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Two new remotely sensed Leaf Area Index (LAI) and Surface Soil Moisture (SSM) satellite products are compared with two sets of simulations of the ORCHIDEE and ISBA-A-gs land surface models to investigate how recent droughts affected vegetation over the Euro-Mediterranean area. We analyze the interannual variability over the period 1991-2008. The leaf onset and the Length of the vegetation Growing Period (LGP) are derived from the satellite-derived LAI and from the modelled LAI. The LGP values produced by the photosynthesis-driven phenology model of ISBA-A-gs are closer to the satellite-derived LAI LGP than those produced by ORCHIDEE. In the latter, the phenology is based on a growing degree-day model for leaf onset, and on both climatic conditions and leaf life span for senescence. Further, the interannual variability of LAI is better captured by ISBA-A-gs than by ORCHIDEE. The summer 2003 drought case study shows a relatively good agreement of the modelled LAI anomalies with the observations, but the two models underestimate plant regrowth in the autumn. A better representation of the root-zone soil moisture profile could improve the simulations of both models. The satellite-derived SSM is compared with SSM simulations of ISBA-Ags, only, as ORCHIDEE has no explicit representation of SSM. Overall, the ISBA-A-gs simulations of SSM agree well with the satellite-derived SSM and are used to detect regions where the satellite product could be improved. Finally, a correspondence is found between the interannual variability of detrended SSM and LAI. The predictability of LAI is less pronounced using remote sensing observations than using simulated variables. However, consistent results are found in July for the croplands of Ukraine and southern Russia. [ABSTRACT FROM AUTHOR]

Moderate resolution satellite sensors including MODIS already provide more than 10 yr of observations well suited to describe and understand the dynamics of the Earth surface. However, these time series are incomplete because of cloud cover and associated with significant uncertainties. This study compares eight methods designed to improve the continuity by filling gaps and the consistency by smoothing the time course. It includes methods exploiting the time series as a whole (Iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of few weeks to few months (Adaptive Savitzky-Golay filter (SGF), temporal smoothing and gap filling (TSGF) and asymmetric Gaussian function (AGF)) in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to MODIS leaf area index product for the period 2000-2008 over 25 sites showing a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that EMD, LPF and AGF methods were failing in case of significant fraction of gaps (%Gap>20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (respectively Clim) was able to fill more than 50% of the gaps for sites with more than 60% (resp. 80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the several methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with % Gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with % Gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with % Gap < 10% and increases rapidly with % Gap. TSGF provides the more accurate estimates of phenological timing up to % Gap < 60%. [ABSTRACT FROM AUTHOR]

Abstract: This study focuses on the directionality of wheat leaf reflectance as a function of leaf surface characteristics. Wheat leaf BRF measurements were completed under 45° zenith illumination angle in three visible broad spectral bands with a conoscope that provides very high angular resolution data over a large portion of the whole hemisphere, including around the illumination direction. The measurements show a clear anisotropy with a specular lobe in the forward scattering direction and a small but significant hotspot feature in the backward scattering direction. The BRF directional features further depend on the illumination orientation because of the leaf roughness created by longitudinal veins: the specular lobe was more pronounced when the illumination was perpendicular to the veins, while specular reflection was more spread over azimuths for longitudinal illumination. Moreover, a sharp hotspot feature was observed for transversal illumination where the apparent roughness is the largest. The scattering was tentatively decomposed into specular, hotspot and isotropic components. Results showed that the hotspot contribution to the directional hemispherical reflectance factor (DHRF) was marginal conversely to that of the specular component that ranges between 0.036 and 0.050 (absolute DHRF value). The specular component was almost the same in the three visible bands considered. The isotropic component originating from volume scattering was contributing the most to the DHRF and was depending on wavelength, ranging between 0.055 and 0.097 in absolute DHRF value. A simple model was proposed to estimate the volume scattering from the isotropic and the surface components. Consequences of these findings were drawn on the ability to estimate leaf biochemical composition independently from leaf surface scattering, as well as on the interpretation of remote sensing at the canopy level. [Copyright &y& Elsevier]

