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See detailEstimating crop-specific evapotranspiration using remote-sensing imagery at various spatial resolutions for improving crop growth modelling
Sepulcre-Cantó, Guadalupe; Gellens-Meulenberghs, Françoise; Arboleda, Alirio et al

in International Journal of Remote Sensing (2013)

By governing water transfer between vegetation and atmosphere, evapotranspiration (ET) can have a strong influence on crop yields. An estimation of ET from remote sensing is proposed by the EUMETSAT ... [more ▼]

By governing water transfer between vegetation and atmosphere, evapotranspiration (ET) can have a strong influence on crop yields. An estimation of ET from remote sensing is proposed by the EUMETSAT ‘Satellite Application Facility’ (SAF) on Land Surface Analysis (LSA). This ET product is obtained operationally every 30 min using a simplified SVAT scheme that uses, as input, a combination of remotely sensed data and atmospheric model outputs. The standard operational mode uses other LSA-SAF products coming from SEVIRI imagery (the albedo, the downwelling surface shortwave flux, and the downwelling surface longwave flux), meteorological data, and the ECOCLIMAP database to identify and characterize the land cover. With the overall objective of adapting this ET product to crop growth monitoring necessities, this study focused first on improving the ET product by integrating crop-specific information from high and medium spatial resolution remote-sensing data. A Landsat (30 m)-based crop type classification is used to identify areas where the target crop, winter wheat, is located and where crop-specific Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m) time series of green area index (GAI) can be extracted. The SVAT model was run for 1 year (2007) over a study area covering Belgium and part of France using this supplementary information. Results were compared to those obtained using the standard operational mode. ET results were also compared with ground truth data measured in an eddy covariance station. Furthermore, transpiration and potential transpiration maps were retrieved and compared with those produced using the Crop Growth Monitoring System (CGMS), which is run operationally by the European Commission’s Joint Research Centre to produce in-season forecast of major European crops. The potential of using ET obtained from remote sensing to improve crop growth modelling in such a framework is studied and discussed. Finally, the use of the ET product is also explored by integrating it in a simpler modelling approach based on light-use efficiency. The Carnegie–Ames–Stanford Approach (CASA) agroecosystem model was therefore applied to obtain net primary production, dry matter productivity, and crop yield using only LSA-SAF products. The values of yield were compared with those obtained using CGMS, and the dry matter productivity values with those produced at the Flemish Institute for Technological Research (VITO). Results showed the potential of using this simplified remote-sensing method for crop monitoring. [less ▲]

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See detailEstimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data
Kouadio, Amani Louis ULg; Duveiller, Gregory; Djaby, Bakary ULg et al

in International Journal of Applied Earth Observation and Geoinformation (2012), 18

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation ... [more ▼]

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation between green leaf area (GLA) during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Our study sought to explore a simple approach for wheat yield forecasting at the regional level, based on metrics derived from the senescence phase of the green area index (GAI) retrieved from remote sensing data. This study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the target crop: winter wheat. A logistic function was used to characterize the GAI senescence phase and derive the metrics of this phase. Four regression-based models involving these metrics (i.e., the maximum GAI value, the senescence date and the thermal time taken to reach 50% of the green surface in the senescent phase) were related to official wheat yield data. The performances of such models at this regional scale showed that final yield could be estimated with an RMSE of 0.57 ton ha−1, representing about 7% as relative RMSE. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems. [less ▲]

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See detailWheat Yield Estimates at NUTS-3 level using MODIS data: an approach based on the decreasing curves of green area index temporal profiles
Kouadio, Amani Louis ULg; Duveiller, Gregory; Djaby, Bakary ULg et al

in Cawkwell, Fiona (Ed.) Proceedings of RSPSoc2010 Annual Conference. 1st-3rd September 2010, Cork, Ireland (Nottingham: RSPSoc) (2010, September)

Wheat is the most widely-grown food crop in the world and the most important cereal crop traded on international markets. An early prediction of its yield prior to harvest at regional, national and ... [more ▼]

