Crop Area Mapping Using 100-m Proba-V Time Series

Durgun, Yetkin Özüm; Gobin, Anne; Kerchove, Ruben Van De; Tychon, Bernard

doi:10.3390/rs8070585

Cited by 28 publications

(27 citation statements)

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“…This demonstrates how the accuracy within the growing season depends strongly on the evolution of the crop's phenological stages. These insights agree with previous research showing that using optical time series which include phenological differences in classification tasks has a positive impact on the performance [14]. The ability of discriminating winter cereals earlier in the season compared to summer crops could in principle be used in future work to discriminate winter cereals early in the growing season, as this might be helpful for some applications.…”

Section: Discussionsupporting

confidence: 88%

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Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

et al. 2018

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A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security and allowing early crop monitoring practices. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here, we used joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical inputs across the country, Sentinel-1 12-day backscatter mosaics were created after incidence angle normalization, and Sentinel-2 normalized difference vegetation index (NDVI) images were smoothed to yield 10-daily cloud-free mosaics. An optimized random forest classifier predicted the eight crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types were largest. Furthermore, we showed that the concept of classification confidence derived from the random forest classifier provided insight into the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.

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Section: Discussionsupporting

confidence: 88%

“…This has started with the Landsat missions [8][9][10]. Gradually, the spatial, temporal, and spectral resolutions have increased, constantly improving the classification results [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

et al. 2018

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“…Apart from typical GIS software (e.g., QGIS) similar results might be obtained from dedicated toolboxes like for example SNAP (successor of BEAM) from ESA or TSPT (Time Series Product Tool) from NASA. The added value of using timeseries of near-daily 100 m Proba-V NDVI estimates for mapping crops was proven successful in Sahelian and Sudanian Agrosystems [26] as well as in South America, Western and Eastern Europe [27]. The successful use of Proba-V NDVI timeseries for Spectral Matching Techniques (SMT) demonstrated in the work of Durgun [27] also showed the application of the well-developed SPIRITS software.…”

Section: Discussionmentioning

confidence: 99%

Automatic Proba-V Processor: TREX—Tool for Raster Data Exploration

et al. 2019

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The processing tool TREX, standing for ‘Tool for Raster data EXploration’ is presented and evaluated in the Biebrza wetlands in northeastern Poland. TREX was designed for the automatization of processing satellite data from the Proba-V satellite into maps of NDVI or LAI in any defined by the user projection, spatial resolution, or extent. The open source and access concept of TREX encourages the potential community of users to collaborate, develop, and integrate the tool with other satellite imagery and models. TREX reprojects, shifts, and resamples original data obtained from the Proba-V satellite to deliver reliable maps of NDVI and LAI. Validation of TREX in Biebrza wetlands resulted in correlations between 0.79 and 0.92 for NDVI data (measured with ASD Field Spec 4) and 0.92 for LAI data (measured with LiCOR—LAI-2000 Plant Canopy Analyzer).

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“…The sensor on-board PROBA-V generates products at three different spatial resolutions: 1 km, 300 m and 100 m [16,17]. For agricultural applications, the 100 m product is of particular interest and has been shown useful for cropland and crop type mapping [18][19][20]. These studies used the traditional, hard classification approach where each pixel is assigned to one class.…”

mentioning

confidence: 99%

“…These studies used the traditional, hard classification approach where each pixel is assigned to one class. However, even with the improved spatial resolution of 100 m, the presence of mixed pixels may be a problem in agro-systems dominated by small fields [20]. It is thus meaningful to test the feasibility of the sub-pixel classification approach with the new PROBA-V 100 m data.…”

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confidence: 99%

Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications

et al. 2019

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This paper presents the results of a sub-pixel classification of crop types in Bulgaria from PROBA-V 100 m normalized difference vegetation index (NDVI) time series. Two sub-pixel classification methods, artificial neural network (ANN) and support vector regression (SVR) were used where the output was a set of area fraction images (AFIs) at 100 m resolution with pixels containing estimated area fractions of each class. High-resolution maps of two test sites derived from Sentinel-2 classifications were used to obtain training data for the sub-pixel classifications. The estimated area fractions have a good correspondence with the true area fractions when aggregated to regions of 10 × 10 km 2 , especially when the SVR method was used. For the five dominant classes in the test sites the R 2 obtained after the aggregation was 86% (winter cereals), 81% (sunflower), 92% (broad-leaved forest), 89% (maize), and 67% (grasslands) when the SVR method was used.including the linear mixture model (LMM) [4], artificial neural network (ANN) [9-11], regression tree [5], fuzzy classification [6,12], and support vector machine (SVM) [13].Liu and Wu [11] argued that the non-linear models, especially neural network-based models, outperformed the traditional linear unmixing models. Support for such a conclusion is given in Verbeiren et al.[1] who compared ANN and LMM in an attempt to generate a sub-pixel map from SPOT-VEGETATION 1 km data across Belgium. The authors showed that the ANN approach outperformed LMM and that, for the major classes, the acreage estimates obtained via ANN, when aggregated to the level of the administrative regions, were in good agreement with the true values. Also, a multilayer perceptron (MLP) neural network regression algorithm has been shown to outperform the regression tree algorithm [5]. Atkinson et al. [9] also obtained better results with ANN than with the other tested methods but pointed out that its successful implementation depends on accurate co-registration and the availability of a training data set. Liu et al.[14] compared a linear spectral unmixing model, a supervised fully-fuzzy classification method and a SVM to generate a fraction map and achieved the most accurate fraction result using SVM. Six machine learning methods were compared in a recent study [15] based on multiple criteria, where the authors found that, in general, no method performs best for all evaluation criteria. However, when both time available for preprocessing and the magnitude of the training data set are unconstrained, support vector regression (SVR) and least-squares SVM for regression clearly outperform the other methods.Regarding the satellite imagery widely used for agricultural monitoring, SPOT-VEGETATION (SPOT-VGT) sensors provided one of the longest time series of multispectral reflectance since 1998. The mission was succeeded in 2013 by PROBA-V (PRoject for On-Board Autonomy-Vegetation), a small satellite commissioned by the European Space Agency. The sensor on-board PROBA-V generates products at three different ...

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Crop Area Mapping Using 100-m Proba-V Time Series

Cited by 28 publications

References 40 publications

Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

Automatic Proba-V Processor: TREX—Tool for Raster Data Exploration

Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications

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