Abstract: This paper presents a novel database of ground and remotely sensed data from the United Kingdom, which is uniquely suited to scaling-up multispectral measurements from a single plot to the scale of satellite sensor observations. Multiple aircraft and satellite sensors were involved, and most of the data were acquired on a single day in June 2006, providing a synoptic view which, at its largest extent, covered most of southern England and Wales. Three airborne imaging spectrometers were involved (Specim AISA Eagle, Itres CASI-2 and -3) and three satellite sensors (UK-DMC, PROBA/CHRIS, and SPOT HRG), complemented with airborne LiDAR, multispectral survey cameras, and ground measurements (land cover, LAI, reflectance factors, and atmospheric measurements). In this paper the NCAVEO Field Campaign (NFC) database is described and an example of its use to produce a high spatial resolution leaf area index map for the validation of medium-resolution products (MODIS, VEGETATION, and MERIS) is presented. [Copyright &y& Elsevier]

The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. However the accuracy of the estimates depends on a range of factors, most notably the realism with which the canopy is represented by the models and the possibility of introducing a priori knowledge on canopy characteristics to constrain the inversion procedure. The objective of the present work was to compare the performances and operational limitations of two contrasting types of radiative transfer models: a classical one-dimensional canopy reflectance model, PROSPECT+SAIL (PROSAIL), and a three-dimensional dynamic (4-D) maize model. The latter introduces greater realism into the description of the canopy structure and implicit a priori information on the crop. The assessment was carried out with multiple view angle data recorded from field experiments on maize at stages V5 to V8. The simplex numerical optimization algorithm was used to invert the two models, using spectral reflectance data for PROSAIL and gap fraction data for the 4-D maize model. Leaf area index (LAI) was estimated with a RMSE of 0.48 for PROSAIL and 0.35 for the 4-D model. Retrieval of average leaf inclination angle (ALA) was problematic with both models. The effect of the number and distribution of observation view angles was examined, and the results highlight the advantage of oblique angle measurements. [ABSTRACT FROM AUTHOR]

Spectral indices from remotely sensed data, such as the Normalized Difference Vegetation Index (NDVI), are often used to estimate biophysical characteristics of vegetation. The objective of this study is to evaluate the effect of senescent leaves on the estimation of the fraction of photosynthetically active radiation absorbed by the green elements of the canopy ( f APAR g ) from NDVI measurements. An experiment was conducted under controlled conditions over grass canopies. Both NDVI and f APAR g were measured when the cover fraction by senescent leaves was changed. The results demonstrated that the effect of senescent leaves was significant on NDVI values. A similar effect was observed on the f APAR g values. In these conditions, simple models were developed to relate the f APAR g to NDVI values when the cover fractions of the senescent leaves vary. When combining these models, a linear relationship was found between NDVI and f APAR g , which was in good agreement with our experimental observations. Complementary radiative transfer model simulations were then run and confirmed these results. In addition, the vertical and horizontal distribution of the senescent leaves was investigated. They show that the relationship between NDVI and f APAR g was only marginally affected by the distribution of the senescent leaves. [ABSTRACT FROM AUTHOR]

This paper outlines the strategies available for estimating the biophysical properties of crop canopies from remotely sensed data. Spectral reflectance and biophysical data were obtained over 132 plots of sugar beet ( Beta vulgaris L.) and in the first part of the paper the strength of the relationships between vegetation indices (VI) and leaf area index (LAI) are examined. In the second part, an approach is tested in which a canopy reflectance model is used to generate simulated spectra for a wide range of biophysical conditions and these data are used to train an artificial neural network (ANN). The advantage of the second approach is that a priori knowledge of the measurement conditions including soil reflectance, canopy architecture and solar position can be included explicitly in the modelling. The results show that the estimation of sugar beet LAI using a trained neural network is more reliable than the use of VI and has the potential to replace the use of VI for operational applications. The use of a priori data on the variation in soil spectral reflectance gave rise to a small increase in LAI estimation accuracy. [ABSTRACT FROM AUTHOR]

