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In this upcoming Common Agricultural Policy (CAP) reform, the use of satellite imagery is taking an increasing role for improving the Integrated Administration and Control System (IACS). Considering the operational aspect of the CAP monitoring process, the use of Sentinel-1 SAR (Synthetic Aperture Radar) images is highly relevant, especially in regions with a frequent cloud cover, such as Belgium. Indeed, SAR imagery does not depend on sunlight and is barely affected by the presence of clouds. Moreover, the SAR signal is particularly sensitive to the geometry and the water content of the target. Crop identification is often a pre-requisite to monitor agriculture at parcel level (ploughing, harvest, grassland mowing, intercropping, etc.) The main goal of this study is to assess the performances and constraints of a SAR-based crop classification in an operational large-scale application. The Random Forest object-oriented classification model is built on Sentinel-1 time series from January to August 2020 only. It can identify crops in the Walloon Region (south part of Belgium) with high performance: 93.4% of well-classified area, representing 88.4% of the parcels. Among the 48 crop groups, the six most represented ones get a F1-score higher or equal to 84%. Additionally, this research documents how the classification performance is affected by different parameters: the SAR orbit, the size of the training dataset, the use of different internal buffers on parcel polygons before signal extraction, the set of explanatory variables, and the period of the time series. In an operational context, this allows to choose the right balance between classification accuracy and model complexity. A key result is that using a training dataset containing only 3.2% of the total number of parcels allows to correctly classify 91.7% of the agricultural area. The impact of rain and snow is also discussed. Finally, this research analyses how the classification accuracy depends on some characteristics of the parcels like their shape or size. This allows to assess the relevance of the classification depending on those characteristics, as well as to identify a subset of parcels for which the global accuracy is higher.
Emilie Beriaux; Alban Jago; Cozmin Lucau-Danila; Viviane Planchon; Pierre Defourny. Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring. Remote Sensing 2021, 13, 2785 .
AMA StyleEmilie Beriaux, Alban Jago, Cozmin Lucau-Danila, Viviane Planchon, Pierre Defourny. Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring. Remote Sensing. 2021; 13 (14):2785.
Chicago/Turabian StyleEmilie Beriaux; Alban Jago; Cozmin Lucau-Danila; Viviane Planchon; Pierre Defourny. 2021. "Sentinel-1 Time Series for Crop Identification in the Framework of the Future CAP Monitoring." Remote Sensing 13, no. 14: 2785.
Winter cover crops, used as green manure, can supply up to 45 units of nitrogen per hectare to the following summer crops. In order to contribute to the establishment of the nitrogen balance sheet for fertilisation recommendation of subsequent main crop at field scale, this supply is currently derived from the biomass production, classically estimated visually using 3 classes: 0–1, 1–3, 3+ tons of dry matter per hectare (Mg DM ha−1). The capabilities of Sentinel-2 satellite data to retrieve an operator-independent winter cover crop biomass have been assessed. Biomass samples were collected in 1 m quadrats for various types of winter cover fields classically used in Belgium (mustard, phacelia, oat and a range of mixed cover), in 2016 and 2017 (yield between 0.1 to 5 tons of dry matter per hectare). Empirical relationships between the winter cover crop biomass and a wide range of vegetation indices (VIs) derived from Sentinel-2 have been defined, and the most performant VI identified. For pure stands of winter cover crops, the cross-validation RMSE (CVRMSE) of the best model is 0.36 Mg DM ha−1 for mustard and 0.3 Mg DM ha−1 for phacelia. The CVRMSE observed for mixed stands, around 0.61 Mg DM ha−1, is roughly two times higher than the CVRMSE observed for pure stands. The added-value of objective satellite-based estimation of winter cover biomass was also assessed by comparing respective estimations with regards to an independent reference dataset made of sample measurements on the ground. Models based on Earth observation showed better results than farmer visual assessment for mustard crops, and were as good as farmers for phacelia crops.
Dimitri Goffart; Yannick Curnel; Viviane Planchon; Jean-Pierre Goffart; Pierre Defourny. Field-scale assessment of Belgian winter cover crops biomass based on Sentinel-2 data. European Journal of Agronomy 2021, 126, 126278 .
AMA StyleDimitri Goffart, Yannick Curnel, Viviane Planchon, Jean-Pierre Goffart, Pierre Defourny. Field-scale assessment of Belgian winter cover crops biomass based on Sentinel-2 data. European Journal of Agronomy. 2021; 126 ():126278.
