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See detailMonitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions
Benabdelouahab, Tarik ULg; Balaghi, Riad; Hadria, Rachid et al

in International Journal of Remote Sensing (2015), 36(15), 4018-4036

Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can ... [more ▼]

Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can contribute to their optimal management. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 high-resolution visible (HRV) data, to retrieve the surface water content values (from bare soil to completely covered soil) over wheat fields and detect irrigation supplies in an irrigated area. These indices are the normalized difference water index (NDWI) and the moisture stress index (MSI), covering the main growth stages of wheat. These indices were compared to corresponding in situ measurements of soil moisture and vegetation water content in 30 wheat fields in an irrigated area of Morocco, during the 2012–2013 and 2013–2014 cropping seasons. NDWI and MSI were highly correlated with in situ measurements at both the beginning of the growing season (sowing) and at full vegetation cover (grain filling). From sowing to grain filling, the best correlation (R2 = 0.86; p < 0.01) was found for the relationship between NDWI values and observed soil moisture values. These results were validated using a k-fold cross-validation methodology; they indicated that NDWI can be used to estimate and map surface water content changes at the main crop growth stages (from sowing to grain filling). NDWI is an operative index for monitoring irrigation, such as detecting irrigation supplies and mitigating wheat water stress at field and regional levels in semi-arid areas. [less ▲]

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See detailFodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series
Diouf, Abdoul Aziz; Brandt, Martin; Verger, Aleixandre et al

in Remote sensing (2015), 7(9122-9148),

Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship ... [more ▼]

Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship between satellite annual Normalized Difference Vegetation Index (NDVI) and in situ biomass data. This study proposes a new methodology using multi-linear models between phenological metrics from the SPOT-VEGETATION time series of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and in situ biomass. A model with three variables—large seasonal integral (LINTG), length of growing season, and end of season decreasing rate—performed best (MAE = 605 kg·DM/ha; R2 = 0.68) across Sahelian ecosystems in Senegal (data for the period 1999–2013). A model with annual maximum (PEAK) and start date of season showed similar performances (MAE = 625 kg·DM/ha; R2 = 0.64), allowing a timely estimation of forage availability. The subdivision of the study area in ecoregions increased overall accuracy (MAE = 489.21 kg·DM/ha; R2 = 0.77), indicating that a relation between metrics and ecosystem properties exists. LINTG was the main explanatory variable for woody rangelands with high leaf biomass, whereas for areas dominated by herbaceous vegetation, it was the PEAK metric. The proposed approach outperformed the established biomass NDVI-based product (MAE = 818 kg·DM/ha and R2 = 0.51) and should improve the operational monitoring of forage resources in Sahelian rangelands. [less ▲]

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See detailThe iPot Project: improved potato monitoring in Belgium using remote sensing and crop growth modelling
Piccard, I.; Nackaerts, K.; Gobin, A. et al

Poster (2015, April)

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See detailModelling carbon fluxes of forest and grassland ecosystems in Western Europe using the CARAIB dynamic vegetation model: evaluation against eddy covariance data.
Henrot, Alexandra-Jane ULg; François, Louis ULg; Dury, Marie ULg et al

in Geophysical Research Abstracts (2015, April), 17

Eddy covariance measurements are an essential resource to understand how ecosystem carbon fluxes react in response to climate change, and to help to evaluate and validate the performance of land surface ... [more ▼]

Eddy covariance measurements are an essential resource to understand how ecosystem carbon fluxes react in response to climate change, and to help to evaluate and validate the performance of land surface and vegetation models at regional and global scale. In the framework of the MASC project (« Modelling and Assessing Surface Change impacts on Belgian and Western European climate »), vegetation dynamics and carbon fluxes of forest and grassland ecosystems simulated by the CARAIB dynamic vegetation model (Dury et al., iForest - Biogeosciences and Forestry, 4:82-99, 2011) are evaluated and validated by comparison of the model predictions with eddy covariance data. Here carbon fluxes (e.g. net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RECO)) and evapotranspiration (ET) simulated with the CARAIB model are compared with the fluxes measured at several eddy covariance flux tower sites in Belgium and Western Europe, chosen from the FLUXNET global network (http://fluxnet.ornl.gov/). CARAIB is forced either with surface atmospheric variables derived from the global CRU climatology, or with in situ meteorological data. Several tree (e.g. Pinus sylvestris, Fagus sylvatica, Picea abies) and grass species (e.g. Poaceae, Asteraceae) are simulated, depending on the species encountered on the studied sites. The aim of our work is to assess the model ability to reproduce the daily, seasonal and interannual variablility of carbon fluxes and the carbon dynamics of forest and grassland ecosystems in Belgium and Western Europe. [less ▲]

