Suchergebnisse

The introduction of high-resolution Sentinels combined with the use of high-quality digital agricultural parcel registration systems is driving the move towards at-parcel agricultural monitoring. The European Union's Common Agricultural Policy (CAP) has introduced the concept of CAP monitoring to help simplify the management and control of farmers' parcel declarations for area support measures. This study proposes a proof of concept of this monitoring approach introducing and applying the concept of 'markers'. Using Sentinel-1- and -2-derived (S1 and S2) markers, we evaluate parcels declared as grassland in the Gelderse Vallei in the Netherlands covering more than 15,000 parcels. The satellite markers—respectively based on crop-type deep learning classification using S1 backscattering and coherence data and on detecting bare soil with S2 during the growing season—aim to identify grassland-declared parcels for which (1) the marker suggests another crop type or (2) which appear to have been ploughed during the year. Subsequently, a field-survey was carried out in October 2017 to target the parcels identified and to build a relevant ground-truth sample of the area. For the latter purpose, we used a high-definition camera mounted on the roof of a car to continuously sample geo-tagged digital imagery, as well as an app-based approach to identify the targeted fields. Depending on which satellite-based marker or combination of markers is used, the number of parcels identified ranged from 2.57% (marked by both the S1 and S2 markers) to 17.12% of the total of 11,773 parcels declared as grassland. After confirming with the ground-truth, parcels flagged by the combined S1 and S2 marker were robustly detected as non-grassland parcels (F-score = 0.9). In addition, the study demonstrated that street-level imagery collection could improve collection efficiency by a factor seven compared to field visits (1411 parcels/day vs. 217 parcels/day) while keeping an overall accuracy of about 90% compared to the ground-truth. This ...

The MARS-Crop Yield Forecasting System (M-CYFS) is used since 1993 to forecast the yields of all major crops in the European Union (EU) based on gridded runs of the WOFOST crop model. Using 28 years of observation, from 1988 to 2015, we quantified the variability in crop yield reported by all 28 EU Member States (MS) that can be explained by each individual WOFOST crop model based predictors and a few simple meteorological variables. A linear regression is used as a screening tool to quantify the relationship between each predictor and the yield residuals from the trend throughout the crop cycle for 168 country/crop combinations, assuming the yield residuals from the trend depend on the inter-annual climate variability. The results are plotted and analyzed at different level: every 10 days for each country crop/combination and each predictor; synthetized every 10 days for each country/crop combination keeping the predictor showing the best relationship with the yield residuals; finally, the best predictor found for each MS during the entire growing season is used to evaluate the ability of the model to estimate yield variability of each crop at European scale. While 61% of the grain maize (Zea mays L.) yield variability can be anticipated 80 days before harvest with the simulated water limited biomass for countries where rainfed maize prevails, 41% of the soft wheat (Triticum aestivum L.) yield variability can be reproduced a month before harvest, the best estimates being obtained where wheat is predominantly exposed to water stress. For the other crops analyzed, the results are also found to be reliable for crops predominantly exposed to water stress and becoming unreliable in agricultural systems exposed to an oceanic climate with a high level of inputs. The agro-meteorological processes related to an excess of water (nitrogen losses, diseases, anoxia, harvest conditions) would need to be disentangled and better integrated into the crop modeling system to improve the predictors. The monthly cumulated meteorological predictors are performing only slightly worse than the crop model predictors and help to characterize the main processes responsible for the yield variability. Nevertheless, the predictive capacity of the meteorological predictors is spatially and temporally incoherent and differs according to the crop phenology. In comparison, the M-CYFS crop model predictors are more consistent since the predictors summarize the succession of agro-meteorological conditions determining the yield throughout the entire growing season.