Chicago/Turabian StyleDimitri Goffart; Yannick Curnel; Viviane Planchon; Jean-Pierre Goffart; Pierre Defourny. 2021. "Field-scale assessment of Belgian winter cover crops biomass based on Sentinel-2 data." European Journal of Agronomy 126, no. : 126278.
Crop type classification with satellite imageries is widely applied in support of crop production management and food security strategy. The abundant supply of these satellite data is accelerating and blooming the application of crop classification as satellite data at 10 m to 30 m spatial resolution have been made accessible easily, widely and free of charge, including optical sensors, the wide field of viewer (WFV) onboard the GaoFen (GF, high resolution in English) series from China, the MultiSpectral Instrument (MSI) onboard Sentinel 2 (S2) from Europe and the Operational Land Imager (OLI) onboard Landsat 8 (L8) from USA, thanks to the implementation of the open data policy. There are more options in using the satellite data as these three data sources are available. This paper explored the different capability of these three data sources for the crop type mapping in the same area and within the same growing season. The study was executed in a flat and irrigated area in Northwest China. Nine types of crop were classified using these three kinds of time series of data sources in 2017 and 2018, respectively. The same suites of the training samples and validation samples were applied for each of the data sources. Random Forest (RF) was used as the classifier for the crop type classification. The confusion error matrix with the OA, Kappa and F1-score was used to evaluate the accuracy of the classifications. The result shows that GF-1 relatively has the lowest accuracy as a consequence of the limited spectral bands, but the accuracy is at 93–94%, which is still excellent and acceptable for crop type classification. S2 achieved the highest accuracy of 96–98%, with 10 available bands for the crop type classification at either 10 m or 20 m. The accuracy of 97–98% for L8 is in the middle but the difference is small in comparison with S2. Any of these satellite data may be used for the crop type classification within the growing season, with a very good accuracy if the training datasets were well tuned.
Jinlong Fan; Xiaoyu Zhang; Chunliang Zhao; Zhihao Qin; Mathilde De Vroey; Pierre Defourny. Evaluation of Crop Type Classification with Different High Resolution Satellite Data Sources. Remote Sensing 2021, 13, 911 .
AMA StyleJinlong Fan, Xiaoyu Zhang, Chunliang Zhao, Zhihao Qin, Mathilde De Vroey, Pierre Defourny. Evaluation of Crop Type Classification with Different High Resolution Satellite Data Sources. Remote Sensing. 2021; 13 (5):911.
Chicago/Turabian StyleJinlong Fan; Xiaoyu Zhang; Chunliang Zhao; Zhihao Qin; Mathilde De Vroey; Pierre Defourny. 2021. "Evaluation of Crop Type Classification with Different High Resolution Satellite Data Sources." Remote Sensing 13, no. 5: 911.
Grasslands encompass vast and diverse ecosystems that provide food, wildlife habitat and carbon storage. Their large range in land use intensity significantly impacts their ecological value and the balance between these goods and services. Mowing dates and frequencies are major aspects of grassland use intensity, which have an impact on their ecological value as habitats. Previous studies highlighted the feasibility of detecting mowing events based on remote sensing time series, a few of which using synthetic aperture radar (SAR) imagery. Although providing encouraging results, research on grassland mowing detection often lacks sufficient precise reference data for corroboration. The goal of the present study is to quantitatively and statistically assess the potential of Sentinel-1 C-band SAR for detecting mowing events in various agricultural grasslands, using a large and diverse reference data set collected in situ. Several mowing detection methods, based on SAR backscattering and interferometric coherence time series, were thoroughly evaluated. Results show that 54% of mowing events could be detected in hay meadows, based on coherence jumps. Grazing events were identified as a major confounding factor, as most false detections were made in pastures. Parcels with one mowing event in the summer were identified with the highest accuracy (71%). Overall, this study demonstrates that mowing events can be detected through Sentinel-1 coherence. However, the performances could probably be further enhanced by discriminating pastures beforehand and combining Sentinel-1 and Sentinel-2 data for mowing detection.
Mathilde De Vroey; Julien Radoux; Pierre Defourny. Grassland Mowing Detection Using Sentinel-1 Time Series: Potential and Limitations. Remote Sensing 2021, 13, 348 .
