GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production
TL;DR: Improved global estimates of LAI, FAPAR and FCOVER variables are developed by capitalizing on the development and validation of already existing products by training neural networks to estimate these fused and scaled products from SPOT-VEGETATION top of canopy directionally normalized reflectance values.
About: This article is published in Remote Sensing of Environment.The article was published on 2013-10-01 and is currently open access. It has received 430 citations till now.
Summary (3 min read)
Jump to: [2-Neural network calibration:] – [3-Application of the network:] – [2.2 Generation of training dataset] – [2.2.1 Selection of products] – [2.2.2 Spatial and temporal sampling for the training dataset] – [2.2.3 Fusing the products] – [2.4 Associated uncertainties and quality assessment] – [2.4.1 Input out of range] – [2.4.2 Output out of range] – [3 Operational production and dissemination] and [4 CONCLUSION]
2-Neural network calibration:
- A neural network is trained to estimate these 'best estimates' from the input reflectance values as observed by specific sensors and the associated geometrical configuration.
- Quality flags and quantitative uncertainties are also derived.
3-Application of the network:
- Once the network is calibrated, it is run to provide estimates of the biophysical variables for each of the sensors considered, along with the quality flags and quantitative uncertainties.
- Note that it would have been possible to follow more formally the scheme proposed by (Verger et al. 2008 ) and later developed in (Verger et al. 2011) .
- However this would need to use concurrently and in real time two (or more) sensors.
- This was not compatible with the available processing capacity for GEOV1.
- Further, the use of a single product in the learning database as proposed in (Verger et al. 2011 ) such as MODIS collection 5 would not allow improvement of the biases sometimes observed, but would mainly decrease the frequency of missing data and smooth the temporal series.
2.2 Generation of training dataset
- The way the training dataset is generated from already existing products is sketched in Figure 1 , top box.
- Four main steps are identified: (1) selection of the most relevant products, (2) setting the products on consistent spatial and temporal supports, (3) fusing the products and (4) eventually scaling the fused products.
- Details of each of these steps are given in the following.
2.2.1 Selection of products
- Apart from CYCLOPES FCOVER products, no other global FCOVER product is currently available apart from the SAF-LAND products covering the METEOSAT disk (Camacho-de Coca et al. 2006) .
- Several studies have pointed out that NDVI could be a good proxy for FCOVER (Baret et al.
- Camacho-de Coca et al. (2006) compared several regional FCOVER products over Africa and showed that the CYCLOPES FCOVER product was very consistent with other products although a significant and systematic bias was observed.
- It is therefore proposed to select the CYCLOPES FCOVER original product while rescaling it to provide values more consistent with ground measurements as detailed later.
2.2.2 Spatial and temporal sampling for the training dataset
- The temporal sampling used to fuse MODIS and CYCLOPES products will be that of the CYCLOPES original products, i.e. dekadal (10 days).
- It will allow using directly the normalized reflectance values derived from VEGETATION based on the CYCLOPES preprocessing algorithm (Baret et al. 2007 ) that will also constitute the GEOV1 temporal sampling.
- Finally, for the GEOV1 dates fulfilling the above criterions both for CYCLOPES and MODIS products, the LAI and FAPAR values corresponding to the 70% percentile was computed for CYCLOPES and MODIS.
- This allows minimizing the influence of possible residual cloud contamination and atmospheric effects that negatively biased the product values (Chen et al. 2006) .
- Because of the homogeneity of the sites and the short time period considered, the values once filtered as described above, should be closely distributed around the median, i.e. LAI or FAPAR values at 50% and 70% frequencies should be very close together for a given date and site.
2.2.3 Fusing the products
- These ground measurements are not very numerous (Camacho et al.
- Further, it is not advisable to use the validation data to calibrate an algorithm in order to preserve the required independency between the calibration and the validation processes.
- For these reasons, the weight used in the fusion between MODIS and CYCLOPES were based on heuristic arguments.
2.4 Associated uncertainties and quality assessment
- All the quality control flags associated to the top of canopy reflectance values are available along with the products.