Wheat is the most widely-grown food crop in the world and the most important cereal crop traded on international markets. An early prediction of its yield prior to harvest at regional, national and international scales can play a crucial role in global markets, policy and decision making. Many models for yield forecasting are available with varying levels of complexity and empiricism. The use of remote sensing technology for monitoring crop condition and predicting crop yields at regional scales have been studied extensively during these last decades. Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for yield and production forecasting. At field level, studies on crop breeding showed that a close correlation exists between green leaf area during maturation and grain yield in wheat. Thus, the onset and rate of senescence appeared as important factors for determining grain yield of this crop. The aim of this research is to explore a simplified approach for wheat yield forecasting at the European NUTS-3 administrative level, based on metrics derived from the senescence phase of green area index (GAI) estimated from remote sensing data. This study takes advantage of considerable recent improvements in sensor technology and data availability through the opportunity of applying medium/coarse spatial resolution data for deriving crop specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is constituted by a high cover fraction of winter wheat. This approach is tested on 2 crop growing season over a 300 by 300 km study site comprising Belgium and northern France within the framework of the GLOBAM (GLObal Agricultural Monitoring systems by integration of earth observation and modelling techniques) project. The validation of such an approach will involve the comparison with official wheat yield data at NUTS-3 level. [less ▲]

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See detailCombining remote sensing imagery of both fine and coarse spatial resolution to estimate crop evapotranspiration and quantifying its influence on crop growth monitoring
Sepulcre-Canto, Guadalupe; Gellens-Meulenberghs, Françoise; Arboleda, Alirio et al

in Geophysical Research Abstracts (2010)

This study has been carried out in the framework of the GLOBAM -Global Agricultural Monitoring system by integration of earth observation and modeling techniques- project whose objective is to fill the ... [more ▼]

This study has been carried out in the framework of the GLOBAM -Global Agricultural Monitoring system by integration of earth observation and modeling techniques- project whose objective is to fill the methodological gap between the state of the art of local crop monitoring and the operational requirements of the global monitoring system programs. To achieve this goal, the research aims to develop an integrated approach using remote sensing and crop growth modeling. This paper concerns the use of MSG geostationnary satellite data for the calculation of Actual Evapotranspiration and its integration into a crop growth model. [less ▲]

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See detailExploring the potential of crop specific green area index time series to improve yield estimation at regional scale
Duveiller, Gregory; de Wit, Allard; Kouadio, Amani Louis ULg et al

in Sobrino, J. A. (Ed.) Proceedings of the 3rd International Symposium on Recent Advances in Quantitative Remote Sensing (RAQRS'III) (2010)

Crop status, such as the Green Area Index (GAI), can be retrieved from satellite observations by modelling and inverting the radiative transfer within the canopy. Providing such information along the ... [more ▼]

Crop status, such as the Green Area Index (GAI), can be retrieved from satellite observations by modelling and inverting the radiative transfer within the canopy. Providing such information along the growing season can potentially improve crop growth modelling and yield estimation. However, such approaches have proven difficult to apply on coarse resolution satellite data due to the fragmented land cover in many parts of the World. Advances in operational crop mapping will sooner or later allow the production of crop maps relatively early in the crop growth season, thereby providing an opportunity to sample pixels from medium/coarse spatial resolution data with relatively high cover fraction of a particular crop type to derive crop specific GAI time series. This research explores how to use such time series derived from MODIS to produce indicators of crop yield using two approaches over part of Belgium. The first method consists in looking at metrics of the decreasing part of the GAI curves when senescence occurs. Such metrics, like the position of the inflexion point, have been shown to be significantly correlated to yield. The second approach is to optimize the WOFOST model used in the European Crop Growth Monitoring System (CGMS) based on the GAI time series. Results show that, although the optimized model shows considerably better performance than the model running on the default parameter, the model is sometimes outperformed by the simpler metric approach. In all cases, indicators including remote sensing information provide better estimates that the average yield of previous years. [less ▲]

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