International audience ; The MARS-Crop Yield Forecasting System (M-CYFS) is used since 1993 to forecast the yields of all major crops in the European Union (EU) based on gridded runs of the WOFOST crop model. Using 28 years of observation, from 1988 to 2015, we quantified the variability in crop yield reported by all 28 EU Member States (MS) that can be explained by each individual WOFOST crop model based predictors and a few simple meteorological variables. A linear regression is used as a screening tool to quantify the relationship between each predictor and the yield residuals from the trend throughout the crop cycle for 168 country/crop combinations, assuming the yield residuals from the trend depend on the inter-annual climate variability. The results are plotted and analyzed at different level: every 10 days for each country crop/combination and each predictor; synthetized every 10 days for each country/crop combination keeping the predictor showing the best relationship with the yield residuals; finally, the best predictor found for each MS during the entire growing season is used to evaluate the ability of the model to estimate yield variability of each crop at European scale. While 61% of the grain maize ( Zea mays L.) yield variability can be anticipated 80 days before harvest with the simulated water limited biomass for countries where rainfed maize prevails, 41% of the soft wheat ( Triticum aestivum L.) yield variability can be reproduced a month before harvest, the best estimates being obtained where wheat is predominantly exposed to water stress. For the other crops analyzed, the results are also found to be reliable for crops predominantly exposed to water stress and becoming unreliable in agricultural systems exposed to an oceanic climate with a high level of inputs. The agro-meteorological processes related to an excess of water (nitrogen losses, diseases, anoxia, harvest conditions) would need to be disentangled and better integrated into the crop modeling system to improve the predictors. The ...

International audience ; The MARS-Crop Yield Forecasting System (M-CYFS) is used since 1993 to forecast the yields of all major crops in the European Union (EU) based on gridded runs of the WOFOST crop model. Using 28 years of observation, from 1988 to 2015, we quantified the variability in crop yield reported by all 28 EU Member States (MS) that can be explained by each individual WOFOST crop model based predictors and a few simple meteorological variables. A linear regression is used as a screening tool to quantify the relationship between each predictor and the yield residuals from the trend throughout the crop cycle for 168 country/crop combinations, assuming the yield residuals from the trend depend on the inter-annual climate variability. The results are plotted and analyzed at different level: every 10 days for each country crop/combination and each predictor; synthetized every 10 days for each country/crop combination keeping the predictor showing the best relationship with the yield residuals; finally, the best predictor found for each MS during the entire growing season is used to evaluate the ability of the model to estimate yield variability of each crop at European scale. While 61% of the grain maize ( Zea mays L.) yield variability can be anticipated 80 days before harvest with the simulated water limited biomass for countries where rainfed maize prevails, 41% of the soft wheat ( Triticum aestivum L.) yield variability can be reproduced a month before harvest, the best estimates being obtained where wheat is predominantly exposed to water stress. For the other crops analyzed, the results are also found to be reliable for crops predominantly exposed to water stress and becoming unreliable in agricultural systems exposed to an oceanic climate with a high level of inputs. The agro-meteorological processes related to an excess of water (nitrogen losses, diseases, anoxia, harvest conditions) would need to be disentangled and better integrated into the crop modeling system to improve the predictors. The monthly cumulated meteorological predictors are performing only slightly worse than the crop model predictors and help to characterize the main processes responsible for the yield variability. Nevertheless, the predictive capacity of the meteorological predictors is spatially and temporally incoherent and differs according to the crop phenology. In comparison, the M-CYFS crop model predictors are more consistent since the predictors summarize the succession of agro-meteorological conditions determining the yield throughout the entire growing season.