This study focused on atmospheric corrections of airborne thermal infrared remote sensing data acquired with a multidirectional and single broadband channel sensor during the ReSeDA experiment. For single channel sensors, atmospheric corrections are generally performed using atmospheric radiative transfer models such as MODTRAN 3.5 along with radiosoundings. A sensitivity study was performed using MODTRAN 3.5 simulations to assess the accuracy of the processing regarding the experimental context. It was shown that the local topography and the atmosphere spatial variability could affect significantly the radiosounding representativeness, whereas the fluctuations of the flight altitude around the nominal value induced non-negligible inaccuracies. Moreover, using a broadband sensor induced non-linear effects that required a second order correction and also the use of look-up tables to reduce computation time. A theoretical error was next proposed to account for both the sensor accuracy and the experimental uncertainties considered when performing the sensitivity study. This theoretical error was about 1°C, and agreed well with the results obtained when validating against field measurements. [ABSTRACT FROM AUTHOR]

Estimation of canopy biophysical variables from remote sensing data was investigated using radiative transfer model inversion. Measurement and model uncertainties make the inverse problem ill posed, inducing difficulties and inaccuracies in the search for the solution. This study focuses on the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process. For this purpose, lookup table (LUT), quasi-Newton algorithm (QNT), and neural network (NNT) inversion techniques were adapted to account for prior information. Results were evaluated over simulated reflectance data sets that allow a detailed analysis of the effect of measurement and model uncertainties. Results demonstrate that the use of prior information significantly improves canopy biophysical variables estimation. LUT and QNT are sensitive to model uncertainties. Conversely, NNT techniques are generally less accurate. However, in our conditions, its accuracy is little dependent significantly on modeling or measurement error. We also observed that bias in the reflectance measurements due to miscalibration did not impact very much the accuracy of biophysical estimation. [Copyright &y& Elsevier]

Presents the correction to the figure presented in the article previously published in 'International Journal of Remote Sensing' as of December 10, 2002.

High temporal frequency remote sensing observations are required to monitor vegetation functioning. These observations are currently provided by moderate resolution sensor (with pixel size ranging from 250 m to 10 km). However, the intra-pixel spatial heterogeneity which may be important at moderate resolution, induces a scaling bias on non-linear estimation processes of land surface variable. A possible strategy to correct this scaling bias consists in using variogram model of high spatial resolution data (e.g. SPOT/HRV 20 m) as a proxy for the spatial heterogeneity within moderate resolution pixel. However, ways have to be found to get prior knowledge of this intra-pixel spatial heterogeneity metric without systematic concurrent high spatial resolution images. This paper aims at proposing a spatio-temporal model of the variogram of high spatial resolution data, which enables us to retrieve the spatial heterogeneity within moderate spatial resolution pixel using a temporal sampling of few high spatial resolution scenes. It capitalizes on variogram modeling of a time series of high spatial resolution NDVI images to quantify and model the temporal changes in landscape spatial heterogeneity over a particular crop site (Fundulea, Romania). We first demonstrate that important temporal variations in surface spatial heterogeneity observed over an agricultural site mainly result from the shift in seasonal trajectories between crop classes (here winter versus summer crops). The mean length scale as measured by the variogram integral range is mainly influenced by the gathering of fields with similar NDVI values. The scene overall spatial variability and the spatial heterogeneity within moderate resolution pixels, as quantified by the variogram sill and the dispersion variance, respectively, increase with the difference in NDVI between winter and summer crops. The influence of surface spatial heterogeneity on the description of land surface processes is thus critical when the phenological variability between crop classes is maximum, suggesting that the number of high spatial resolution scenes should increase at these periods of the seasonal cycle. Then, based on these observations, we build a model describing the temporal course of surface spatial heterogeneity, i.e. the temporal trajectory of the variogram of high spatial resolution NDVI image, as a function of crop seasonality. Once calibrated from a temporal sampling of few high spatial resolution scenes, this model proves to be powerful to predict the variogram at a date at which the high spatial resolution scene is not available and thus to retrieve the spatial heterogeneity within moderate resolution pixels through the seasonal cycle within a mean relative uncertainty of 20%. [Copyright &y& Elsevier]

