Suchergebnisse

This study addresses the role of satellite Earth Observation (EO) indicators within an operational crop yield forecasting system for the European Union (EU) and neighbouring countries, by exploring the correlation between official yield statistics and indicators derived from fAPAR time-series at sub-national level for the period 1999-2012, and by identifying possible differences across agro-climatic conditions in Europe.A significant correlation between fAPAR and official yields (R2>0.6) was found in water-limited yield agro-climatic conditions (e.g. the Black Sea region and the Mediterranean basin) for all three crops studied. In regions where crops experience frequent water stress, most of the yield inter-annual variability is explained by substantial changes in leaf area from one year to another, and can be well captured by regional fAPAR time-series. By contrast, in regions characterized by high yields (e.g. northern Europe) - where water constraints are generally not frequent and, therefore, fAPAR inter-annual variability is low - the correlation between fAPAR and yield is weaker (R2<0.5) as yield variations tend to be explained by multiple factors other than green leaf area.These results confirm the reliability of EO time-series for operational crop yield forecasting at regional level, but also suggest that additional meteorological variables (temperature, precipitation, evapotranspiration) need to be taken into account to interpret EO products meaningfully. Moreover, specific issues related to the spatial resolution of the EO-products, and the absence of dynamic crop masks, currently impede access to crop-specific time-series in the fragmented agricultural landscapes of Europe, and restrict the use of 1-km biophysical products to major crops.

Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough crop signals. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop groups (i.e., winter and spring crops and summer crops) based on a MODIS-NDVI timeseries. We applied this method to the European Union at a 250 m spatial resolution. Pre-processed and smoothed, daily normalized difference vegetation index (NDVI) data (2001-2017) were used to first extract the phenological data. To account for regional characteristics (varying climate, agro-management, etc.), these data were clustered by administrative units and by year using a Gaussian mixture model. The number of clusters was pre-defined using data from regional agricultural acreage statistics. After automatic labelling, clusters were filtered based on agronomic knowledge and phenological information extracted from the same timeseries. The resulting pure pixels were validated with two different datasets, one based on high-resolution Sentinel-2 data (5 sites, 2 years) and one based on a regional crop map (1 site, 7 years). For the winter and spring crop class, pixel purity amounted to 93% using the first validation dataset and to 73% using the second one, averaged over the different years. For summer crops, the respective values were 61% (91% without one critical validation site) and 72%. The phenological analyses revealed a clear trend towards an earlier NDVI peak (approximately -0.28 days/year) for winter and spring crops across Europe. We expect that this dataset will be useful for various applications, from crop model calibration to ...

Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough crop signals. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop groups (i.e., winter and spring crops and summer crops) based on a MODIS-NDVI timeseries. We applied this method to the European Union at a 250 m spatial resolution. Pre-processed and smoothed, daily normalized difference vegetation index (NDVI) data (2001-2017) were used to first extract the phenological data. To account for regional characteristics (varying climate, agro-management, etc.), these data were clustered by administrative units and by year using a Gaussian mixture model. The number of clusters was pre-defined using data from regional agricultural acreage statistics. After automatic labelling, clusters were filtered based on agronomic knowledge and phenological information extracted from the same timeseries. The resulting pure pixels were validated with two different datasets, one based on high-resolution Sentinel-2 data (5 sites, 2 years) and one based on a regional crop map (1 site, 7 years). For the winter and spring crop class, pixel purity amounted to 93% using the first validation dataset and to 73% using the second one, averaged over the different years. For summer crops, the respective values were 61% (91% without one critical validation site) and 72%. The phenological analyses revealed a clear trend towards an earlier NDVI peak (approximately -0.28 days/year) for winter and spring crops across Europe. We expect that this dataset will be useful for various applications, from crop model calibration to operational crop monitoring and yield forecasting.

International audience ; This study addresses the role of satellite Earth Observation (EO) indicators within an operational crop yield forecasting system for the European Union (EU) and neighbouring countries, by exploring the correlation between official yield statistics and indicators derived from fAPAR time-series at sub-national level for the period 1999-2012, and by identifying possible differences across agro-climatic conditions in Europe.A significant correlation between fAPAR and official yields (R2>0.6) was found in water-limited yield agro-climatic conditions (e.g. the Black Sea region and the Mediterranean basin) for all three crops studied. In regions where crops experience frequent water stress, most of the yield inter-annual variability is explained by substantial changes in leaf area from one year to another, and can be well captured by regional fAPAR time-series. By contrast, in regions characterized by high yields (e.g. northern Europe) - where water constraints are generally not frequent and, therefore, fAPAR inter-annual variability is low - the correlation between fAPAR and yield is weaker (R2<0.5) as yield variations tend to be explained by multiple factors other than green leaf area.These results confirm the reliability of EO time-series for operational crop yield forecasting at regional level, but also suggest that additional meteorological variables (temperature, precipitation, evapotranspiration) need to be taken into account to interpret EO products meaningfully. Moreover, specific issues related to the spatial resolution of the EO-products, and the absence of dynamic crop masks, currently impede access to crop-specific time-series in the fragmented agricultural landscapes of Europe, and restrict the use of 1-km biophysical products to major crops.

International audience ; This study addresses the role of satellite Earth Observation (EO) indicators within an operational crop yield forecasting system for the European Union (EU) and neighbouring countries, by exploring the correlation between official yield statistics and indicators derived from fAPAR time-series at sub-national level for the period 1999-2012, and by identifying possible differences across agro-climatic conditions in Europe.A significant correlation between fAPAR and official yields (R2>0.6) was found in water-limited yield agro-climatic conditions (e.g. the Black Sea region and the Mediterranean basin) for all three crops studied. In regions where crops experience frequent water stress, most of the yield inter-annual variability is explained by substantial changes in leaf area from one year to another, and can be well captured by regional fAPAR time-series. By contrast, in regions characterized by high yields (e.g. northern Europe) - where water constraints are generally not frequent and, therefore, fAPAR inter-annual variability is low - the correlation between fAPAR and yield is weaker (R2<0.5) as yield variations tend to be explained by multiple factors other than green leaf area.These results confirm the reliability of EO time-series for operational crop yield forecasting at regional level, but also suggest that additional meteorological variables (temperature, precipitation, evapotranspiration) need to be taken into account to interpret EO products meaningfully. Moreover, specific issues related to the spatial resolution of the EO-products, and the absence of dynamic crop masks, currently impede access to crop-specific time-series in the fragmented agricultural landscapes of Europe, and restrict the use of 1-km biophysical products to major crops.