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See detailunité: Eau-Environnement-Développement (ULg Campus Arlon): la télédétection au service de l'agriculture
Wellens, Joost ULg; Lang, Marie ULg; Benabdelouahab, Tarik et al

Diverse speeche and writing (2015)

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See detailLIVESTOCK SYSTEMS--TECHNICAL REPORT
Minet, Julien ULg; Diouf, Abdoul Aziz; Garba, Issa et al

Report (2015)

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See detailRemote sensing enables high discrimination between organic and non-organic cotton for organic cotton certification in West Africa
Denis, Antoine ULg; Tychon, Bernard ULg

in Agronomy for Sustainable Development (2015)

One of the challenges of organic crop certification is the efficient targeting of the relatively small percentage of risk-sensitive fields that have to be controlled during the regulatory annual in situ ... [more ▼]

One of the challenges of organic crop certification is the efficient targeting of the relatively small percentage of risk-sensitive fields that have to be controlled during the regulatory annual in situ inspection. A previous study carried out on wheat and maize in Germany has shown that organic and non-organic crops can be efficiently distinguished by remote sensing. That study pointed to the possibility that these techniques could be used for helping organic crop certification bodies to better target risk-sensitive fields. This study is a first adaptation of that research on organic cotton in southwestern Burkina Faso, West Africa. This study assumed that organic and non-organic cotton, primarily because of their different approaches to fertilization and pest control, would result in bio-chemico-physical differences measurable by both in situ and remote sensing indicators. This study included 100 cotton fields, of which 50 were organic, 28 conventional, and 22 genetically modified. In situ indicators were derived from chlorophyll content, canopy cover, height, and spatial heterogeneity measurements. Remote sensing spectral and spatial heterogeneity indicators were derived from two SPOT 5 satellite images. Discriminant models were then computed. The results show statistically highly significant differences between organic and non-organic cotton fields for both in situ and satellite indicators, using univariate and multivariate linear models, with up to 86 % discrimination performance. This is the first time that the efficiency of using remote sensing to discriminate between organic and non-organic crops is evaluated in a developing country, particularly for cotton, with good discrimination being achieved. Pending further validation, it therefore seems that remote sensing could be used to enhance organic cotton certification in West Africa by enabling more efficient targeting of suspect fields and consequently could contribute to a better development of this sector. [less ▲]

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See detailBayesian inversions of a dynamic vegetation model at four European grassland sites
Minet, Julien ULg; Laloy, Eric; Tychon, Bernard ULg et al

in Biogeosciences (2015), 12(9), 2809--2829

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters ... [more ▼]

Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters, using the DREAM(ZS) (DiffeRential Evolution Adaptive Metropolis) Markov chain Monte Carlo (MCMC) sampler. We focus on comparing model inversions, considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a priori or jointly inferred together with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root mean square errors (RMSEs) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19, 1.04 to 1.56 g C m−2 day−1 and 0.50 to 1.28 mm day−1, respectively. For the calibration period, using a homoscedastic eddy covariance residual error model resulted in a better agreement between measured and modelled data than using a heteroscedastic residual error model. However, a model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors perform better. Posterior parameter distributions derived from using a heteroscedastic model of the residuals thus appear to be more robust. This is the case even though the classical linear heteroscedastic error model assumed herein did not fully remove heteroscedasticity of the GPP residuals. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides the residual error treatment, differences between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics. [less ▲]

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See detailOutcomes from the MACSUR grassland model inter-comparison with the model CARAIB
Minet, Julien ULg; Laloy, Eric; Tychon, Bernard ULg et al

Conference (2014, October 15)

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See detailMapping of vegetation water content using Shortwave Infrared SPOT5 data to monitor irrigation in semi-arid regions.
Benabdelouahab, Tarik ULg; Balaghi, Riad; Lionboui, Hayat et al

Conference (2014, May 28)

Half of world’s food comes from irrigated area that uses about 72% of available water resources. In Morocco, water availability is the main limiting factor for crop growth and final yield and it is ... [more ▼]

