Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring

Rousi, Maria; Sitokonstantinou, Vasileios; Meditskos, Georgios; Papoutsis, Ioannis; Gialampoukidis, Ilias; Koukos, Alkiviadis; Karathanassi, Vassilia; Drivas, Thanassis; Vrochidis, Stefanos; Kontoes, Charalampos; Kompatsiaris, Ioannis

doi:10.1109/jstars.2020.3038152

Cited by 20 publications

(6 citation statements)

References 45 publications

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“…Many approaches rely on optical satellite time series with its strong capability to track vegetation phenology in a comprehensible way. Sentinel-2 data have been used in a number of studies to map agricultural areas worldwide (e.g., [7,8]), in Europe (e.g., [9][10][11][12][13][14][15][16][17][18][19][20]), and in Germany (e.g., [21][22][23][24]) with promising accuracies, sometimes in combination with Landsat or other optical satellite data (e.g., for Europe [25] and for Germany [26][27][28]). A major drawback of optical data, however, is that observations of crucial phenological crop stages can be lost due to cloud cover.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…At the same time there have not been that many applications reported in the literature that demonstrated the use of Semantic Web and Linked Data technologies in linking EO datasets with other geo-information sources. Inspiring examples of such use are related to EO for crop type classification 3 , wildfire monitoring 4 and wildlife modelling 5 .…”

Section: Background 21 Geo-spatial Linked Datamentioning

confidence: 99%

Linking Earth observation data with ground truth from the open web

Koster,

Valk,

Athanasiadis

2023

Image and Signal Processing for Remote Sensing XXIX

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This paper presents a system for linking Earth Observation with open Web data, into a Linked Open Data architecture. The architecture has two components, one for extracting signals from Earth Observation data, and another for harvesting web sources. Both are linked with spatial objects. Web scraped data, either from APIs or crowd-sourced websites are geo-referenced and thematically annotated with standard vocabularies. The architecture has been demonstrated in two case studies, one for building permits and another for crowd-sourced observations of invasive aquatic plants.

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“…Multispectral image processing is one of the essential characteristics of satellite image processing systems. The work in [4,5,6] propose use of multiple sensors & multiple temporal image classification using hybrid CNN (HCNN) model, semantically enriched crop type classification models, and deep learning models for classification of different crop types. These models assist in improving efficiency of crop classification via redundancy reduction, and feature augmentation, which assists in enhancing classification performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of a high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning for crop analysis

Dhande

Malik

2022

Preprint

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Satellite image processing is a multidomain task which involves design of image capturing, denoising, segmentation, feature extraction, feature reduction, classification, and post-processing tasks. A wide variety of satellite image processing models are proposed by researchers, and each of them have different data & process requirements. For instance, image capturing module Might obtain Images in layered form, while feature extraction module might require data in 2D or 3D forms. Moreover, performance of these models also varies due to changes in internal process parameters & dataset parameters, which limits their accuracy and scalability when applied to real-time scenarios. To reduce the probability of these limitations, a novel high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning is proposed & discussed in this text. The model initially captures real-time satellite data using Google’s Earth Engine and processes it using a transfer learning-based convolutional neural network (CNN) via backscatter coefficient analysis. These coefficients indicate average intensity value of Precision Image (PRI) when evaluated over a distributed target. Due to extraction of backscattering coefficients, the model is capable of representing crop images in VV (vertical transmit, vertical receive), and HV (horizontal transmit vertical receive) modes. Thereby assisting the CNN model to extract a wide variety of features from input satellite image, which classifies these datasets (original, VV, and VH) into different crop categories. The classified images are further processed via an incremental learning layer, which assists in visual identification of affected regions. Due to use of incremental learning and CNN for classification, the proposed TRSAITL model is capable of achieving an average accuracy of 97.8% for crop type & severity of damage detection, with an average PSNR of 29.6 dB for different image types. The model was tested on different regions around our local geographical area, and consistent performance was observed. This performance was also compared with various state-of-the-art approaches, and it was observed that the proposed TRSATL model has 5% better accuracy, 4.6% better precision, and 7.9% better recall when compared with them, which makes it highly useful for real-time satellite-based crop classification applications.

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Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring

Cited by 20 publications

References 45 publications

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

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

Linking Earth observation data with ground truth from the open web

Design of a high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning for crop analysis

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