The monitoring of earth surface processes at a global scale requires high temporal frequency remote sensing observations provided up to now by moderate spatial resolution sensors (from 250 m to 7 km). Non-linear estimation processes of land surface variables derived from remote sensing data can be biased by the surface spatial heterogeneity within the moderate spatial resolution pixel. Quantifying this surface spatial heterogeneity is thus required to correct non-linear estimation processes of land surface variables. The first step in this process is to properly characterize the scale of spatial variation of the processes structuring the landscape. Since the description of land surface processes generally involves various spectral bands, a multivariate approach to characterize the surface spatial heterogeneity from multi-spectral remote sensing observations has to be established. This work aims at quantifying the landscape spatial heterogeneity captured by red and near infrared high spatial resolution images using direct and cross-variograms modeled together with the geostatistical linear model of coregionalization. This model quantifies the overall spatial variability and correlation of red and near infrared reflectances over the scene. In addition, it provides an explicit understanding of the landscape spatial structures captured by red and near infrared reflectances and is thus appropriate to describe landscapes composed of areas with contrasted red and near infrared spectral properties. The application of the linear model of coregionalization to 18 contrasted landscapes provides a spatial signature of red and near infrared spectral properties characterizing each type of landscape. Low vegetation cover sites are characterized by positive spatial correlation between red and near infrared. The mosaic pattern of vegetation fields and bare soil fields over crop sites generates high and negative spatial correlation between red and near infrared and increases the spatial variability of red and near infrared. On forest sites, the important amount of vegetation limits the spatial variability of red and the shadow effects mainly captured by near infrared induce a low and positive spatial correlation between red and near infrared. Finally, the linear model of coregionalization applied to red and near infrared is shown to be more powerful than the univariate variogram modeling applied to NDVI because the second order stationarity hypothesis on which variogram modeling relies is more frequently verified for red and near infrared than for NDVI. [Copyright &y& Elsevier]

Abstract: The monitoring of earth surface dynamic processes requires global observations of the structure and the functioning of vegetation. Moderate resolution sensors (with pixel size ranging from 250 m to 7 km) provide frequent estimates of biophysical variables to characterize vegetation such as the leaf area index (LAI). However, the computation of LAI from moderate resolution remote sensing data induces a scaling bias on the LAI estimate if the moderate resolution pixel is heterogeneous and if the transfer function that relates remote sensing data to LAI is non-linear. This study provides a model to evaluate and correct the scaling bias. The model is built first for a univariate semi-empirical transfer function relating LAI directly to NDVI. The scaling bias is a function of (i) the degree of non-linearity of the transfer function quantified by its second derivative and (ii) the spatial heterogeneity of the moderate resolution pixel quantified by the variogram of the high spatial resolution (20 m) NDVI image. Then, the model is extended to a bivariate transfer function where LAI is related to red and near infrared reflectances. The scaling bias depends on (i) the Hessian matrix of the transfer function and (ii) the variograms and cross variogram of the red and near infrared reflectances. The scaling bias is investigated on several distinct landscapes from the VALERI database. Adjusting for scaling bias is critical on crop sites which are the most heterogeneous sites at the landscape level. Regarding the univariate transfer function, the magnitude of the scaling bias increases rapidly with pixel size until this size is larger than the typical spatial scale of the data. For the bivariate transfer function, it results from the addition of several components that may add up or cancel each other out. It is thus more difficult to analyze. The accuracy of the model to estimate the scaling bias is discussed. It depends mainly on the ability of the variograms and cross variogram to represent the local dispersion variances and covariance within the moderate resolution pixel. The model is generally highly accurate at 1000 m spatial resolution for the univariate transfer function and less accurate for the bivariate transfer function. [Copyright &y& Elsevier]

Abstract: The monitoring of earth surface dynamic processes at a global scale requires high temporal frequency remote sensing observations which are provided up to now by moderate spatial resolution sensors. However, the spatial heterogeneity within the moderate spatial resolution pixel biases non-linear estimation processes of land surface variables from remote sensing data. To limit its influence on the description of land surface processes, corrections based on the quantification of the intra-pixel heterogeneity may be applied to non-linear estimation processes. A complementary strategy is to define the proper pixel size to capture the spatial variability of the data and minimize the intra-pixel variability. This work provides a methodology to characterize and quantify the spatial heterogeneity of landscape vegetation cover from the modeling of the variogram of high spatial resolution NDVI data. NDVI variograms for 18 landscapes extracted from the VALERI database show that the land use is the main factor of spatial variability as quantified by the variogram sill. Crop sites are more heterogeneous than natural vegetation and forest sites at the landscape level. The integral range summarizes all structural parameters of the variogram into a single characteristic area. Its square root quantifies the mean length scale (i.e. spatial scale) of the data, which varies between 216 and 1060 m over the 18 landscapes considered. The integral range is also used as a yardstick to judge if the size of an image is large enough to measure properly the length scales of the data with the variogram. We propose that it must be smaller than 5% of the image surface. The theoretical dispersion variance, computed from the variogram model, quantifies the spatial heterogeneity within a moderate resolution pixel. It increases rapidly with pixel size until this size is larger than the mean length scale of the data. Finally based on the analysis of 18 landscapes, the sufficient pixel size to capture the major part of the spatial variability of the vegetation cover at the landscape scale is estimated to be less than 100 m. Since for all the heterogeneous landscapes the loss of NDVI spatial variability was small at this spatial resolution, the bias generated by the intra-pixel spatial heterogeneity on non-linear estimation processes will be reduced. [Copyright &y& Elsevier]

