Top PDF Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data

Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data

Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data

Sicre, J.-F. Dejoux, M. Huc, P. Keravec, P. B´ eziat, et al. To cite this version: M. Claverie, V. Demarez, B. Duchemin, Olivier Hagolle, D. Ducrot, et al.. Maize and sunflower biomass estimation in southwest France using high spatial and temporal res- olution remote sensing data. Remote Sensing of Environment, Elsevier, 2012, pp.1-14. <10.1016/j.rse.2012.04.005>. <ird-00718813>

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Estimation of biophysical variables and cartography of irrigated surfaces with high temporal and spatial resolution images

Estimation of biophysical variables and cartography of irrigated surfaces with high temporal and spatial resolution images

• Correct results with BVNET for biophysical variables estimation with remote sensing data -> good temporal profile for use as input in models. • Good estimation of biomass, and pers[r]

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Agro-hydrology and multi-temporal high-resolution remote sensing: toward an explicit spatial processes calibration

Agro-hydrology and multi-temporal high-resolution remote sensing: toward an explicit spatial processes calibration

1.6.4 Measurements of vegetation dynamics Destructive measurements of vegetation dynamics were car- ried out on the experimental plot during each crop season of the study period. They consisted of estimating LAI and green area index (GAI) from aerial biomass measurements at the main development stages (Béziat et al., 2009). 10 and 30 plants were collected on two diagonals across the fields for wheat and sunflower, respectively. Sampling fre- quency was adapted to the vegetation development, from 1 month during the slow vegetation development period to 2 weeks during the fast development period. LAI and GAI were measured by means of a LI-COR planimeter (LI3100, LI-COR, Nebraska, USA). Between each destruc- tive measurement date, several randomly distributed hemi- spherical photographs were taken to capture the leaf devel- opment dynamics. The camera used for these measurements, a Nikon COOLPIX 8400 equipped with an FC-E8 fisheye lens, was placed on top of a pole to keep the viewing di- rection (downward-looking) and canopy-to-sensor distance (1.5 m) constant throughout the growing season. The hemi- spherical photographs were processed using CAN-EYE V5 (http://www4.paca.inra.fr/can-eye), which provides an effec- tive GAI (Baret et al., 2010; Demarez et al., 2008) for the whole image. These data were used to assess the model’s ac- curacy in reproducing the biomass production and LAI dy- namics of the crops. A field crop comparable to the experi- mental plot in terms of situation and cropping pattern was se- lected within the catchment: hereafter it is called crop field 8 (Fig. 1).
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Agro hydrology and multi temporal high resolution remote sensing: toward an explicit spatial processes calibration

Agro hydrology and multi temporal high resolution remote sensing: toward an explicit spatial processes calibration

Destructive measurements of vegetation dynamics were car- ried out on the experimental plot during each crop season of the study period. They consisted of estimating LAI and green area index (GAI) from aerial biomass measurements at the main development stages (Béziat et al., 2009). 10 and 30 plants were collected on two diagonals across the fields for wheat and sunflower, respectively. Sampling fre- quency was adapted to the vegetation development, from 1 month during the slow vegetation development period to 2 weeks during the fast development period. LAI and GAI were measured by means of a LI-COR planimeter (LI3100, LI-COR, Nebraska, USA). Between each destruc- tive measurement date, several randomly distributed hemi- spherical photographs were taken to capture the leaf devel- opment dynamics. The camera used for these measurements, a Nikon COOLPIX 8400 equipped with an FC-E8 fisheye lens, was placed on top of a pole to keep the viewing di- rection (downward-looking) and canopy-to-sensor distance (1.5 m) constant throughout the growing season. The hemi- spherical photographs were processed using CAN-EYE V5 (http://www4.paca.inra.fr/can-eye), which provides an effec- tive GAI (Baret et al., 2010; Demarez et al., 2008) for the whole image. These data were used to assess the model’s ac- curacy in reproducing the biomass production and LAI dy- namics of the crops. A field crop comparable to the experi- mental plot in terms of situation and cropping pattern was se- lected within the catchment: hereafter it is called crop field 8 (Fig. 1).
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IMPACTS OF SENSOR SPATIAL RESOLUTION ON REMOTE SENSING IMAGE CLASSIFICATION

