0000000000248364
AUTHOR
Francisco Javier García-haro
Intercomparison of instruments for measuring leaf area index over rice
Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. LAI estimates can be classified as direct or indirect methods. Direct methods are destructive, time consuming, and difficult to apply over large fields. Indirect methods are non-destructive and cost-effective due to its portability, accuracy and repeatability. In this study, we compare indirect LAI estimates acquired from two classical instruments such as LAI-2000 and digital cameras for hemispherical photography, with LAI estimates acquired with a smart app (PocketLAI) installed on a mobile smartphone. In this work it is shown that LAI…
Capability assessment of the SEVIRI/MSG GPP product for the detection of areas affected by water stress
[ES] Se presenta el nuevo producto de producción primaria bruta (GPP) de EUMETSAT derivado a partir de datos del satélite geoestacionario SEVIRI/MSG (MGPP LSA-411) y se evalúa su potencial para detectar zonas afectadas por estrés hídrico (hot spots). El producto GPP se basa en la aproximación de Monteith, que modela la GPP de la vegetación como el producto de la radiación fotosintéticamente activa (PAR) incidente, la fracción de PAR absorbida (fAPAR) y un factor de eficiencia de uso de la radiación (ε). El potencial del producto MGPP para detectar hot spots se evalúa, utilizando un periodo corto de tres años, a escala local y regional, comparando con datos in situ derivados de medidas en to…
A methodology to generate a synergetic land-cover map by fusion of different land-cover products
Abstract The main goal of this study is to develop a general framework for building a hybrid land-cover map by the synergistic combination of a number of land-cover classifications with different legends and spatial resolutions. The proposed approach assesses class-specific accuracies of datasets and establishes affinity between thematic legends using a common land-cover language such as the UN Land-Cover Classification System (LCCS). The approach is illustrated over a large region in Europe using four land-cover datasets (CORINE, GLC2000, MODIS and GlobCover), but it can be applied to any set of existing products. The multi-classification map is expected to improve the performance of indiv…
Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe
One of the major drawbacks of land cover products is the lack of interoperability among them. Since their development was driven by different national or international initiatives, they were developed for different purposes and hold diverse technical characteristics. Thus, comparison among products and quality monitoring is necessary in assessing their usefulness. This paper provides a methodology to compare global land cover maps that allows for differences in legend definitions among products. Two different approaches were considered for map comparison, a Boolean approach and a new methodology based on fuzzy set theory in which the Land Cover Classification System (LCCS) acted as a genera…
Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications
Remote sensing is a key tool for precision agriculture applications as it is capable of capturing spatial and temporal variations in crop status. However, satellites often have an inadequate spatial resolution for precision agriculture applications. High-resolution Unmanned Aerial Vehicles (UAV) imagery can be obtained at flexible dates, but operational costs may limit the collection frequency. The current study utilizes data fusion to create a dataset which benefits from the temporal resolution of Formosat-2 imagery and the spatial resolution of UAV imagery with the purpose of monitoring crop growth in a potato field. The correlation of the Weighted Difference Vegetation Index (WDVI) from …
Retrieval of vegetation height in rice fields using polarimetric SAR interferometry with TanDEM-X data
This work presents for the first time a demonstration with satellite data of polarimetric SAR interferometry (PolInSAR) applied to the retrieval of vegetation height in rice fields. Three series of dual-pol interferometric SAR data acquired with large baselines (2–3 km) by the TanDEM-X system during its science phase (April–September 2015) are exploited. A novel inversion algorithm especially suited for rice fields cultivated in flooded soil is proposed and evaluated. The validation is carried out over three test sites located in geographically different areas: Sevilla (SW Spain), Valencia (E Spain), and Ipsala (W Turkey), in which different rice types are present. Results are obtained duri…
Global Estimation of Biophysical Variables from Google Earth Engine Platform
This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estim…
A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System
Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetation and agricultural land monitoring and modelling studies. This paper is aimed at comparing, validating and discussing different LAI satellite products from operational services and customized solution based on innovative Earth Observation (EO) data such as Landsat-7/8 and Sentinel-2A. The comparison was performed to assess overall quality of LAI estimates for rice, as a fundamental input of different scale (regional to local) operational crop monitoring systems such as the ones developed during the "An Earth obseRvation Model based RicE information Service" (ERMES) project. We adopted a multiscale approach f…