New approaches to collect in-situ data are needed to complement the high spatial (10 m) and temporal (5 d) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground truth collection is expensive, lacks scale, fails to exploit opportunities for automation, and is prone to sampling error. Here we evaluate the potential contribution of opportunistically exploiting crowdsourced street-level imagery to collect massive high-quality in-situ data in the context of crop monitoring. This study assesses this potential by answering two questions: (1) what is the spatial availability of these images across the European Union (EU), and (2) can these images be transformed to useful data? To answer the first question, we evaluated the EU availability of street-level images on Mapillary—the largest open-access platform for such images—against the Land Use and land Cover Area frame Survey (LUCAS) 2018, a systematic surveyed sampling of 337,031 points. For 37.78% of the LUCAS points a crowdsourced image is available within a 2 km buffer, with a mean distance of 816.11 m. We estimate that 9.44% of the EU territory has a crowdsourced image within 300 m from a LUCAS point, illustrating the huge potential of crowdsourcing as a complementary sampling tool. After artificial and built up (63.14%), and inland water (43.67%) land cover classes, arable land has the highest availability at 40.78%. To answer the second question, we focus on identifying crops at parcel level using all 13.6 million Mapillary images collected in the Netherlands. Only 1.9% of the contributors generated 75.15% of the images. A procedure was developed to select and harvest the pictures potentially best suited to identify crops using the geometries of 785,710 Dutch parcels and the pictures' meta-data such as camera orientation and focal length. Availability of crowdsourced imagery looking at parcels was assessed for eight different crop ...

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.

Even if global warming is kept below +2°C, European agriculture will be significantly impacted. Soil degradation may amplify these impacts substantially and thus hamper crop production further. We quantify biophysical consequences and bracket uncertainty of +2°C warming on calories supply from 10 major crops and vulnerability to soil degradation in Europe using crop modeling. The Environmental Policy Integrated Climate (EPIC) model together with regional climate projections from the European branch of the Coordinated Regional Downscaling Experiment (EURO-CORDEX) was used for this purpose. A robustly positive calorie yield change was estimated for the EU Member States except for some regions in Southern and South-Eastern Europe. The mean impacts range from +30 Gcal ha−1 in the north, through +25 and +20 Gcal ha−1 in Western and Eastern Europe, respectively, to +10 Gcal ha−1 in the south if soil degradation and heat impacts are not accounted for. Elevated CO2 and increased temperature are the dominant drivers of the simulated yield changes in high-input agricultural systems. The growth stimulus due to elevated CO2 may offset potentially negative yield impacts of temperature increase by +2°C in most of Europe. Soil degradation causes a calorie vulnerability ranging from 0 to 50 Gcal ha−1 due to insufficient compensation for nutrient depletion and this might undermine climate benefits in many regions, if not prevented by adaptation measures, especially in Eastern and North-Eastern Europe. Uncertainties due to future potentials for crop intensification are about 2-50 times higher than climate change impacts.

International audience ; This study assesses crop residues in the EU from major crops using empirical models to predict crop residues from yield statistics; furthermore it analyses the inter‐annual variability of those estimates over the period 1998‐2015, identifying its main drivers across Europe. The models were constructed based on an exhaustive collection of experimental data from scientific papers for the crops: wheat, barley, rye, oats, triticale, rice, maize, sorghum, rapeseed, sunflower, soybean, potato and sugarbeet. We discuss the assumptions on the relationship between yield and the harvest index, adopted by previous studies, to interpret the experimental data, quantify the uncertainties of these models, and establish the premises to implement them at regional scale –i.e NUTS level 3– within the EU. To cope this, we created a consolidated sub‐national statistical data along with an algorithm able to aggregate (figures are provided at country level) and disaggregate (production at 25 km grid is provided as supplementary material) estimates. The total lignocellulosic biomass production in the EU28 over the review period, according to our models, is 419 Mt, from which wheat is the major contributor (155 Mt). Our results show that maize and rapeseed are the two crops with the highest residue yield, respectively 8.9 and 8.6 t ha‐1. The spatial analysis revealed that these three crops, which, according to our results, are feedstocks highly suitable a priori for second generation biofuels in the EU and are unevenly distributed across Europe. Weather fluctuation was identified as the major driver in residue production from cereals, while, in the case of starch crops and oilseeds – which are predominant in northern Europe – corresponded to the marked production trend likely influenced by the agricultural policies and agro‐management over the review period. Additionally, our study highlights the limitation of such empirical models in quantifying lignocellulosic biomass in the EU.