IMPACTS OF SENSOR SPATIAL RESOLUTION ON REMOTE SENSING IMAGE CLASSIFICATION

environment, the kind of information desired and the techniques used to extract information. Thus, selecting a specific spatial resolution from various spatial resolutions for particular purpose can lead to ambiguous image interpretation. Finally, it was revealed that, even though, higher spatial resolution remotely sensed data may deliver improved data, it may not always be desired due to intricate nature of interpretation, data volume and data acquisition costs. And despite the increasing propensity for more satellites with improved spatial characteristics and to develop applications for the same, the lower spatial resolution satellites remain invaluable. Selecting suitable spatial resolution(s) of remotely sensed data is one of the most essential considerations prior to remotely sensed data classification. When defining the appropriate spatial resolution for analysis, certain factors are very salient including spatial resolution of available data, environmental conditions, anticipated information, and procedures employed in extracting information. Proper image classification is to a great extent dependent on the knowledge of certain spatial attributes of the data so as to determine the appropriate classification procedure and parameters to use. The reason for this is because the spatial resolution of remotely sensed data largely affects each of the stages involve in image classification. With the increased availability of very high resolution multi-spectral images spatial resolution variation will play an increasingly important role in the employment of remotely sensed imagery. Though, higher spatial resolution remotely sensed data may deliver enhanced data, it may not always be desired due to intricate nature of interpretation, data volume and data acquisition costs. And despite the increasing propensity for more satellites with improved spatial characteristics and to develop applications for the same, the lower spatial resolution satellites remain invaluable
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Ground Data are Essential for Biomass Remote Sensing Missions

Ground Data are Essential for Biomass Remote Sensing Missions

estimates. Also, the forest lower canopy may play a significant role in the radar backscattering properties, and, like wood density, this is not readily assessed remotely. We therefore provide ecologically informed guiding principles for the selection of sites. Tropical forests vary in their structure and floristic composition, and this in turn impacts their biomass storage capacity (Malhi et al. 2004; Stegen et al. 2011). The four main driving factors of this variation are soil fertility, moisture supply, elevation, and disturbance regime. Therefore, forests often hold less biomass on very infertile or very fertile soils (Castilho et al. 2006). Also, dry tropical forests have less biomass, but there is also potentially a hump-shaped distribution of biomass with respect to annual precipitation whereby ever-wet forests tend to have lower biomass stocks than moist forests (Brown and Lugo 1982). Elevation is another important factor, and biomass usually declines with increasing altitude, although some exceptions exist, for example when trees of the oak family are present (Phillips et al. 2016). Finally, disturbed forests have a lower biomass than undisturbed ones. The foremost cause of disturbance in the tropics is anthropic, but other causes exist including wildfires, wind storms, insect predation, or
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Monitoring spatial and temporal variation of ocean productivity in the Bay of Bengal using remote sensing

Monitoring spatial and temporal variation of ocean productivity in the Bay of Bengal using remote sensing

The area of study, Bay of Bengal (Figure 1.1) (Lat. 13° 31' 54.2634" N and Lon. 87° 32' 22.4982"E), is located at the northeastern part of the Indian Ocean, bordered by India and Sri Lanka to the west, Bangladesh to the north, and Myanmar to the east. The bay occupies an area of about 2.2 million sq km with the average depth of 2600 m. It is known as the largest bay in the world with a unique semi- enclosed tropical basin and is characterized as a region with tropical climate, high rainfall, and monsoon variation. The seasonal reversing monsoon with large amount of water discharged from Brahmaputra and Irrawaddy rivers contribute to the unstable trend of ocean productivity/Chl-a concentration and makes Bay of Bengal an unique place to study.
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Improved Modeling of Evapotranspiration using Satellite Remote Sensing at Varying Spatial and Temporal Scales