Half of world’s food comes from irrigated area that uses about 72% of available water resources. In Morocco, water availability is the main limiting factor for crop growth and final yield and it is becoming a national priority for the agricultural sector. This situation leads the stakeholders to define most favorable strategies in planning and management of available water resources, on one hand, and to assess accurately vegetation water content status, on the other hand, in order to improve irrigation scheduling and prevent water stress adversely affecting yield. Remotely sensed reflectance has been used to estimate vegetation water content for different crops and to monitor water irrigation per surface unit, considering its high temporal and spatial resolution. In this study, we used two spectral indices of vegetation water content indicator (the Normalized Difference Infrared Index (NDII) and the Moisture Stress Index (MSI)) developed using Near Infrared (NIR) and Short Wave Infra-Red (SWIR) bands. The study area is the irrigated perimeter of Tadla in Morocco (35% dominated by irrigated wheat crop). In a first step, we compared observed vegetation water content of 16 studied plots of wheat and derived spectral indices NDII and MSI at the end of cropping season. The two images used at this step were acquired on March 26, 2013 and on April 11, 2013 when soil was fully covered by vegetation. Statistical analyses showed that the two spectral indices, NDII and MSI, simulated accurately vegetation water content. The statistical indicators, r, R², RMSE, nRMSE and MAE were -0.81, 0.65, 3.26% of water content (≈0.13 kg/m²), 4.26% and 2.69% for the NDII and 0.81, 0.65, 3.27% of water content (≈0.14 kg/m²), 4.27% and 2.72% for the MSI, respectively. To validate these results, we compared observed vegetation water content values and those predicted using the k-fold CV method. The errors were minimal for NDII and MSI, and the indicators of model evaluation obtained for predicted vegetation water content from NDII were: RMSE = 3.17%, nRMSE = 4.13%, MAE = 2.52% and R²=0.64. For MSI, these indicator were RMSE = 3.28%, nRMSE = 4.29%, MAE = 2.68% and R²=0.61. In a second step, we delimited the cereal area in the studied perimeter using a supervised classification method. The classification has been validated and the overall accuracy and Kappa coefficient were estimated respectively at 96.7% and 0.9545. Based on the regression model resulting from the comparison between NDII and measured vegetation water content, we produced maps of vegetation water content of wheat over the whole Beni-Moussa East irrigated area (41,000 hectares). The results of this work demonstrated the potential of spectral indices (NDII and MSI) derived from SPOT5 satellite images data to quantify and map vegetation water content of wheat. It showed also the potential of the SWIR band to improve the monitoring of irrigation by mapping water stress of wheat at field and regional level. [less ▲]

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See detailAquaCrop: Manuel d'utilisation
Raes, Dirk; Steduto, Paquale; Hsiao, Theodore et al

Learning material (2014)

Le logiciel AquaCrop est un modèle de bilan d’eau qui permet d’évaluer les efficiences en irrigation, l’élaboration des calendriers d’irrigation au niveau de la parcelle et l'estimation des rendements ... [more ▼]

Le logiciel AquaCrop est un modèle de bilan d’eau qui permet d’évaluer les efficiences en irrigation, l’élaboration des calendriers d’irrigation au niveau de la parcelle et l'estimation des rendements. AquaCrop a été sélectionné en raison de sa simplicité et de sa robustesse, et du nombre limité de variables à introduire. Il existe bien des modèles déterministes, mais pour fonctionner correctement, ils exigent des paramètres d’entrée très détaillés qui ne sont pas toujours disponibles quand il s’agit de recherche en milieu rural (‘on-farm’). [less ▲]

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See detailBrown rust disease control in winter wheat: II. Exploring the optimization of fungicide sprays through a decision support system
El Jarroudi, Moussa ULg; kOUADIO, Louis; Giraud, Frédéric et al

in Environmental Science and Pollution Research (2014), 21(4), 4809-4818

A decision support system (DSS) involving an approach for predicting wheat leaf rust (WLR) infection and progress based on night weather variables (i.e., air temperature, relative humidity, and rainfall ... [more ▼]