AMA StyleMathilde De Vroey, Julien Radoux, Pierre Defourny. Grassland Mowing Detection Using Sentinel-1 Time Series: Potential and Limitations. Remote Sensing. 2021; 13 (3):348.
Chicago/Turabian StyleMathilde De Vroey; Julien Radoux; Pierre Defourny. 2021. "Grassland Mowing Detection Using Sentinel-1 Time Series: Potential and Limitations." Remote Sensing 13, no. 3: 348.
Land cover maps contribute to a large diversity of geospatial applications, including but not limited to land management, hydrology, land use planning, climate modeling and biodiversity monitoring. In densely populated and highly fragmented landscapes as observed in the Walloon region (Belgium), very high spatial resolution is required to depict all the infrastructures, buildings and most of the structural elements of the semi-natural landscapes (like hedges and small water bodies). Because of the resolution, the vertical dimension needs explicit handling to avoid discontinuities incompatible with many applications. For example, how to map a river flowing under a bridge? The particularity of our data is to provide a two-digit land cover code to label all the overlapping items. The identification of all the overlaps resulted from the combination of remote sensing image analysis and decision rules involving ancillary data. The final product is therefore semantically precise and accurate in terms of land cover description thanks to the addition of 24 classes on top of the 11 pure land cover classes. The quality of the map has been assessed using a state-of-the-art validation scheme. Its overall accuracy is as high as 91.5%, with an average producer’s accuracy of 86% and an average user’s accuracy of 91%.
Céline Bassine; Julien Radoux; Benjamin Beaumont; Taïs Grippa; Moritz Lennert; Céline Champagne; Mathilde De Vroey; Augustin Martinet; Olivier Bouchez; Nicolas Deffense; Eric Hallot; Eléonore Wolff; Pierre Defourny. First 1-M Resolution Land Cover Map Labeling the Overlap in the 3rd Dimension: The 2018 Map for Wallonia. Data 2020, 5, 117 .
AMA StyleCéline Bassine, Julien Radoux, Benjamin Beaumont, Taïs Grippa, Moritz Lennert, Céline Champagne, Mathilde De Vroey, Augustin Martinet, Olivier Bouchez, Nicolas Deffense, Eric Hallot, Eléonore Wolff, Pierre Defourny. First 1-M Resolution Land Cover Map Labeling the Overlap in the 3rd Dimension: The 2018 Map for Wallonia. Data. 2020; 5 (4):117.
Chicago/Turabian StyleCéline Bassine; Julien Radoux; Benjamin Beaumont; Taïs Grippa; Moritz Lennert; Céline Champagne; Mathilde De Vroey; Augustin Martinet; Olivier Bouchez; Nicolas Deffense; Eric Hallot; Eléonore Wolff; Pierre Defourny. 2020. "First 1-M Resolution Land Cover Map Labeling the Overlap in the 3rd Dimension: The 2018 Map for Wallonia." Data 5, no. 4: 117.
Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work.
Libo Wang; Paul Bartlett; Darren Pouliot; Ed Chan; Céline Lamarche; Michael A. Wulder; Pierre Defourny; Mike Brady. Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada. Remote Sensing 2019, 11, 2286 .
AMA StyleLibo Wang, Paul Bartlett, Darren Pouliot, Ed Chan, Céline Lamarche, Michael A. Wulder, Pierre Defourny, Mike Brady. Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada. Remote Sensing. 2019; 11 (19):2286.
Chicago/Turabian StyleLibo Wang; Paul Bartlett; Darren Pouliot; Ed Chan; Céline Lamarche; Michael A. Wulder; Pierre Defourny; Mike Brady. 2019. "Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada." Remote Sensing 11, no. 19: 2286.
Ecotopes are the smallest ecologically distinct landscape features in a landscape mapping and classification system. Mapping ecotopes therefore enables the measurement of ecological patterns, process and change. In this study, a multi-source GEOBIA workflow is used to improve the automated delineation and descriptions of ecotopes. Aerial photographs and LIDAR data provide input for landscape segmentation based on spectral signature, height structure and topography. Each segment is then characterized based on the proportion of land cover features identified at 2 m pixel-based classification. The results show that the use of hillshade bands simultaneously with spectral bands increases the consistency of the ecotope delineation. These results are promising to further describe biotopes of high ecological conservation value, as suggested by a successful test on ravine forest biotope.
Julien Radoux; Axel Bourdouxhe; William Coos; Marc Dufrêne; Pierre Defourny. Improving Ecotope Segmentation by Combining Topographic and Spectral Data. Remote Sensing 2019, 11, 354 .