Improved Modeling of Evapotranspiration using Satellite Remote Sensing at Varying Spatial and Temporal Scales

conditions on the latent heat flux (Batra et al., 2006; Nishida et al., 2003). DANR, to a large extent, determines the magnitude of estimates of ET for a given evaporative fraction from a pixel standpoint. It is thus believed that a considerable effort should be made to substantially improve the accuracy of both evaporative fraction and DANR, with the objective to make reliable predictions of ET. On the other hand, although comparisons of satellite-based latent heat flux with point-scale ground observations or more regionally with aircraft for several retrievals have been performed, it seems that to date there have not been universally acceptable approaches to effectively assessing the accuracy of the extrapolated daily ET from evaporative fraction. Discretization of ground-based measurements, the number of measurements, the difference between satellite-based pixel scales (e.g., 1000 m) and measurement scales (e.g., 100 m), and errors associated with such measurements should be taken into account (Bisht et al., 2005; Kempf and Tyler, 2006; McCabe and Wood, 2006). It appears that although evaluation of the accuracy of spatially distributed estimates of ET from remote sensing-based energy balance models cannot be readily performed, improvements in the daily estimates of ET could be potentially achieved by increasing the number and/or frequency of ground-based observations of each component of DANR or significantly enhancing the capability of the parameterization scheme of DANR to represent reality.
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Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data

Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data

For flood loss estimation the following important damage influencing parameters were identified (see Subsection 2.5) and are thus required as input data on an area wide basis: water depth, contamination, and building characteristics (floor space, building quality, age of building, heating system, precaution measures). This study uses the maximum flood extent and water depth of the Elbe flood in 2002 that is provided by the Landeshauptstadt Dresden as a raster dataset [47]. Data regarding the precaution measures and the flood damage influencing factor contamination was taken from [48]. Information about the building types and their size, i.e., floor space, is generated in this study by the urban structure mapping approach. Various building characteristics (e.g., age, heating system) were obtained from telephone interviews that have been conducted after the flood events in 2002, 2005, and 2006 [45,46]. A total of about 2700 telephone interviews were carried out, of which 800 originate from Dresden. Detailed descriptions of the questionnaire can be found in [49]. Information on building qualities are provided by INFAS GEOdaten [50]. Originally, this data set consists of six classes from very simple to an exclusive quality. Based on this data, Thieken et al. [51] calculated the mean building quality for each ATKIS building block, divided it into two quality classes and converted the vector data in raster format with a spatial resolution of 10 m. Since relative damage models are used, building values are also necessary to estimate flood losses in monetary terms. Asset values of residential buildings for all municipalities of Germany for the reference year 2000 are available from [52]. Its spatial localization within Dresden was implemented by Kreibich et al. [53] using a binary distribution based on ATKIS building blocks and INFAS GEOdaten. The generated raster data with a resolution of 10 m contains the building values that are given in Euro per square meter. In order to adjust these values to the reference year 2002 a construction price index of 0.999 was used [54]. 2.4. Classification of Elements at Risk: Urban Structure Type Classification
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Multi sensor, multi temporal, and ultra high resolution environmental remote sensing from UAVs

Multi sensor, multi temporal, and ultra high resolution environmental remote sensing from UAVs

Unlike the TIR and the visible imagery it was not possible to process the multispectral imagery with Photoscan. Although multi-band 16-bit images could be imported into Photoscan, it was not feasible to reliably align the images of different spectral bands, no matter what parameters were selected. The CMOS sensors in the mini-MCA have a rolling shutter, which built up each image as a scan from top to bottom rather than a whole-frame snapshot as in the case of a global shutter. Given the movements of the sensor during image acquisition the rolling shutter leads to geometric distortions in each image, which were unpredictable. The SfM algorithm in Photoscan expects images to be acquired by a global shutter. Thus, the distorted image geometry of the mini-MCA leads to very poor and false image matching results. Laliberte et al. (2010) developed a method for rectifying, georeferencing, and mosaicking UAV visible imagery by matching the individual images with a pre- existing orthophoto of the study area using image correlation techniques. This method served as the basis for our approach, however, we have implemented the SIFT algorithm as we did with the mini-MCA band alignment workflow (see Section 4.2.5). Instead of using a low-resolution orthophoto we matched each mini-MCA frame to the ultra-high spatial resolution visible orthophotomap (Section 4.2.6). This allowed SIFT to select thousands of features per image to be used as control points, providing a denser transformation matrix.
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Spatio-temporal modeling of Louisiana land subsidence using high resolution geo-spatial data

