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

By Jordi Inglada, Marcela Arias, Benjamin Tardy, O. Hagolle, Silvia Valero, David Morin, Gérard Dedieu, G. Sepulcre, S. Bontemps, Pierre Defourny and 1 more. Remote Sensing, 2015

360 citations recorded by the source corpus.

Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the map production is fully automatic.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/4e92a53db8762a9d851ca172e9c98977cec39aef

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Citations in the corpus

Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series (2017, methodology): “The same approach used in [7], which consists of a linear interpolation of the invalid pixels using the previous and following cloud-free dates, was used here.”
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas (2016, methodology): “It accomplished performances comparable to traditional classifiers such as DT or SVM, with a lower computational time (Inglada et al., 2015; Rodriguez-Galiano et al., 2012; Gislason et al., 2006).”
Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review (2020, background): “However, this depends on the data availability and may fail when images of key crop developmental stages of the crops are not available [34,139].”
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series (2018, methodology): “They can help the classifier to handle some nonlinear relationships between the spectral bands [4] More specifically, we compute three commonly used indexes: the Normalized Difference Vegetation Index (NDVI) [60], the Normalized Difference Water Index (NDWI) [61] and a brilliance index (IB) defined ”