Abstract Surface soil moisture content (SMC) is known to impact soil reflectance at all wavelengths of the solar spectrum. As a consequence, many semi-empirical methods aim at inferring SMC from soil reflectance, but very few rely on physically-based models. This article presents a multilayer radiative transfer model of soil reflectance called MARMIT (multilayer radiative transfer model of soil reflectance) as a function of SMC given on a mass basis and a method called MARMITforSMC to estimate it from soil reflectance spectra. This model depicts a wet soil as a dry soil covered with a thin film of water. It is used to assess SMC over seven independent laboratory datasets gathered from the literature. A learning phase is required to link the thickness of the water film with the SMC. For that purpose, a sigmoid function, the parameters of which are related to soil physical and chemical properties such as porosity, grain size and mineralogy composition, is fitted. SMC can be inferred with good accuracy (RMSE ≈ 3%) if the learning step is applied soil by soil. The link between SMC and water thickness actually depends on soil texture and chemical composition. If the soils are divided into classes and if the learning phase is applied to a class, the RMSE slightly increases up to 5%. Finally, MARMITforSMC provides lower RMSE than any other existing semi-empirical or physically-based method. Highlights • A multilayer radiative transfer model of soil reflectance as a function of surface water content is developed. • A new method of SMC retrieval is developed. • SMC retrieval combines good accuracy and efficiency after a soil classification. • The new method is compared to other SMC retrieval methods. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this study is to evaluate the performances of estimating agronomic variables, such as total above ground biomass at key stages, or yield, from LAI data that could potentially be obtained from remote sensing observations. Approaches based either on empirical relationships or on forcing LAI within the STICS model () are considered, with emphasis on the effect of the accuracy and frequency of LAI data used. Both actual and simulated case studies on wheat for Northern France conditions were investigated under several levels of knowledge of the model input parameters and initial conditions. The results highlight the interest of using model based approaches for the estimation of agronomic variables. Forcing LAI data into the crop model allows compensating for the lack of detailed knowledge on management practices or soil characteristics. However, error and frequency of LAI observations may have an important impact on the estimation of agronomic variables, particularly for the early growth stages. In these conditions, an empirical approach, based on the calibration of a relationship between LAI at a given stage and the agronomic variable, provides an efficient alternative, though the validity of empirical relationships depends greatly on the database on which they have been obtained. [Copyright &y& Elsevier]

This study focuses on albedo mapping over agricultural surfaces using multidirectional and multispectral remote sensing data. These data were acquired using the airborne PolDER sensor during the Remote Sensing Data Assimilation (ReSeDA) experiment. The data set allowed to perform a validation over the growth cycles of several crops. Problems induced by mixed pixels were reduced since the ground spatial resolution was 20 m. First, linear kernel-driven bidirectional reflectance distribution function (BRDF) models were used to retrieve the BRDF and then to compute the hemispherical reflectance in the PolDER channels. We tested the four most classical models: Li-Ross, MRPV, Roujean, and Walthall. They presented similar interpolation performances, whereas the quality of the hemispherical reflectance estimates was also driven by the extrapolation performances. Second, the albedo was computed as a linear combination of the waveband hemispherical reflectances. We used several sets of coefficients proposed in the literature for different sensors. The validation of the albedo maps against field measurements showed that it was possible to achieve a relative accuracy about 9% when using an appropriate coefficient set. [ABSTRACT FROM AUTHOR]