Spatio-temporal modeling of Louisiana land subsidence using high resolution geo-spatial data

The second conclusion is that Kriged Kalman Filter (KKF) is suitable to process GPS data and predict the subsidence rates pattern each year in the study site, in this research. KKF can overcome the flaw of low density for GPS stations and predict large areas of subsidence rates around all of GPS stations in the study site (Mardia et al. 1998; Lu, C. et al. 2012). The KKF results show a significant accelerating subsidence area near Bayou Corne, and these results have been validated by Bayou Corne Sinkhole knowledge (Cusanza 2013; Jones and Blom 2014; Jones and Blom 2015). Besides, KKF as an extended type of Kriging interpolation, has been used to interpolate and generate a raster of subsidence rates distribution in this study, thus, as the KKF results from Figure 13, 14 and 15 show, the distance of less than 10 miles between the dark colored accelerating subsidence area and the exact Bayou Corne location (the purple point), should be allowed because of the interpolation error by KKF (Mardia et al. 1998; Cusanza 2013; Olea and Ricardo 1991; Olea 1999).
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Noise estimation in remote sensing imagery using data masking

Noise estimation in remote sensing imagery using data masking

A technique has been developed in which Laplacian and gradient data masks are used to estimate the additive and multiplicative noise standard deviations in an image. The estimation was shown to perform best for low to moderate levels of both additive and multiplicative noise applied to the imagery. Increasing the window size to calculate the local standard deviations for the histogram tends to improve the estimates for higher amounts of noise. Comparisons of the estimation method with currently existing noise-estimation techniques show favourable results. Further quan- tification of the relationship between the window size of the local standard deviation used in the histogram analysis and the initial image variance on the resulting noise estimates are being explored.
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Operational Flexibility of Future Generation Portfolios Using High Spatial- and Temporal-Resolution Wind Data

Operational Flexibility of Future Generation Portfolios Using High Spatial- and Temporal-Resolution Wind Data

Abstract—Increasing amounts of variable renewable energy sources will cause fundamental and structural changes to ther- mal power plant operating regimes. Maintaining key reserve requirements will lead to an increase in power plant start-ups and cycling operations for some units. An enhanced unit com- mitment model with energy storage and flexible CO 2 capture is formulated. High-resolution on-/offshore wind data for the U.K., and probabilistic wind power forecast, model wind imbalances at operational timescales. The strategic use of flexible CO 2 cap- ture and energy storage helps maintain reserve levels, decreasing power plant cycling operations and wind curtailment. A tempo- rally explicit variability assessment of net demand illustrates the generation flexibility requirements and the nonlinear impacts of increasing wind capacity on power plant operating regimes.
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Spatio-temporal Modeling of Louisiana Land Subsidence Using High-resolution Geo-spatial Data

Spatio-temporal Modeling of Louisiana Land Subsidence Using High-resolution Geo-spatial Data

Hyun-Joo Oh’s researchers from Korea initiated a series of studies on mining subsidence by collecting relevant contributing factors and using many classic models from general statistics (Kim et al. 2006; Kim et al. 2009; Oh and Lee 2010; Oh et al. 2011). The case study sites, located in abandoned coal mines, modeled a) frequency ratios, b) logistic regression, c) weights of evidence, and d) artificial neural networks; these were tested successively. The testing sought possible relationships between subsidence and contributing factors by calculating factor ratings/weights to map subsidence hazards; this was accomplished by means of overlaying the ratings or weights. The results for most of the tested models showed predicted accuracies of over 90% (Kim et al. 2006; Kim et al. 2009; Oh and Lee 2010; Oh et al. 2011; Freedman 2009). This series of studies on mining subsidence evidenced a maximum progress, yet also revealed evident drawbacks. The tested models involved the global approach on subsidence prediction, as the spatial heterogeneity of factors was not yet considered (Shang et al 2011; Kim et al 2006; Kim et al 2009; Oh and Lee 2010; Oh et al 2011). Moreover, the dependent variable of subsidence was regarded initially as a dichotomous or categorical one (presence/absence). In fact, subsidence is a numerical variable; therefore, the modeling process by a dichotomous variable as a subsidence may tend to cause a coarser prediction with much less detailed information (Kim et al. 2006; Kim et al. 2009; Oh and Lee 2010; Oh et al. 2011; Freedman 2009).
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SILVA FENNICA. Detection of the need for seedling stand tending using high-resolution remote sensing data

