Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data

Asam, Sarah; Geßner, Ursula; González, Roger Almengor; Wenzl, Martina; Kriese, Jennifer; Kuenzer, Claudia

doi:10.3390/rs14132981

Cited by 34 publications

(27 citation statements)

References 77 publications

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“…The mapped areas of the crop classes in this study area, overall, do well with OSS data at the provincial level, with on average 1% deviation in coverage resulting with L8 sensor datasets using RF-EVI and SVM-EVI-NDVI methods, as shown in Table 7 . A similar result was also found in the work of Asam et al, 2022 [ 58 ]. The deviation between 1% to 13% resulted from the EVI and NDVI used by all ML classifiers and sensor datasets.…”

Section: Discussionsupporting

confidence: 90%

“…Cropland areas derived from this study were compared with OSS data at the provincial level. The other studies also compared calculated croplands area with OSS data at national and regional levels [ 27 , 56 , 57 , 58 ]. In Figure 16 , a comparison of the total area classified for the land use categories of cotton, wheat, rice, and other crops is displayed by the sensor, classifier, and index and compared to the equivalent figures from OSS.…”

Section: Discussionmentioning

confidence: 99%

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Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

Erdanaev

Kappas

Wyss

2022

Sensors

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Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices “Normalized Difference Vegetation Index” (NDVI), “Enhanced Vegetation Index” (EVI), and “Normalized Difference Water Index” (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

Erdanaev

Kappas

Wyss

2022

Sensors

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“…Together with the availability of these data, the evolution of technology regarding the computational power and the arising of new big data processing techniques leveraged the production of detailed land cover maps 1,5,6 . In particular, mapping crop areas for larger areas, for example at national scale, have become feasible 7 . The Sen2Agri is a good example of the application of automated methodologies with multi temporal satellite data for monitoring agriculture crops at an operational level 8 .…”

Section: Introductionmentioning

confidence: 99%

Mapping annual crops in Portugal with Sentinel-2 data

Benevides

Costa²,

Moreira³

et al. 2022

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV

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This paper presents an annual crop classification exercise considering the entire area of continental Portugal for the 2020 agricultural year. The territory was divided into landscape units, i.e. areas of similar landscape characteristics for independent training and classification. Data from the Portuguese Land Parcel Identification System (LPIS) was used for training. Thirty-one annual crops were identified for classification. Supervised classification was undertaken using Random Forest. A time-series of Sentinel-2 images was gathered and prepared. Automatic processes were applied to auxiliary datasets to improve the training data quality and lower class mislabelling. Automatic random extraction was employed to derive a large amount of sampling units for each annual crop class in each landscape unit. An LPIS dataset of controlled parcels was used for results validation. An overall accuracy of 85% is obtained for the map at national level indicating that the methodology is useful to identify and characterize most of annual crop types in Portugal. Class aggregation of the annual crop types by two types of growing season, autumn/winter and spring/summer, resulted in large improvements in the accuracy of almost all annual crops, and an overall accuracy improvement of 2%. This experiment shows that LPIS dataset can be used for training a supervised classifier based on machine learning with highresolution remote sensing optical data, to produce a reliable crop map at national level.

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“…GPC out-performed widely used MLCAs such as RF, support vector regression, or neural networks. Each of these classifiers were evaluated as top-performing against other common classifiers in earlier studies [ 30 , 90 , 91 ]. The outstanding accuracy reached by GPC is remarkable; as in this study, we did not aim for detecting the usual thematic land covers with distinct spectral behaviors (e.g., water, land, vegetation).…”

Section: Discussionmentioning

confidence: 99%

Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

et al. 2022

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Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO’s software framework developed in Matlab which implements most of the state-of-the-art methods in the machine learning community. To demonstrate its utility, we chose a heterogeneous case study scene, a landscape in Southwest Iran to map PTs. In this area, four main PTs were identified, consisting of shrub land, grass land, semi-shrub land, and shrub land–grass land vegetation. Having developed 21 MLCAs using the same training and validation, datasets led to varying accuracy results. Gaussian process classifier (GPC) was validated as the top-performing classifier, with an overall accuracy (OA) of 90%. GPC follows a Laplace approximation to the Gaussian likelihood under the supervised classification framework, emerging as a very competitive alternative to common MLCAs. Random forests resulted in the second-best performance with an OA of 86%. Two other types of ensemble-learning algorithms, i.e., tree-ensemble learning (bagging) and decision tree (with error-correcting output codes), yielded an OA of 83% and 82%, respectively. Following, thirteen classifiers reported OA between 70% and 80%, and the remaining four classifiers reported an OA below 70%. We conclude that GPC substantially outperformed all classifiers, and thus, provides enormous potential for the classification of a diversity of land-cover types. In addition, its probabilistic formulation provides valuable band ranking information, as well as associated predictive variance at a pixel level. Nevertheless, as these are supervised (data-driven) classifiers, performances depend on the entered training data, meaning that an assessment of all MLCAs is crucial for any application. Our analysis demonstrated the efficacy of ARTMO’s MLCA toolbox for an automated evaluation of the classifiers and subsequent thematic mapping.

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Mapping Crop Types of Germany by Combining Temporal Statistical Metrics of Sentinel-1 and Sentinel-2 Time Series with LPIS Data

Cited by 34 publications

References 77 publications

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods

Mapping annual crops in Portugal with Sentinel-2 data

Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

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