- They describe the nature of the surface (land/sea), the presence of snow, the possible contamination by clouds or cloud shadow, the aerosol characteristics used for the atmospheric correction, and the possible saturation of the radiometric signal.
- Two additional qualitative assessment criterions more directly dedicated to the biophysical products are provided along with a quantitative estimation of the associated uncertainties.
- The way they are computed is described here after.
2.4.1 Input out of range
- Since the algorithm is based on a learning machine approach, it is important to verify whether the inputs of a given observation keeps within the range of variation of the training dataset called here the definition domain.
- If this condition is not fulfilled, the network will run in extrapolation mode, with no warranty about the realism of the outputs.
- The definition domain is limited by the convex hull formed in the BRF feature space by the cases used in the training process .
- For the sake of simplicity and ease of implementation, the 3D feature space formed by B2, B3 and SWIR bands was gridded by dividing the range of variation of each band (.
2.4.2 Output out of range
- The physical limits for the three variables are described in Table 3 .
- For LAI, the upper limit is not a physical limit, but a value just slightly higher than the maximum value that can be reached by the MODIS and CYCLOPES original products.
- The product uncertainty value will be also set to its maximum value.
3 Operational production and dissemination
- The GEOV1 products are generated in multi-band hdf5 format (the variable, its uncertainty, the quality flag, the number of input observations, the land-sea mask) and in tiles of 10°x10° covering the land surfaces of the whole globe.
- They are available in open access through the Geoland2 web platform (WWW4) where users can browse the catalogue, order the products after registration, and subscribe to receive the products.
- The GEOV1 products are also disseminated via the Eumetcast system to African and South American users.
4 CONCLUSION
- These problems call for an improved spatial resolution that will allow resolving most of the vegetation patches and will authorize identifying the corresponding vegetation type from the past observations and use it as prior information.
- Such systems are currently being available with hectometric resolutions, such as the PROBA-V (300m daily), Sentinel-3 (300 m every 2 days), VIIRS (370m daily).
- Decametric systems such as Sentinel 2 or LDCM in combination with the previous hectometric ones would probably provide the most efficient observation system.
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Figures (16)
Figure 1. Schematic description of the principle used to develop the GEOV1 product. 105 ![Figure 5. Relationships between NDVI and both FAPAR (a) and LAI (b) ‘best estimates’. 317 The red points correspond to the cases rejected that are outside the [5% 95%] percentile 318 range. 319](/figures/figure-5-relationships-between-ndvi-and-both-fapar-a-and-lai-2rknw8b2.png)
Figure 5. Relationships between NDVI and both FAPAR (a) and LAI (b) ‘best estimates’. 317 The red points correspond to the cases rejected that are outside the [5% 95%] percentile 318 range. 319 
Table 2. Range of variation of BRF values (Minimum and maximum) observed in the training 420 dataset and used to compute the definition domain. 421 
Figure 10. Definition domain for input VEGETATION Top of canopy directionally normalized 415 BRF values in B2, B3 and SWIR bands. Axes are scaled between 1 to 10, corresponding to the 10 416 classes of reflectance values ranging between the minimum value (class 1) and the maximum 417 value (class 10). The cells containing data from the training dataset are represented in black. 418 They form the definition domain. 419 
Table 3. Minimum, Maximum, Resolution and Tolerance values used to raise the output 434 out of range flag. 435 
Figure 2. The 420 BELMANIP2 sites used to sample vegetation types and conditions. 176 
Figure 4. Cumulated frequency of CYCLOPES (green solid line), MODIS (red solid 259 line) fused (dashed black line for FAPAR) and scaled (black solid line) LAI (a), FAPAR 260 (b) and FCOVER (c) products as observed over the 420 BELMANIP2 sites during years 261 2003-2004. 262 ![Figure 9. Difference between the LAI estimated from the NNT (LAIest) and the ‘best estimates’ 386 LAI value used for the training (LAItrain) for the four main vegetation types : Evergreen 387 Broadleaf Forest (EBF), Needle leaf Forest (ENF), Deciduous Broadleaf Forest (DBF) and Non-388 Forest. The red line, blue box and black whiskers represent respectively 50%, [25%,75%] and 389 [5%-95%] percentiles. Outliers are indicated by stars. 390](/figures/figure-9-difference-between-the-lai-estimated-from-the-nnt-1mlce9l7.png)
