m above the ground. Performance of the LDAS is examined in the three configurations described above with either one variable (either SSM or LAI) or two variables (both SSM and LAI) assimilated. The joint assimilation of SSM and LAI has a positive impact on the carbon, water, and heat fluxes. It represents a greater impact than assimilating one variable (either LAI or SSM). Moreover, the LDAS is able to counterbalance large errors in the precipitation forcing given as input to the model

Reviews and syntheses: remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring

Global retrieval of bidirectional reflectance and albedo over land from EOS MODIS and MISR data: Theory and algorithm

This paper describes the theory and the algorithm to be used in producing a global bidirectional reflectance distribution function (BRDF) and albedo product from data to be acquired by the moderate resolution imaging spectroradiometer (MODIS) and the multiangle imaging spectroradiometer (MISR), both to be launched in 1998 on the AM-I satellite platform as part of NASA's Earth Observing System (EOS). The product will be derived using the kernel-driven semiempirical Ambrals BRDF model, utilizing five variants of kernel functions characterizing isotropic, volume and surface scattering. The BRDF and the albedo of each pixel of the land surface will be modeled at a spatial resolution of I km and once every 16 days in seven spectral bands spanning the visible and the near infrared. The BRDF parameters retrieved and recorded in the MODIS BRDF/albedo product will be intrinsic surface properties decoupled from the prevailing atmospheric state and hence suited for a wide range of applications requiring characterization of the directional anisotropy of Earth surface reflectance. A set of quality control flags accompanies the product. An initial validation of the Ambrals model is demonstrated using a variety of field-measured data sets for several different land cover types