A decision support system (DSS) involving an approach for predicting wheat leaf rust (WLR) infection and progress based on night weather variables (i.e., air temperature, relative humidity, and rainfall) and a mechanistic model for leaf emergence and development simulation (i.e., PROCULTURE) was tested in order to schedule fungicide time spray for controlling leaf rust progress in wheat fields. Experiments including a single fungicide treatment based upon the DSS along with double and triple treatment were carried out over the 2007–2009 cropping seasons in four representative Luxembourgish wheat field locations. The study showed that the WLR occurrences and severities differed according to the site, cultivar, and year. We also found out that the single fungicide treatment based on the DSS allowed a good protection of the three upper leaves of susceptible cultivars in fields with predominant WLR occurrences. The harvested grain yield was not significantly different from that of the double and triple fungicide-treated plots (P < 0.05). Such results could serve as basis or be coupled to cost-effective and environmentally friendly crop management systems in operational context. [less ▲]

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See detailBayesian inference of a dynamic vegetation model for grassland
Minet, Julien ULg; Laloy, Eric; Tychon, Bernard ULg et al

Conference (2014, April 02)

As a part of the MACSUR task L2.4, we probabilistically calibrated the CARAIB dynamic vegetation model by Markov chain Monte Carlo (MCMC) simulation with the DREAMZS sampler.. CARAIB is a mechanistic ... [more ▼]

As a part of the MACSUR task L2.4, we probabilistically calibrated the CARAIB dynamic vegetation model by Markov chain Monte Carlo (MCMC) simulation with the DREAMZS sampler.. CARAIB is a mechanistic model that calculates the carbon assimilation of the vegetation as a function of the soil and climatic conditions, and can thus be used for simulating grassland production under cutting or grazing management. Bayesian model inversion was performed at 4 grassland sites across Europe: Oensingen, CH; Grillenburg, DE; Laqueuille, FR and Monte-Bodone, IT. Four daily measured variables from these sites: the Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), Evapotranspiration (ET) and Soil Water Content (SWC) were used to sample 10 parameters related to rooting depth, stomatal conductance, specific leaf area, carbon-nitrogen ratio and water stresses. The maximized likelihood function therefore involved four objectives, whereas the applied Bayesian framework allowed for assessing the so called parameter posterior probability density function (pdf), which quantifies model parameter uncertainty caused by measurement and model errors. Sampling trials were performed using merged data from all sites (all-sites-sampling) and for each site (site-specific sampling) separately. The derived posterior parameter pdfs from the all-sites sampling and site-specific sampling runs showed differences in relation with the specificities of each site. Analysis of these distributions also revealed model sensitivity to parameters conditioned on the measured data, as well as parameter correlations. [less ▲]

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See detailSoil organic carbon assessment by field and airborne spectrometry in bare croplands: accounting for soil surface roughness
Denis, Antoine ULg; Stevens, Antoine; Van Wesemael, Bas et al

in Geoderma (2014), 226-227(August 2014), 94102

Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350 nm to 2500 nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC ... [more ▼]

Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350 nm to 2500 nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC assessment (SOCa) is usually done by using calibration samples and multivariate models. However one of the major constraints of this technique, when used in field conditions is the spatial variation in surface soil properties (soil water content, roughness, vegetation residue) which induces a spectral variability not directly related to SOC and hence reduces the SOCa accuracy. This study focuses on the impact of soil roughness on SOCa by outdoor VIS-NIR-SWIR spectroscopy and is based on the assumption that soil roughness effect can be approximated by its related shadowing effect. A new method for identifying and correcting the effect of soil shadow on reflectance spectra measured with an Analytical Spectral Devices (ASD) spectroradiometer and an Airborne Hyperspectral Sensor (AHS-160) on freshly tilled fields in the Grand Duchy of Luxembourg was elaborated and tested. This method is based on the shooting of soil vertical photographs in the visible spectrum and the derivation of a shadow correction factor resulting from the comparison of “reflectance” of shadowed and illuminated soil areas. Moreover, the study of laboratory ASD reflectance of shadowed soil samples showed that the influence of shadow on reflectance varies according to wavelength. Consequently a correction factor in the entire [350–2500 nm] spectral range was computed to translate this differential influence. Our results showed that SOCa was improved by 27% for field spectral data and by 25% for airborne spectral data by correcting the effect of soil relative shadow. However, compared to simple mathematical treatment of the spectra (first derivative, etc.) able to remove variation in soil albedo due to roughness, the proposed method, leads only to slightly more accurate SOCa. [less ▲]

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