SILVA FENNICA. Detection of the need for seedling stand tending using high-resolution remote sensing data

Seedling stands are problematic in airborne laser scanning (ALS) based stand level forest manage- ment inventories, as the stem density and species proportions are difficult to estimate accurately using only remotely sensed data. Thus the seedling stands must still be checked in the field, which results in an increase in costs. In this study we tested an approach where ALS data and aerial images are used to directly classify the seedling stands into two categories: those that involve tending within the next five years and those which involve no tending. Standard ALS-based height and density features, together with texture and spectral features calculated from aerial images, were used as inputs to two classifiers: logistic regression and the support vector machine (SVM). The classifiers were trained using 208 seedling plots whose tending need was estimated by a local forestry expert. The classification was validated on 68 separate seedling stands. In the training data, the logistic model’s kappa coefficient was 0.55 and overall accuracy (OA) 77%. The SVM did slightly better with a kappa = 0.71 and an OA = 86%. In the stand level validation data, the performance decreased for both the logistic model (kappa = 0.38, OA = 71%) and the SVM (kappa = 0.37, OA = 72%). Thus our approach cannot totally replace the field checks. However, in considering the stands where the logistic model predictions had high reliability, the number of misclassifications reduced drastically. The SVM however, was not as good at recognizing reliable cases.
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Assessing carbon in urban trees: benefits of using high-resolution remote sensing

Assessing carbon in urban trees: benefits of using high-resolution remote sensing

classifications benefit from additional spectral information of an 8-band WorldView 2 springtime image compared to a 4-band IKONOS springtime image. The maximum overall accuracy of 63 percent was nevertheless too low to be useful. Immitzer et al. (2012) found similar classification results for European tree species using a summertime WorldView 2 image, but the benefits of additional spectral information were highly species-dependent. High resolution airborne data most commonly uses spectral or structural information for tree species classification, whereas studies of traditional 4-band high resolution imagery was found too low for tree species classification and was affected by high variations (Zhang and Hu 2012). In contrast, very high spectral information of recent hyperspectral airborne data produced high accuracies for tree species classification in forests of southeast Britain, as well as urban settings of Tampa, USA (Pu and Liu 2011; Ghiyamat et al. 2013). Airborne LiDAR data has become an important source for the 3-dimensional analysis of trees with a focus on carbon estimates (Omasa et al. 2007; Miraliakbari et al. 2010; Shrestha and Wynne 2012). Li et al. (2013) indicate a positive linear correlation between point density and classification accuracy. High point density discrete LiDAR systems and full-waveform LiDAR particularly offer extensive opportunities for tree species classification (Hyyppä et al. 2008; Korpela et al. 2010; Pirotti 2011; Vaughn et al. 2012). Primary properties of full- waveform systems are derived from the complete returned signal; they offer additional points for a three-dimensional analysis of species-related differences between leaves and branches (Wagner et al. 2008). Heinzel and Koch (2011) successfully extended the use of secondary LiDAR properties for European tree species classification using width, amplitude, intensity and the total number of targets within one laser beam.
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Detection and Removal of Shadow Using Very High Resolution Remote Sensing Images

Detection and Removal of Shadow Using Very High Resolution Remote Sensing Images

This paper deal with the important and challenging problem of detection and reconstruction of shadow in VHR Remote Sensing image. In this project we examined first the basics of the shadow, how shadow occurs, then different types of shadows are also mentioned which can appear in the images. Detection and Removal of Shadow Regions may be useful for the geosciences department. Detecting of shadows in an image and reconstructing the shadow area gives the original information to some extent. As the performance of proposed method is analyzed by calculating PSNR and MSE values, in future work, we will try to get 100% reconstructed image. Also by using different methods of reconstruction of shadow, we will try to get better original information.
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Aircraft Identification in High Resolution Remote Sensing Images using Shape Analysis