Figure 9. Difference between the LAI estimated from the NNT (LAIest) and the ‘best estimates’ 386 LAI value used for the training (LAItrain) for the four main vegetation types : Evergreen 387 Broadleaf Forest (EBF), Needle leaf Forest (ENF), Deciduous Broadleaf Forest (DBF) and Non-388 Forest. The red line, blue box and black whiskers represent respectively 50%, [25%,75%] and 389 [5%-95%] percentiles. Outliers are indicated by stars. 390 
Table 1. The currently LAI, FAPAR and FCOVER products available globally at 46 approximately 1 km spatial sampling distance. 47 ![Figure 8. Comparison between the ‘best estimates’ of LAI (a), FAPAR (b) and FCOVER (c) 373 products and the values estimated from the trained neural networks (called NNT estimates). The 374 solid thick line corresponds to the median value. Dark, medium and light gray areas correspond 375 respectively to [25% 75%] [10% 90%] and [1% 99%] of the estimated cases for each class of 376 actual values (20 classes are considered, from 0 to the maximum of actual values). The data 377 correspond to the test data set (n=1420). 378](/figures/figure-8-comparison-between-the-best-estimates-of-lai-a-10jppzsy.png)
Figure 8. Comparison between the ‘best estimates’ of LAI (a), FAPAR (b) and FCOVER (c) 373 products and the values estimated from the trained neural networks (called NNT estimates). The 374 solid thick line corresponds to the median value. Dark, medium and light gray areas correspond 375 respectively to [25% 75%] [10% 90%] and [1% 99%] of the estimated cases for each class of 376 actual values (20 classes are considered, from 0 to the maximum of actual values). The data 377 correspond to the test data set (n=1420). 378 
Figure 7. Structure of the neural network used to derive LAI, FAPAR and FCOVER (the ‘Var’ 363 box) from VEGETATION input reflectance in red (B2), NIR (B3) and SWIR as well as 364 
Figure 12. Theoretical performances of the neural network model used to describe RMSE values 460 for LAI (a) FAPAR (b) and FCOVER (c) from input reflectance values and geometry of 461 observation. 462 
Figure 6. Relationship between LAI and FAPAR for CYCLOPES (CYCV31 a), MODIS 326 (MODC5 b) and ‘best estimates’ (c) as observed over the 420 BELMANIP2 sites during 327 2003-2004 period (14200 points). 328 
Figure 3. Variation of the weight (w) as a function of the LAICYC value. 241 
Table 4. Values of the uncertainty model used for the input BRFs. From Baret et al. 449 (2007). 450 
Figure 11. Scheme showing how the uncertainties attached to the products were 447 computed 448
![Figure 5. Relationships between NDVI and both FAPAR (a) and LAI (b) ‘best estimates’. 317 The red points correspond to the cases rejected that are outside the [5% 95%] percentile 318 range. 319](/figures/figure-5-relationships-between-ndvi-and-both-fapar-a-and-lai-2rknw8b2.webp)
![Figure 9. Difference between the LAI estimated from the NNT (LAIest) and the ‘best estimates’ 386 LAI value used for the training (LAItrain) for the four main vegetation types : Evergreen 387 Broadleaf Forest (EBF), Needle leaf Forest (ENF), Deciduous Broadleaf Forest (DBF) and Non-388 Forest. The red line, blue box and black whiskers represent respectively 50%, [25%,75%] and 389 [5%-95%] percentiles. Outliers are indicated by stars. 390](/figures/figure-9-difference-between-the-lai-estimated-from-the-nnt-1mlce9l7.webp)
![Figure 8. Comparison between the ‘best estimates’ of LAI (a), FAPAR (b) and FCOVER (c) 373 products and the values estimated from the trained neural networks (called NNT estimates). The 374 solid thick line corresponds to the median value. Dark, medium and light gray areas correspond 375 respectively to [25% 75%] [10% 90%] and [1% 99%] of the estimated cases for each class of 376 actual values (20 classes are considered, from 0 to the maximum of actual values). The data 377 correspond to the test data set (n=1420). 378](/figures/figure-8-comparison-between-the-best-estimates-of-lai-a-10jppzsy.webp)
Citations
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01 Jan 2020
TL;DR: In this article, the authors examined the detection of the greening signal, its causes and its consequences, and showed that greening is pronounced over intensively farmed or afforested areas, such as in China and India, reflecting human activities.