International audience ; This study assesses crop residues in the EU from major crops using empirical models to predict crop residues from yield statistics; furthermore it analyses the inter‐annual variability of those estimates over the period 1998‐2015, identifying its main drivers across Europe. The models were constructed based on an exhaustive collection of experimental data from scientific papers for the crops: wheat, barley, rye, oats, triticale, rice, maize, sorghum, rapeseed, sunflower, soybean, potato and sugarbeet. We discuss the assumptions on the relationship between yield and the harvest index, adopted by previous studies, to interpret the experimental data, quantify the uncertainties of these models, and establish the premises to implement them at regional scale –i.e NUTS level 3– within the EU. To cope this, we created a consolidated sub‐national statistical data along with an algorithm able to aggregate (figures are provided at country level) and disaggregate (production at 25 km grid is provided as supplementary material) estimates. The total lignocellulosic biomass production in the EU28 over the review period, according to our models, is 419 Mt, from which wheat is the major contributor (155 Mt). Our results show that maize and rapeseed are the two crops with the highest residue yield, respectively 8.9 and 8.6 t ha‐1. The spatial analysis revealed that these three crops, which, according to our results, are feedstocks highly suitable a priori for second generation biofuels in the EU and are unevenly distributed across Europe. Weather fluctuation was identified as the major driver in residue production from cereals, while, in the case of starch crops and oilseeds – which are predominant in northern Europe – corresponded to the marked production trend likely influenced by the agricultural policies and agro‐management over the review period. Additionally, our study highlights the limitation of such empirical models in quantifying lignocellulosic biomass in the EU.

This study assesses crop residues in the EU from major crops using empirical models to predict crop residues from yield statistics; furthermore it analyses the inter‐annual variability of those estimates over the period 1998‐2015, identifying its main drivers across Europe. The models were constructed based on an exhaustive collection of experimental data from scientific papers for the crops: wheat, barley, rye, oats, triticale, rice, maize, sorghum, rapeseed, sunflower, soybean, potato and sugarbeet. We discuss the assumptions on the relationship between yield and the harvest index, adopted by previous studies, to interpret the experimental data, quantify the uncertainties of these models, and establish the premises to implement them at regional scale –i.e NUTS level 3– within the EU. To cope this, we created a consolidated sub‐national statistical data along with an algorithm able to aggregate (figures are provided at country level) and disaggregate (production at 25 km grid is provided as supplementary material) estimates. The total lignocellulosic biomass production in the EU28 over the review period, according to our models, is 419 Mt, from which wheat is the major contributor (155 Mt). Our results show that maize and rapeseed are the two crops with the highest residue yield, respectively 8.9 and 8.6 t ha‐1. The spatial analysis revealed that these three crops, which, according to our results, are feedstocks highly suitable a priori for second generation biofuels in the EU and are unevenly distributed across Europe. Weather fluctuation was identified as the major driver in residue production from cereals, while, in the case of starch crops and oilseeds – which are predominant in northern Europe – corresponded to the marked production trend likely influenced by the agricultural policies and agro‐management over the review period. Additionally, our study highlights the limitation of such empirical models in quantifying lignocellulosic biomass in the EU.

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens. Moreover, the photographs submitted through the app were not always of sufficient quality. For these reasons, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree. Furthermore, a near real-time quality assurance system was implemented to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to determine whether citizens can provide high quality in situ data on LULC through crowdsourcing that can complement LUCAS. We compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. As shown by the cost comparisons with LUCAS, FotoQuest can complement LUCAS surveys by enabling continuous collection of ...

Accurately characterizing land surface changes with Earth Observation requires geo-located ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS). A total of 1351293 observations at 651780 unique locations for 106 variables along with 5.4 million photos were collected during five LUCAS surveys. Until now, these data have never been harmonised into one database, limiting full exploitation of the information. This paper describes the LUCAS point sampling/surveying methodology, including collection of standard variables such as land cover, environmental parameters, and full resolution landscape and point photos, and then describes the harmonisation process. The resulting harmonised database is the most comprehensive in-situ dataset on land cover and use in the EU. The database is valuable for geo-spatial and statistical analysis of land use and land cover change. Furthermore, its potential to provide multi-temporal in-situ data will be enhanced by recent computational advances such as deep learning.