Aircraft Identification in High Resolution Remote Sensing Images using Shape Analysis

A BSTRACT: Automatic aircraft recognition in a complex environment has long been an interesting and challenging task. Conventional aircraft recognition methods always extract the overall shape features of aircraft for recognition, which is too idealistic for targets in remote sensing images. In this letter, an aircraft recognition system is proposed that provides the best way to recognize the aircraft robustly without perfect extractions of shape as a precondition. This recognition system involves dimensionality reduction, segmentation and aircraft identification with templates. Specifically, a principal component analysis is proposed to reduce the dimensionality of the satellite image. Then, histogram probability thresholding is used to detect the desired object from background. Connected component analysis is used here to extract the local object shape descriptors for identifying desired target. Template is used as a matching model. Finally, correlation measurement is used for measuring similarity between two object region features and simulation demonstrated that the capability of object tracking in remote sensing images with help of used approaches. KEY WORDS: Dimensionality reduction, Segmentation, Aircraft identification.
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Monitoring Aquatic Habitat Restoration using High-Resolution Multispectral Remote Sensing

Monitoring Aquatic Habitat Restoration using High-Resolution Multispectral Remote Sensing

This study focuses on the Middle Fork of the John Day River (MFJD) in northeastern Oregon (Figure 1). The MFJD is one of three major headwaters of the John Day River, which is itself the longest undammed tributary of the Columbia River. The MFJD provides critical spawning habitat for wild native runs of spring Chinook and summer steelhead salmon. This river drains 2088 km^ 2 of semi-arid mountainous terrain receiving between 25cm and 100cm of precipitation annually, with a range of elevation of 2500m at its headwaters to 670m at its confluence with the North Fork John Day. The MFJD additionally has a long history of human impacts, the most dramatic of which is the floodplain dredge mining that occurred during the late 19 th through mid-20 th century (Middle Fork IMW, 2017, Magilligan & McDowell, 1998). Grazing pressure, and manual clearing additionally stripped much of the floodplain of its woody vegetation, increasing the solar loading on the channel and intensifying high summer stream temperatures that threaten cold water dependent salmon. In dredged reaches, the mainstem MFJD became incised and disconnected from its floodplain. Additionally, the coarse cobble-sized substrates left by dredging prevented already degraded reaches from meandering naturally – a pattern seen across many streams in the American West (Kondolf, 1997).
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Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

This study deals with the inter-comparison between two spa- tialized hydrometeorological ISBA modeling approaches in an agricultural zone in southwestern France from year 2006 to 2010. A first experiment was performed with LAI forc- ing from ECOCLIMAP-II database which was generated from MODIS data. It was considered as the reference sim- ulation. The second experiment included LAI forcing from high-resolution Formosat-2 (Sentinel-2 like) time series data. Both simulations were performed with plots as computing units, where plot segmentation was derived from Formosat-2 high-resolution land cover maps classifications. The use of the plot-scale approach allowed for exploiting the high spa- tial resolution on coherent hydrometeorological units while limiting the calculation time compared to a pixel-based ap- proach. Thanks to the high revisit frequency of Formosat-2, the complex anthropogenic effects which affect land surface properties (e.g., seeding and harvest dates, crop rotations) can be captured. The comparison between the two experi- ments reveals significant differences in the simulated water fluxes. The results shows that summer crop LAI dynamics appear more realistic when using Formosat-2 data. Conse- quently, the modeled evapotranspiration also appears more realistic on this kind of crop. These results point out the lim- itations of both the LAI retrieval method of ECOCLIMAP-II and the lack of interannual variability of the vegetation in the model. As expected, however, the incorporation of satellite LAI was not sufficient to capture the amplitude of the evap- otranspiration peak in the validation site where irrigation is practiced. Indeed, there is no parameterization for irrigation practices in our model while the irrigated area is known to be 13 % of plots in the study area. Hence, the focus will now be put on the representation of the irrigation in the model. This will allow further evaluation of the model at the catchment scale based on the observed river discharge.
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