Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

Inglada, Jordi; Arias, Marcela; Tardy, Benjamin; Hagolle, Olivier; Valero, Silvia; Morin, David; Dedieu, Gérard; Sepulcre, G.; Bontemps, Sophie; Defourny, Pierre; Koetz, Benjamin

doi:10.3390/rs70912356

Cited by 319 publications

(235 citation statements)

References 41 publications

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“…The state of the art in land cover mapping uses image classification [7]. In the case of global mapping systems using medium to low resolution imagery, as for instance ESA's GlobCover, rule or expert based approaches have been implemented [8].…”

Section: Introductionmentioning

confidence: 99%

Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

et al. 2017

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Abstract:A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic production of land cover maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover maps are provided with a confidence map which gives information at the pixel level about the expected quality of the result.

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Section: Introductionmentioning

confidence: 99%

Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

et al. 2017

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“…Due to the fact that a per-pixel classification approach was adopted at the Landsat scale, a pixel classified as cropland could have a portion of it as natural vegetation [59]. This is particularly the case in the southern part of the Sudanian Savanna region, where the prevalence of sub-canopy cultivation results in many trees on agricultural plots (agroforestry systems) [60].…”

Section: Discussionmentioning

confidence: 99%

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

et al. 2017

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Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with many challenges including financial, logistical and human capacity limitations. This study investigated the use of fractional cover approaches in mapping cropland area in the heterogeneous landscape of West Africa. Discrete cropland areas identified from multi-temporal Landsat data were upscaled to MODIS resolution using random forest regression. Producer's accuracy and user's accuracy of the cropland class in the Landsat scale analysis averaged 95% and 94%, respectively, indicating good separability between crop and non-crop land. Validation of the fractional cropland cover map at MODIS resolution (MODIS_FCM) revealed an overall mean absolute error of 19%. Comparison of MODIS_FCM with the MODIS land cover product (e.g., MODIS_LCP) demonstrate the suitability of the proposed approach to cropped area estimation in smallholder dominant heterogeneous landscapes over existing global solutions. Comparison with official government statistics (i.e., cropped area) revealed variable levels of agreement and partly enormous disagreements, which clearly indicate the need to integrate remote sensing approaches and ground based surveys conducted by agricultural ministries in improving cropped area estimation. The recent availability of a wide range of open access remote sensing data is expected to expedite this integration and contribute missing information urgently required for regional assessments of food security in West Africa and beyond.

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“…A system developed by [14] requires little human interaction to derive vegetative phenological metrics from traffic webcams. Another work, presented in [15], focuses on training and validating satellite data; it, however, requires the user to select reference data to establish an operational system for crop type maps. Last but not least, Clewley et al [16] built a modular system accessed through Python to conduct Geographical Object-Based Image Analysis (GEOBIA) as an open-source package with functionality similar to existing GEOBIA packages.…”

Section: Introductionmentioning

confidence: 99%

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

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Tolpekin

et al. 2017

Remote Sensing

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Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.

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Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

Cited by 319 publications

References 41 publications

Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

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