AMA StyleJulien Radoux, Axel Bourdouxhe, William Coos, Marc Dufrêne, Pierre Defourny. Improving Ecotope Segmentation by Combining Topographic and Spectral Data. Remote Sensing. 2019; 11 (3):354.
Chicago/Turabian StyleJulien Radoux; Axel Bourdouxhe; William Coos; Marc Dufrêne; Pierre Defourny. 2019. "Improving Ecotope Segmentation by Combining Topographic and Spectral Data." Remote Sensing 11, no. 3: 354.
This study evaluates the potential of high resolution hyperspectral airborne imagery to capture within-field variability of durum wheat grain yield (GY) and grain protein content (GPC) in two commercial fields in the Yaqui Valley (northwestern Mexico). Through a weekly/biweekly airborne flight campaign, we acquired 10 mosaics with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400–850 nanometres (nm). Just before harvest, 114 georeferenced grain samples were obtained manually. Using spectral exploratory analysis, we calculated narrow-band physiological spectral indices—normalized difference spectral index (NDSI) and ratio spectral index (RSI)—from every single hyperspectral mosaic using complete two by two combinations of wavelengths. We applied two methods for the multi-temporal hyperspectral exploratory analysis: (a) Temporal Principal Component Analysis (tPCA) on wavelengths across all images and (b) the integration of vegetation indices over time based on area under the curve (AUC) calculations. For GY, the best R2 (0.32) were found using both the spectral (NDSI—Ri, 750 to 840 nm and Rj, ±720–736 nm) and the multi-temporal AUC exploratory analysis (EVI and OSAVI through AUC) methods. For GPC, all exploratory analysis methods tested revealed (a) a low to very low coefficient of determination (R2 ≤ 0.21), (b) a relatively low overall prediction error (RMSE: 0.45–0.49%), compared to results from other literature studies, and (c) that the spectral exploratory analysis approach is slightly better than the multi-temporal approaches, with early season NDSI of 700 with 574 nm and late season NDSI of 707 with 523 nm as the best indicators. Using residual maps from the regression analyses of NDSIs and GPC, we visualized GPC within-field variability and showed that up to 75% of the field area could be mapped with relatively good predictability (residual class: −0.25 to 0.25%), therefore showing the potential of remote sensing imagery to capture the within-field variation of GPC under conventional agricultural practices.
Jr. Francelino A. Rodrigues; Gerald Blasch; Pierre BlasDefournych; J. Ivan Ortiz-Monasterio; Urs Schulthess; Pablo J. Zarco-Tejada; James A. Taylor; Bruno Gérard. Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content. Remote Sensing 2018, 10, 930 .
AMA StyleJr. Francelino A. Rodrigues, Gerald Blasch, Pierre BlasDefournych, J. Ivan Ortiz-Monasterio, Urs Schulthess, Pablo J. Zarco-Tejada, James A. Taylor, Bruno Gérard. Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content. Remote Sensing. 2018; 10 (6):930.
Chicago/Turabian StyleJr. Francelino A. Rodrigues; Gerald Blasch; Pierre BlasDefournych; J. Ivan Ortiz-Monasterio; Urs Schulthess; Pablo J. Zarco-Tejada; James A. Taylor; Bruno Gérard. 2018. "Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content." Remote Sensing 10, no. 6: 930.
In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi- and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors’ publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors’ applications with Sentinel-2’s and (ii) analyzes the applications’ requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.
Julie Transon; Raphaël D’Andrimont; Alexandre Maugnard; Pierre Defourny. Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context. Remote Sensing 2018, 10, 157 .
AMA StyleJulie Transon, Raphaël D’Andrimont, Alexandre Maugnard, Pierre Defourny. Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context. Remote Sensing. 2018; 10 (3):157.
Chicago/Turabian StyleJulie Transon; Raphaël D’Andrimont; Alexandre Maugnard; Pierre Defourny. 2018. "Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context." Remote Sensing 10, no. 3: 157.
Francois Waldner; Pierre Defourny. Where can pixel counting area estimates meet user-defined accuracy requirements? International Journal of Applied Earth Observation and Geoinformation 2017, 60, 1 -10.
AMA StyleFrancois Waldner, Pierre Defourny. Where can pixel counting area estimates meet user-defined accuracy requirements? International Journal of Applied Earth Observation and Geoinformation. 2017; 60 ():1-10.