Abstract: Vegetation greenness has been increasing globally since at least 1981, when satellite technology enabled large-scale vegetation monitoring. The greening phenomenon, together with warming, sea-level rise and sea-ice decline, represents highly credible evidence of anthropogenic climate change. In this Review, we examine the detection of the greening signal, its causes and its consequences. Greening is pronounced over intensively farmed or afforested areas, such as in China and India, reflecting human activities. However, strong greening also occurs in biomes with low human footprint, such as the Arctic, where global change drivers play a dominant role. Vegetation models suggest that CO2 fertilization is the main driver of greening on the global scale, with other factors being notable at the regional scale. Modelling indicates that greening could mitigate global warming by increasing the carbon sink on land and altering biogeophysical processes, mainly evaporative cooling. Coupling high temporal and fine spatial resolution remote-sensing observations with ground measurements, increasing sampling in the tropics and Arctic, and modelling Earth systems in more detail will further our insights into the greening of Earth. Vegetation on Earth is increasing, potentially leading to a larger terrestrial carbon sink. In this Review, we discuss the occurrence of this global greening phenomenon, its drivers and how it might impact carbon cycling and land-atmosphere heat and water fluxes.
722 citations
TL;DR: A review of state-of-the-art retrieval methods for quantitative terrestrial bio-geophysical variable extraction using optical remote sensing imagery and the prospects of implementing these methods into future processing chains for operational retrieval of vegetation properties are presented and discussed.
Abstract: Forthcoming superspectral satellite missions dedicated to land monitoring, as well as planned imaging spectrometers, will unleash an unprecedented data stream. The processing requirements for such large data streams involve processing techniques enabling the spatio-temporally explicit quantification of vegetation properties. Typically retrieval must be accurate, robust and fast. Hence, there is a strict requirement to identify next-generation bio-geophysical variable retrieval algorithms which can be molded into an operational processing chain. This paper offers a review of state-of-the-art retrieval methods for quantitative terrestrial bio-geophysical variable extraction using optical remote sensing imagery. We can categorize these methods into (1) parametric regression, (2) non-parametric regression, (3) physically-based and (4) hybrid methods. Hybrid methods combine generic capabilities of physically-based methods with flexible and computationally efficient methods, typically non-parametric regression methods. A review of the theoretical basis of all these methods is given first and followed by published applications. This paper focusses on: (1) retrievability of bio-geophysical variables, (2) ability to generate multiple outputs, (3) possibilities for model transparency description, (4) mapping speed, and (5) possibilities for uncertainty retrieval. Finally, the prospects of implementing these methods into future processing chains for operational retrieval of vegetation properties are presented and discussed.
471 citations
Cites background from "GEOV1: LAI and FAPAR essential clim..."
...To mitigate some of the above-identified limitations, Baret et al. (2013) recently presented the global GEOV1 products, available from 1999 to present, at a 1/112 spatial grid size, and a decadal time step....
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TL;DR: In this article, the authors provide a comprehensive analysis of field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies.
Abstract: Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.
331 citations
Cites background or methods from "GEOV1: LAI and FAPAR essential clim..."
...Most LAI estimation algorithms provide dispersion measures as outputs of the theoretical uncertainties (e.g., MODIS, CYCLOPES, GEOV1, JRC‐TIP, and GA‐TIP; Table 4)....
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...Figure 2 shows an example of the global mean LAI, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geoland2/BiopPar version 1 (GEOV1) from 2003 to 2013 in January and July, respectively....
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...The GEOV1 uncertainty information is derived from the NN training database and reflects the sensitivity of the product to the input reflectance values (F. Baret et al., 2013)....
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...Furthermore, the areal coverage of the moderate‐resolution LAI products is not constant over an aggregation period (e.g., 10 days for GEOV1), and pixel geolocation varies....