Airborne measurement of hot spot reflectance signatures
The so-called hot spot is a fine directional signature of the surface reflectance observed for phase angles close to zero. In this paper, we analyze and discuss several such signatures acquired from the airborne POLDER and HyMap instruments during the DAISEX'99 campaign over agricultural crops. The observed signatures are very similar to those acquired from space at a resolution of several tens of kilometers [J. Geophys. Res. 107 (2002)], which provides further evidence that the hot spot is a scale-free feature. The hot spots can be fitted by a two-parameter function (amplitude and width) of the phase angle derived from canopy radiative transfer modeling. The model predicts that the amplitu…
Retrieving leaf area index from multi-angular airborne data
This work is aimed to demonstrate the feasibility of a methodology for retrieving bio-geophysical variables whilst at the same time fully accounting for additional information on directional anisotropy. A model-based approach has been developed to deconvolve the angular reflectance into single landcovers reflectances, attempting to solve the inconsistencies of 1D models and linear mixture approaches. The model combines the geometric optics of large scale canopy structure with principles of radiative transfer for volume scattering within individual crowns. The reliability of the model approach to retrieve LAI has been demonstrated using data from DAISEX- 99 campaign at Barrax, Spain. Airborn…
Derivation of global vegetation biophysical parameters from EUMETSAT Polar System
Abstract This paper presents the algorithm developed in LSA-SAF (Satellite Application Facility for Land Surface Analysis) for the derivation of global vegetation parameters from the AVHRR (Advanced Very High Resolution Radiometer) sensor on board MetOp (Meteorological–Operational) satellites forming the EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Polar System (EPS). The suite of LSA-SAF EPS vegetation products includes the leaf area index (LAI), the fractional vegetation cover (FVC), and the fraction of absorbed photosynthetically active radiation (FAPAR). LAI, FAPAR, and FVC characterize the structure and the functioning of vegetation and are key par…
Development of an earth observation processing chain for crop bio-physical parameters at local scale
This paper proposes a full Earth observation processing chaing for biophysical parameter estimation at local scales. In particular, we focus on the Leaf Area Index (LAI) as an essential climate variable required for the monitoring and modeling of land surfaces at local scale. The main goal of this study is tied to the use of optical satellite images to retrieve Earth Observation (EO) biophysical parameters able to describe the spatio-temporal changes in agro-ecosystems at local scale. The objective of this work is two-fold: (i) to set up and update the EO products processing chain at high resolution (local) scale; and (ii) derive multitemporal LAI maps at 30 m resolution to be fed into a cr…
A global Canopy Water Content product from AVHRR/Metop
Abstract Spatially and temporally explicit canopy water content (CWC) data are important for monitoring vegetation status, and constitute essential information for studying ecosystem-climate interactions. Despite many efforts there is currently no operational CWC product available to users. In the context of the Satellite Application Facility for Land Surface Analysis (LSA-SAF), we have developed an algorithm to produce a global dataset of CWC based on data from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board Meteorological–Operational (MetOp) satellites forming the EUMETSAT Polar System (EPS). CWC reflects the water conditions at the leaf level and information related …
Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring
Abstract This paper presents an operational chain for high-resolution leaf area index (LAI) retrieval from multiresolution satellite data specifically developed for Mediterranean rice areas. The proposed methodology is based on the inversion of the PROSAIL radiative transfer model through the state-of-the-art nonlinear Gaussian process regression (GPR) method. Landsat and SPOT5 data were used for multitemporal LAI retrievals at high-resolution. LAI estimates were validated using time series of in situ LAI measurements collected during the rice season in Spain and Italy. Ground LAI data were collected with smartphones using PocketLAI, a specific phone application for LAI estimation. Temporal…
Multitemporal Monitoring of Plant Area Index in the Valencia Rice District with PocketLAI
Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAI(eff)) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAI(eff) measurements. It was used during an entire rice season for indirect PAI(eff) estimations and for deriving reference high-resolution PAI(eff) maps. Ground PAI(eff) value…
Monitoring fire-affected areas using Thematic Mapper data
In this paper three methods for updating inventories of burned areas have been presented and examined. They include Multitemporal Principal Component Analysis (MPCA), Change Vector Analysis (CVA) a...