Chicago/Turabian StyleFrancois Waldner; Pierre Defourny. 2017. "Where can pixel counting area estimates meet user-defined accuracy requirements?" International Journal of Applied Earth Observation and Geoinformation 60, no. : 1-10.
Recent advances in remote sensing technologies and the cost reduction of surveying, along with the importance of natural resources management, present new opportunities for mapping land cover at a very high resolution over large areas. This paper proposes and applies a framework to update hyperspatial resolution (<1 m) land thematic mapping over large areas by handling multi-source and heterogeneous data. This framework deals with heterogeneity both from observation and the targeted features. First, observation diversity comes from the different platform and sensor types (25-cm passive optical and 1-m LiDAR) as well as the different instruments (three cameras and two LiDARs) used in heterogeneous observation conditions (date, time, and sun angle). Second, the local heterogeneity of the targeted features results from their within-type diversity and neighborhood effects. This framework is applied to surface water bodies in the southern part of Belgium (17,000 km2). This makes it possible to handle both observation and landscape contextual heterogeneity by mapping observation conditions, stratifying spatially and applying ad hoc classification procedures. The proposed framework detects 83% of the water bodies—if swimming pools are not taken into account—and more than 98% of those water bodies greater than 100 m2, with an edge accuracy below 1 m over large areas.
Raphaël D’Andrimont; Catherine Marlier; Pierre Defourny. Hyperspatial and Multi-Source Water Body Mapping: A Framework to Handle Heterogeneities from Observations and Targets over Large Areas. Remote Sensing 2017, 9, 211 .
AMA StyleRaphaël D’Andrimont, Catherine Marlier, Pierre Defourny. Hyperspatial and Multi-Source Water Body Mapping: A Framework to Handle Heterogeneities from Observations and Targets over Large Areas. Remote Sensing. 2017; 9 (3):211.
Chicago/Turabian StyleRaphaël D’Andrimont; Catherine Marlier; Pierre Defourny. 2017. "Hyperspatial and Multi-Source Water Body Mapping: A Framework to Handle Heterogeneities from Observations and Targets over Large Areas." Remote Sensing 9, no. 3: 211.
Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90∘N/90∘S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000–2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between 98% and 100%. The CCI global map of open water bodies provided the best water class representation (F-score of 89%) compared to its constitutive inputs. When focusing on the challenging areas for water bodies’ mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between 74% and 89%. The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km2 ± 0.24 million km2. The dataset is freely available through the ESA CCI Land Cover viewer.
Céline Lamarche; Maurizio Santoro; Sophie Bontemps; Raphaël D’Andrimont; Julien Radoux; Laura Giustarini; Carsten Brockmann; Jan Wevers; Pierre Defourny; Olivier Arino. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sensing 2017, 9, 36 .
AMA StyleCéline Lamarche, Maurizio Santoro, Sophie Bontemps, Raphaël D’Andrimont, Julien Radoux, Laura Giustarini, Carsten Brockmann, Jan Wevers, Pierre Defourny, Olivier Arino. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sensing. 2017; 9 (1):36.
Chicago/Turabian StyleCéline Lamarche; Maurizio Santoro; Sophie Bontemps; Raphaël D’Andrimont; Julien Radoux; Laura Giustarini; Carsten Brockmann; Jan Wevers; Pierre Defourny; Olivier Arino. 2017. "Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community." Remote Sensing 9, no. 1: 36.
Changes in the snow cover extent are both a cause and a consequence of climate change. Optical remote sensing with heliosynchronous satellites currently provides snow cover data at high spatial resolution with daily revisiting time. However, high latitude image acquisition is limited because reflective sensors of many satellites are switched off at high solar zenith angles (SZA) due to lower signal quality. In this study, the relevance and reliability of high SZA acquisition are objectively quantified in the purpose of high latitude snow cover detection, thanks to the PROBA-V (Project for On-Board Autonomy-Vegetation) satellite. A snow cover extent classification based on Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI) has been performed for the northern hemisphere on latitudes between 55°N and 75°N during the 2015–2016 winter season. A stratified probabilistic sampling was used to estimate the classification accuracy. The latter has been evaluated among eight SZA intervals to determine the maximum usable angle. The global overall snow classification accuracy with PROBA-V, 82% ± 4%, was significantly larger than the MODIS (Moderate-resolution Imaging Spectroradiometer) snow cover extent product (75% ± 4%). User and producer accuracy of snow are above standards and overall accuracy is stable until 88.5° SZA. These results demonstrate that optical remote sensing data can still be used with large SZA. Considering the relevance of snow cover mapping for ecology and climatology, the data acquisition at high solar zenith angles should be continued by PROBA-V.