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...Classical inversion methods include the numerical optimization technique (Houborg & Boegh, 2008; Lewis et al., 2012), the NN approach (Baret et al., 2013; Fang & Liang, 2003), and the LUT approach (D. Huang et al., 2008; Verrelst et al., 2014)....
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TL;DR: A method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MOD!S) reflectance data.
Abstract: Leaf area index (LAI) is an important vegetation biophysical variable and has been widely used for crop growth monitoring and yield estimation, land-surface process simulation, and global change studies. Several LAI products currently exist, but most have limited temporal coverage. A long-term high-quality global LAI product is required for greatly expanded application of LAI data. In this paper, a method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. The GLASS LAI product has a temporal resolution of eight days and spans from 1981 to 2014. During 1981–1999, the LAI product was generated from AVHRR reflectance data and was provided in a geographic latitude/longitude projection at a spatial resolution of 0.05°. During 2000–2014, the LAI product was derived from MODIS surface-reflectance data and was provided in a sinusoidal projection at a spatial resolution of 1 km. The GLASS LAI values derived from MODIS and AVHRR reflectance data form a consistent data set at a spatial resolution of 0.05°. Comparison of the GLASS LAI product with the MODIS LAI product (MOD15) and the first version of the Geoland2 (GEOV1) LAI product indicates that the global consistency of these LAI products is generally good. However, relatively large discrepancies among these LAI products were observed in tropical forest regions, where the GEOV1 LAI values were clearly lower than the GLASS and MOD15 LAI values, particularly in January. A quantitative comparison of temporal profiles shows that the temporal smoothness of the GLASS LAI product is superior to that of the GEOV1 and MODIS LAI products. Direct validation with the mean values of high-resolution LAI maps demonstrates that the GLASS LAI values were closer to the mean values of the high-resolution LAI maps ( $\text{RMSE}=0.7848$ and $R^{2}=0.8095$ ) than the GEOV1 LAI values ( $\text{RMSE}=0.9084$ and $R^{2}=0.7939$ ) and the MOD15 LAI values ( $\text{RMSE}=1.1173$ and $R^{2}=0.6705$ ).
270 citations
Cites background or methods from "GEOV1: LAI and FAPAR essential clim..."
...parison of Products (BELMANIP) network of sites [24]....
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...The GEOV1 LAI retrieval algorithm relies on backpropagation neural networks trained with the “best estimates” of LAI obtained by fusing and scaling the MOD15 and CYCLOPES LAI products and the SPOT/VEGETATION nadir surface reflectance values over the BELMANIP sites [24]....
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Zoological Society of London1, German Aerospace Center2, University of Würzburg3, University of Twente4, Wageningen University and Research Centre5, University of Dundee6, Higher University of San Andrés7, University of New South Wales8, University of Zurich9, United Nations Environment Programme10, European Academy of Bozen11, Universities Space Research Association12, Murray State University13, Azim Premji University14, Goddard Space Flight Center15, University of Maryland, College Park16, European Space Agency17, Stellenbosch University18, Carnegie Institution for Science19, University of Ottawa20, African Institute of Science and Technology21, Pennsylvania State University22, Helmholtz Centre for Environmental Research - UFZ23, Edmund Mach Foundation24, Office of Environment and Heritage25, Australian Museum26, Spanish National Research Council27, Council for Scientific and Industrial Research28, University of Granada29, Monash University, Clayton campus30, NASA Headquarters31, Minnesota Department of Natural Resources32, Museum für Naturkunde33, Finnish Environment Institute34, Stockholm Resilience Centre35
TL;DR: This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space.
Abstract: Although satellite-based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations – Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS-EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS-EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS-EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long-term coordinated actions.
249 citations
Additional excerpts
...(2015); Baret et al. (2013); Pettorelli (2013); Racault...
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References
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TL;DR: In this paper, a simple radiative transfer model with vegetation, soil, and atmospheric components is used to illustrate how the normalized difference vegetation index (NDVI), leaf area index (LAI), and fractional vegetation cover are dependent.