Understanding deep learning in land use classification based on Sentinel-2 time series
AbstractThe use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model’s decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims …
Extraction of Endmembers from Spectral Mixtures
Abstract Linear spectral mixture modeling (LSMM) divides each ground resolution element into its constituent materials using endmembers which represent the spectral characteristics of the cover types. However, it is difficult to identify and estimate the spectral signature of pure components or endmembers which form the scene, since they vary with the scale and purpose of the study. We propose three different methods to estimate the spectra of pure components from a set of unknown mixture spectra. Two of the methods consist in different optimization procedures based on objective functions defined from the coordinate axes of the dominant factors. The third one consists in the design of a neu…
Vegetation vulnerability to drought in Spain
[EN] Frequency of climatic extremes like long duration droughts has increased in Spain over the last century.The use of remote sensing observations for monitoring and detecting drought is justified on the basis that vegetation vigor is closely related to moisture condition. We derive satellite estimates of bio-physical variables such as fractional vegetation cover (FVC) from MODIS/EOS and SEVIRI/MSG time series. The study evaluates the strength of temporal relationships between precipitation and vegetation condition at time-lag and cumulative rainfall intervals. From this analysis, it was observed that the climatic disturbances affected both the growing season and the total amount of vegeta…
A directional spectral mixture analysis method: application to multiangular airborne measurements
This study aims at developing an operational approach-namely, directional spectral mixture analysis (DISMA)-for retrieving vegetation parameters like fractional vegetation cover (FVC) and leaf area index (LAI) from multispectral and multiangular data. The approach attempts to highlight the consistency of one-dimensional models and linear mixture approaches. DISMA combines spectral signatures of soil and vegetation components with an analytical approximation of the radiative transfer equation, giving rise to a fast invertible bidirectional reflectance distribution function (BRDF) model of discontinuous canopies. Both the forward model and its inversion using a simple technique based on looku…
Drought Monitoring In The Mediterranean Basin Using The Seviri/Msg Gpp Product (Mgpp)
Recently, the Satellite Application Facility for Land Surface Analysis (LSA-SAF) has just released a new product that helps to characterize ecosystem processes, the 10-day LSA SAF GPP product from SEVIRI/MSG data (MGPP LSA-411). The GPP product is based on Monteith's concept, which models GPP as the product of the incoming photosynthetically active radiation (PAR), the fractional absorption of that flux $(\mathrm{f}_{APAR})$ and a light-use efficiency factor $(\varepsilon)$. Preliminary results on the use of the MGPP product in the assessment of ecosystem response to drought events are presented in this work for a short period of three years. A few sites located in the Mediterranean basin a…
A Mixture Modeling Approach to Estimate Vegetation Parameters for Heterogeneous Canopies in Remote Sensing
In this article, we describe a reflectance model which parametrizes the reflectance of vegetation canopies from optical properties of leaves and soil, and dominant canopy structural parameters. The model assumes certain principles of geometric models, for example, that sensor integrates the radiance reflected from three components, plant, shaded soil, and illuminated soil. Its inversion provides compositional information of the ground surface that is linked with the interpretation of the linear spectral mixture modeling (LSMM). This model also offers the potential for retrieving other meaningful biophysical properties such as LAI. The model has been tested on simulated spectra of spectral m…
A high-resolution, integrated system for rice yield forecasting at district level
Abstract To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 8…
Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site
The validation of coarse satellite-derived products from field measurements generally relies on up-scaling the field data to the corresponding satellite products. This question is commonly addressed through the generation of a reference high-resolution map of an area covering several moderate resolution pixels. This paper describes a method by which reference leaf area index (LAI) maps can be generated from ground-truth LAI measurements. The method is based on a multivariate ordinary least squares (OLS) algorithm which uses an iteratively re-weighted least squares (IRLS) algorithm. It provides an empirical relationship (i.e. a transfer function) between in situ measurements and concomitant …
Accuracy assessment of fraction of vegetation cover and leaf area index estimates from pragmatic methods in a cropland area
The fraction of vegetation cover (FVC) and the leaf area index (LAI) are important parameters for many agronomic, ecological and meteorological applications. Several in-situ and remote sensing techniques for estimating FVC and LAI have been developed in recent years. In this paper, the uncertainty of in-situ FVC and LAI measurements was evaluated by comparing estimates from LAI-2000 and digital hemispherical photography (DHP). The accuracy achieved with a spectral mixture analysis algorithm and two vegetation indices-based methods was assessed using atmospherically corrected Landsat Thematic Mapper (TM) data over the Barrax cropland area where the European Space Agency (ESA) SENtinel-2 and …
Patterns Comparison Between Gome-2 Sun-Induced Fluorescence and Msg Gross Primary Production