Florent Hawotte; Julien Radoux; Guillaume Chomé; Pierre Defourny. Assessment of Automated Snow Cover Detection at High Solar Zenith Angles with PROBA-V. Remote Sensing 2016, 8, 699 .
AMA StyleFlorent Hawotte, Julien Radoux, Guillaume Chomé, Pierre Defourny. Assessment of Automated Snow Cover Detection at High Solar Zenith Angles with PROBA-V. Remote Sensing. 2016; 8 (9):699.
Chicago/Turabian StyleFlorent Hawotte; Julien Radoux; Guillaume Chomé; Pierre Defourny. 2016. "Assessment of Automated Snow Cover Detection at High Solar Zenith Angles with PROBA-V." Remote Sensing 8, no. 9: 699.
Soil fertility in smallholder farming areas is known to vary strongly on multiple scales. This study measures the sensitivity of the recorded satellite signal to on-farm soil fertility treatments applied to five crop types, and quantifies this fertilization effect with respect to within-field variation, between-field variation and field position in the catena. Plant growth was assessed in 5–6 plots per field in 48 fields located in the Sudano-Sahelian agro-ecological zone of southeastern Mali. A unique series of Very High Resolution (VHR) satellite and Unmanned Aerial Vehicle (UAV) images were used to calculate the Normalized Difference Vegetation Index (NDVI). In this experiment, for half of the fields at least 50% of the NDVI variance within a field was due to fertilization. Moreover, the sensitivity of NDVI to fertilizer application was crop-dependent and varied through the season, with optima at the end of August for peanut and cotton and early October for sorghum and maize. The influence of fertilizer on NDVI was comparatively small at the landscape scale (up to 35% of total variation), relative to the influence of other components of variation such as field management and catena position. The NDVI response could only partially be benchmarked against a fertilization reference within the field. We conclude that comparisons of the spatial and temporal responses of NDVI, with respect to fertilization and crop management, requires a stratification of soil catena-related crop growth conditions at the landscape scale.
Xavier Blaes; Guillaume Chomé; Marie-Julie Lambert; Pierre Sibiry Traoré; Antonius G. T. Schut; Pierre Defourny. Quantifying Fertilizer Application Response Variability with VHR Satellite NDVI Time Series in a Rainfed Smallholder Cropping System of Mali. Remote Sensing 2016, 8, 531 .
AMA StyleXavier Blaes, Guillaume Chomé, Marie-Julie Lambert, Pierre Sibiry Traoré, Antonius G. T. Schut, Pierre Defourny. Quantifying Fertilizer Application Response Variability with VHR Satellite NDVI Time Series in a Rainfed Smallholder Cropping System of Mali. Remote Sensing. 2016; 8 (6):531.
Chicago/Turabian StyleXavier Blaes; Guillaume Chomé; Marie-Julie Lambert; Pierre Sibiry Traoré; Antonius G. T. Schut; Pierre Defourny. 2016. "Quantifying Fertilizer Application Response Variability with VHR Satellite NDVI Time Series in a Rainfed Smallholder Cropping System of Mali." Remote Sensing 8, no. 6: 531.
Land cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor’s resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications.
Julien Radoux; Guillaume Chomé; Damien Christophe Jacques; François Waldner; Nicolas Bellemans; Nicolas Matton; Céline Lamarche; Raphaël D’Andrimont; Pierre Defourny. Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection. Remote Sensing 2016, 8, 488 .
AMA StyleJulien Radoux, Guillaume Chomé, Damien Christophe Jacques, François Waldner, Nicolas Bellemans, Nicolas Matton, Céline Lamarche, Raphaël D’Andrimont, Pierre Defourny. Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection. Remote Sensing. 2016; 8 (6):488.
Chicago/Turabian StyleJulien Radoux; Guillaume Chomé; Damien Christophe Jacques; François Waldner; Nicolas Bellemans; Nicolas Matton; Céline Lamarche; Raphaël D’Andrimont; Pierre Defourny. 2016. "Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection." Remote Sensing 8, no. 6: 488.
Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products.
François Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data 2016, 1, 3 .