2,429 citations
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...…FAPAR products showed that the maximum values (at 99% percentile) are around 243 0.90 (Figure 4b, dashed black line) although the maximum values are expected to be close to 244 0.94 (Baret and Guyot 1991) corresponding to full cover dense vegetation with albedo in the 245 PAR domain close to 0.06....
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TL;DR: In this article, the projection coefficients of several objects including spheres, cylinders, hemicircular cylinders, and triangular and square bars are investigated through mathematical derivation and numerical calculation for a range of ellipsoidal angular distributions.
Abstract: To eliminate the confusion in the definition of leaf area index (L) for non-flat leaves, the projection coefficients of several objects including spheres, cylinders, hemicircular cylinders, and triangular and square bars are investigated through mathematical derivation and numerical calculation for a range of ellipsoidal angular distributions. It is shown that the projection coefficient calculated based on half the total intercepting area is close to a constant of 0.5 when the inclination angle of the objects is randomly (spherically) distributed, whereas the calculated results based on the object's largest projected area are strongly dependent on the shape of the objects. Therefore, it is suggested that the leaf area index of non-flat leaves be defined as half the total intercepting area per unit ground surface area and that the definition of L based on the projected leaf area be abandoned.
1,171 citations
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...LAI is defined as half the total 35 developed area of green elements per unit horizontal ground area (Chen and Black 1992)....
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TL;DR: In this article, a surface bidirectional reflectance model was developed for the correction of surface bias in time series of satellite observations, where both sun and viewing angles are varying.
Abstract: A surface bidirectional reflectance model has been developed for the correction of surface bidirectional effects in time series of satellite observations, where both sun and viewing angles are varying. The model follows a semiempirical approach and is designed to be applicable to heterogeneous surfaces. It contains only three adjustable parameters describing the surface and can potentially be included in an algorithm of processing and correction of a time series of remote sensing data. The model considers that the observed surface bidirectional reflectance is the sum of two main processes operating at a local scale: (1) a diffuse reflection component taking into account the geometrical structure of opaque reflectors on the surface, and shadowing effects, and (2) a volume scattering contribution by a collection of dispersed facets which simulates the volume scattering properties of canopies and bare soils. Detailed comparisons between the model and in situ observations show satisfactory agreement for most investigated surface types in the visible and near-infrared spectral bands. The model appears therefore as a good candidate to reduce substantially the undesirable fluctuations related to surface bidirectional effects in remotely sensed multitemporal data sets.
1,134 citations
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...The preprocessing steps include cloud screening, atmospheric correction based on a climatology of aerosols, and BRDF (Bi– directional Reflectance Distribution Function) normalization using a robust fit of Roujean's model (Hagolle et al., 2004; Roujean et al., 1992)....
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TL;DR: In this article, a parameterization of the subgrid-scale forcing of heterogeneous land surfaces for atmospheric numerical models is suggested, where similar homogeneous land patches located at different places within the element are regrouped into subgrid classes.
Abstract: Natural land surfaces are usually heterogeneous over the resolvable scales considered in atmospheric numerical models. Therefore, model surface parameterizations that assume surface homogeneity may fail to represent the surface forcing accurately. In this paper, a parameterization of the subgrid-scale forcing of heterogeneous land surfaces for atmospheric numerical models is suggested. In each surface grid element of the numerical model similar homogeneous land patches located at different places within the element are regrouped into subgrid classes. Then, for each one of the subgrid classes, a sophisticated micrometeorological model of the soil-plant-atmosphere system is applied to assess the surface temperature, humidity, and fluxes to the atmosphere. The global fluxes of energy between the grid and the atmosphere are obtained by averaging according to the distribution of the subgrid classes. In addition to the surface forcing, detailed micrometeorological conditions of the patches are assessed...
769 citations
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...…r m an u sc ri p t users for vegetation monitoring (Lacaze et al. 2009) as well as for partitioning contributions 41 between soil an vegetation within specific models for Numerical Weather Prediction, regional 42 and global climate modeling, and global change monitoring (Avissar and Pielke 1989)....
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...between soil and vegetation within specific models for numerical weather prediction, regional and global climate modeling, and global change monitoring (Avissar and Pielke, 1989)....
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