A comparison between maximum monthly MSG gross primary production (GPP) estimates with the sun-induced chlorophyll fluorescence (SIF) product from the Global Ozone Monitoring Experiment-2 (GOME-2) over Europe and Africa is presented as an indirect validation of MSG GPP estimates. The maximum daily GPP value for each month is derived from daily MSG GPP, which takes full advantage of the SEVIRI/MSG products from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) delivered by the Satellite Application Facility for Land Surface Analysis (LSA SAF). A linear relationship found between both products over savanna, grasslands and forests at high latitudes evidence…
Characterizing land condition variability in Ferlo, Senegal (2001–2009) using multi-temporal 1-km Apparent Green Cover (AGC) SPOT Vegetation data
Abstract The ecosystem state or ‘land condition’ can be characterized by a set of attributes, which show variations at different temporal scales. A multi-resolution analysis (MRA) based on the wavelet transform (WT) has been implemented to examine the land condition of a dryland region in Ferlo (Senegal) over the period 2001–2009. This methodology has proven to be useful for smoothing time series while considering those temporal resolutions that incorporate information about the vegetation dynamics. For this purpose, time series of the 1-km Apparent Green Cover (AGC) from the 10-day composites SPOT Vegetation (VGT) data are analyzed. Two relevant outputs from the MRA, A 1 (de-noised) and th…
Physics-aware Gaussian processes in remote sensing
Abstract Earth observation from satellite sensory data poses challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression has excelled in biophysical parameter estimation tasks from airborne and satellite observations. GP regression is based on solid Bayesian statistics, and generally yields efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations between the state vector and the radiance observations is available though and could be useful to improve pre…
Intercomparison and quality assessment of MERIS, MODIS and SEVIRI FAPAR products over the Iberian Peninsula
Abstract The fraction of absorbed photosynthetically active radiation (FAPAR) is a key variable in productivity and carbon cycle models. The variety of available FAPAR satellite products from different space agencies leads to the necessity of assessing the existing differences between them before using into models. Discrepancies of four FAPAR products derived from MODIS, SEVIRI and MERIS (TOAVEG and MGVI algorithms), covering the Iberian Peninsula from July 2006 to June 2007 are here analyzed. The assessment is based on an intercomparison involving the spatial and temporal consistency between products and a statistical analysis across land cover types. In general, significant differences ar…
Vegetation cover seasonal changes assessment from TM imagery in a semi-arid landscape
This work evaluates the suitability of spectral mixture analysis (SMA) methods to assess vegetation cover seasonal changes in a desertification context. Our main interest is to produce remotely sensed derived maps, sensitive to vegetation activity and quite independent of the soil background. A further aim is to analyse the inter-annual variations of this magnitude for different natural vegetation species, in response to seasonal and climatic changes. Fractional vegetation cover (FVC) was obtained using a Variable Endmember Spectral Mixture Analysis (VESMA) technique. The aim is to identify the main vegetation cover and lithological units and decompose them in separate stages. The use of sp…
Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter
Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR) is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study proposes an adapted (iteratively reweighted) local regression filter (LOESS) and performs a benchmarking intercomparison with four popular and generally applicable smoothing methods: Double Logistic (DLOG), sm…
A unified vegetation index for quantifying the terrestrial biosphere
[EN] Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross prim…
Joint Gaussian Processes for Biophysical Parameter Retrieval
Solving inverse problems is central to geosciences and remote sensing. Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in remote sensing applications (forward models). The numerical inversion of the RTM equations is a challenging and computationally demanding problem, and for this reason, often the application of a nonlinear statistical regression is preferred. In general, regression models predict the biophysical parameter of interest from the corresponding received radiance. However, this approach does not employ the physical information encoded in the RTMs. An alternative strategy, which attempts to include the physical knowledge, co…
Daily GPP estimates in Mediterranean ecosystems by combining remote sensing and meteorological data
The accurate representation of terrestrial CO2 uptake (GPP) using Monteith's approach requires a frequent and site-specific parameterization of the model inputs. In this work, an optimization of this approach has been carried out by adjusting the inputs (f(APAR), PAR and epsilon) for the study area, peninsular Spain, a typical Mediterranean region. The daily GPP images have been calculated for 2008 and 2011 with a 1-km spatial resolution and validated by comparison with in situ GPP estimates from the eddy covariance data (direct validation) and by inter-comparison with the MODIS GPP product. The direct validation has evidenced an excellent agreement with correlations up to 0.98 in 2008 and …
Prototyping of Land-SAF leaf area index algorithm with VEGETATION and MODIS data over Europe