AMA StyleFrançois Waldner, Steffen Fritz, Antonio Di Gregorio, Dmitry Plotnikov, Sergey Bartalev, Nataliia Kussul, Peng Gong, Prasad Thenkabail, Gerard Hazeu, Igor Klein, Fabian Löw, Jukka Miettinen, Vinay Kumar Dadhwal, Céline Lamarche, Sophie Bontemps, Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data. 2016; 1 (1):3.
Chicago/Turabian StyleFrançois Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. 2016. "A Unified Cropland Layer at 250 m for Global Agriculture Monitoring." Data 1, no. 1: 3.
Early warning systems for food security require accurate and up-to-date information on the location of major crops in order to prevent hazards. A recent systematic analysis of existing cropland maps identified priority areas for cropland mapping and highlighted a major need for the Sahelian and Sudanian agrosystems. This paper proposes a knowledge-based approach to map cropland in the Sahelian and Sudanian agrosystems that benefits from the 100-m spatial resolution of the recent PROBA-V sensor. The methodology uses five temporal features characterizing crop development throughout the vegetative season to optimize cropland discrimination. A feature importance analysis validates the efficiency of using a diversity of temporal features. The fully-automated method offers the first cropland map at 100-m using the PROBA-V sensor with an overall accuracy of 84% and an F-score for the cropland class of 74%. The improvements observed compared to existing cropland products are related to the hectometric resolution, to the methodology and to the quality of the labeling layer from which reliable training samples were automatically extracted. Classification errors are mainly explained by data availability and landscape fragmentation. Further improvements are expected with the upcoming enhanced cloud screening of the PROBA-V sensor.
Marie-Julie Lambert; François Waldner; Pierre Defourny. Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m. Remote Sensing 2016, 8, 232 .
AMA StyleMarie-Julie Lambert, François Waldner, Pierre Defourny. Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m. Remote Sensing. 2016; 8 (3):232.
Chicago/Turabian StyleMarie-Julie Lambert; François Waldner; Pierre Defourny. 2016. "Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m." Remote Sensing 8, no. 3: 232.
The exploitation of new high revisit frequency satellite observations is an important opportunity for agricultural applications. The Sentinel-2 for Agriculture project S2Agri (http://www.esa-sen2agri.org/SitePages/Home.aspx) is designed to develop, demonstrate and facilitate the Sentinel-2 time series contribution to the satellite EO component of agriculture monitoring for many agricultural systems across the globe. In the framework of this project, this article studies the construction of a dynamic cropland mask. This mask consists of a binary “annual-cropland/no-annual-cropland” map produced several times during the season to serve as a mask for monitoring crop growing conditions over the growing season. The construction of the mask relies on two classical pattern recognition techniques: feature extraction and classification. One pixel- and two object-based strategies are proposed and compared. A set of 12 test sites are used to benchmark the methods and algorithms with regard to the diversity of the agro-ecological context, landscape patterns, agricultural practices and actual satellite observation conditions. The classification results yield promising accuracies of around 90% at the end of the agricultural season. Efforts will be made to transition this research into operational products once Sentinel-2 data become available.
Silvia Valero; David Morin; Jordi Inglada; Guadalupe Sepulcre; Marcela Arias; Olivier Hagolle; Gérard Dedieu; Sophie Bontemps; Pierre Defourny; Benjamin Koetz. Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sensing 2016, 8, 55 .
AMA StyleSilvia Valero, David Morin, Jordi Inglada, Guadalupe Sepulcre, Marcela Arias, Olivier Hagolle, Gérard Dedieu, Sophie Bontemps, Pierre Defourny, Benjamin Koetz. Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sensing. 2016; 8 (1):55.
Chicago/Turabian StyleSilvia Valero; David Morin; Jordi Inglada; Guadalupe Sepulcre; Marcela Arias; Olivier Hagolle; Gérard Dedieu; Sophie Bontemps; Pierre Defourny; Benjamin Koetz. 2016. "Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions." Remote Sensing 8, no. 1: 55.
In order to monitor crop growth along the season with synthetic aperture radar (SAR) images, radiative transfer models were developed to retrieve key biophysical parameters, such as the Leaf Area Index (LAI). The semi-empirical water cloud model (WCM) can be used to estimate LAI values from SAR data and surface soil moisture information. Nevertheless, instability problems can occur during the model calibration, which subsequently reduce its transferability in both time and space. To avoid these ill-posed cases, three calibration methodologies are benchmarked in the present study. The accuracy of the retrieved LAI values for each methodology was analyzed, as well as the sensitivity of the signal to LAI for different soil moisture values. The sensitivity of the cross-polarization was highlighted especially for high LAI. The VV polarization was found sensitive for LAI values inferior to 2 m 2 /m 2 . Given the differential sensitivity of the C-band backscatter to maize canopies in each polarization, a Bayesian fusion of the LAI estimates in linear polarizations was developed. This fusion gives lower weights to estimates with a high uncertainty. This method systematically reduces the error and its associated variance. When considering all polarizations, the RMSE on LAI estimation decreased by 0.32 m 2 /m 2 , i.e., one fourth of the error value, as compared to the best estimation from a single polarization, and the associated uncertainty was reduced by a factor of two. Focusing on the two most sensitive polarizations to maize canopies (VV-HV), the error diminished by a third. This fusion framework shows thus a great potential to improve the accuracy and reliability of LAI retrieval of C-band quad-polarized data, as well as dual-polarized data, such as Sentinel-1.
Emilie Bériaux; François Waldner; François Collienne; Patrick Bogaert; Pierre Defourny. Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model. Remote Sensing 2015, 7, 16204 -16225.
AMA StyleEmilie Bériaux, François Waldner, François Collienne, Patrick Bogaert, Pierre Defourny. Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model. Remote Sensing. 2015; 7 (12):16204-16225.
Chicago/Turabian StyleEmilie Bériaux; François Waldner; François Collienne; Patrick Bogaert; Pierre Defourny. 2015. "Maize Leaf Area Index Retrieval from Synthetic Quad Pol SAR Time Series Using the Water Cloud Model." Remote Sensing 7, no. 12: 16204-16225.
Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10–20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the “Sentinel2 for Agriculture” project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of “Sentinel-2 like” time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the “Joint Experiment for Crop Assessment and Monitoring” network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future “Sentinel2 for Agriculture” system.
Sophie Bontemps; Marcela Arias; Cosmin Cara; Gérard Dedieu; Eric Guzzonato; Olivier Hagolle; Jordi Inglada; Nicolas Matton; David Morin; Ramona Popescu; Thierry Rabaute; Mickael Savinaud; Guadalupe Sepulcre; Silvia Valero; Ijaz Ahmad; Agnès Bégué; Bingfang Wu; Diego De Abelleyra; Alhousseine Diarra; Stéphane Dupuy; Andrew French; Ibrar Ul Hassan Akhtar; Nataliia Kussul; Valentine Lebourgeois; Michel Le Page; Terence Newby; Igor Savin; Santiago R. Verón; Benjamin Koetz; Pierre Defourny. Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2. Remote Sensing 2015, 7, 16062 -16090.
AMA StyleSophie Bontemps, Marcela Arias, Cosmin Cara, Gérard Dedieu, Eric Guzzonato, Olivier Hagolle, Jordi Inglada, Nicolas Matton, David Morin, Ramona Popescu, Thierry Rabaute, Mickael Savinaud, Guadalupe Sepulcre, Silvia Valero, Ijaz Ahmad, Agnès Bégué, Bingfang Wu, Diego De Abelleyra, Alhousseine Diarra, Stéphane Dupuy, Andrew French, Ibrar Ul Hassan Akhtar, Nataliia Kussul, Valentine Lebourgeois, Michel Le Page, Terence Newby, Igor Savin, Santiago R. Verón, Benjamin Koetz, Pierre Defourny. Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2. Remote Sensing. 2015; 7 (12):16062-16090.
Chicago/Turabian StyleSophie Bontemps; Marcela Arias; Cosmin Cara; Gérard Dedieu; Eric Guzzonato; Olivier Hagolle; Jordi Inglada; Nicolas Matton; David Morin; Ramona Popescu; Thierry Rabaute; Mickael Savinaud; Guadalupe Sepulcre; Silvia Valero; Ijaz Ahmad; Agnès Bégué; Bingfang Wu; Diego De Abelleyra; Alhousseine Diarra; Stéphane Dupuy; Andrew French; Ibrar Ul Hassan Akhtar; Nataliia Kussul; Valentine Lebourgeois; Michel Le Page; Terence Newby; Igor Savin; Santiago R. Verón; Benjamin Koetz; Pierre Defourny. 2015. "Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2." Remote Sensing 7, no. 12: 16062-16090.