Abstract The Satellite Application Facility on Land Surface Analysis (Land-SAF) aims to provide land surface variables for the meteorological and environmental science communities from EUMETSAT satellites. This study assesses the performance of a simplified (i.e. random distribution of vegetation is assumed) version of the Land-SAF algorithm for the estimation of Leaf Area Index (LAI) when prototyped with VEGETATION (processed in CYCLOPES program) and MODIS reflectances. The prototype estimates of LAI are evaluated both by comparison with validated CYCLOPES and MODIS LAI products derived from the same sensors and directly through comparison with ground-based estimates. Emphasis is given on …
Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning
The estimation of biophysical variables is at the core of remote sensing science, allowing a close monitoring of crops and forests. Deriving temporally resolved and spatially explicit maps of parameters of interest has been the subject of intense research. However, deriving products from optical sensors is typically hampered by cloud contamination and the trade-off between spatial and temporal resolutions. In this work we rely on the HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to generate long gap-free time series of Landsat surface reflectance data by fusing MODIS and Landsat reflectances. An artificial neural network is trained on PROSAIL inversion to p…
Retrieval of daily gross primary production over Europe and Africa from an ensemble of SEVIRI/MSG products
The main goal of this paper is to derive a method for a daily gross primary production (GPP) product over Europe and Africa taking the full advantage of the SEVIRI/MSG satellite products from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) sensors delivered from the Satellite Application Facility for Land Surface Analysis (LSA SAF) system. Special attention is paid to model the daily GPP response from an optimized Montheith's light use efficiency model under dry conditions by controlling water shortage limitations from the actual evapotranspiration and the potential evapotranspiration (PET). The PET was parameterized using the mean daily air temperatur…
Machine Learning Methods for Spatial and Temporal Parameter Estimation
Monitoring vegetation with satellite remote sensing is of paramount relevance to understand the status and health of our planet. Accurate and constant monitoring of the biosphere has large societal, economical, and environmental implications, given the increasing demand of biofuels and food by the world population. The current democratization of machine learning, big data, and high processing capabilities allow us to take such endeavor in a decisive manner. This chapter proposes three novel machine learning approaches to exploit spatial, temporal, multi-sensor, and large-scale data characteristics. We show (1) the application of multi-output Gaussian processes for gap-filling time series of…
A generalized soil-adjusted vegetation index
Operational monitoring of vegetative cover by remote sensing currently involves the utilisation of vegetation indices (VIs), most of them being functions of the reflectance in red (R) and near-infrared (NIR) spectral bands. A generalized soil-adjusted vegetation index (GESAVI), theoretically based on a simple vegetation canopy model, is introduced. It is defined in terms of the soil line parameters (A and B) as: GESAVI=(NIRBRA)/(R+Z), where Z is related to the red reflectance at the cross point between the soil line and vegetation isolines. As Z is a soil adjustment coefficient, this new index can be considered as belonging to the SAVI family. In order to analyze the GESAVI sensitivity to s…
Evaluation of the LSA-SAF gross primary production product derived from SEVIRI/MSG data (MGPP)
The objective of this study is to describe a completely new 10-day gross primary production (GPP) product (MGPP LSA-411) based on data from the geostationary SEVIRI/MSG satellite within the LSA SAF (Land Surface Analysis SAF) as part of the SAF (Satellite Application Facility) network of EUMETSAT. The methodology relies on the Monteith approach. It considers that GPP is proportional to the absorbed photosynthetically active radiation APAR and the proportionality factor is known as the light use efficiency ε. A parameterization of this factor is proposed as the product of a εmax, corresponding to the canopy functioning under optimal conditions, and a coefficient quantifying the reduction of …
Climate Data Records of Vegetation Variables from Geostationary SEVIRI/MSG Data: Products, Algorithms and Applications
The scientific community requires long-term data records with well-characterized uncertainty and suitable for modeling terrestrial ecosystems and energy cycles at regional and global scales. This paper presents the methodology currently developed in EUMETSAT within its Satellite Application Facility for Land Surface Analysis (LSA SAF) to generate biophysical variables from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board MSG 1-4 (Meteosat 8-11) geostationary satellites. Using this methodology, the LSA SAF generates and disseminates at a time a suite of vegetation products, such as the leaf area index (LAI), the fraction of the photosynthetically active radiation absorbed …
Linear spectral mixture modelling to estimate vegetation amount from optical spectral data
Abstract Spectral mixture modelling has developed in recent years as a suitable remote sensing tool for analysing the biophysical and compositional character of ground surfaces. In this paper the potentiality of the linear spectral mixture model to extract vegetation related parameters from 0·4-2·5 μm reflectance data has been tested. High spectral resolution reflectance measurements of soil-plant mixtures with different soil colour and plant densities were carried out in a laboratory experiment. The constrained least-squares and the factor analysis unmixing procedures were applied to generate endmember fractions of the components present in the mixtures and to test the validity of the mode…