Large-scale winter catch crop monitoring with Sentinel-2 time series and machine learning–An alternative to on-site controls?

Schulz, Christian; Holtgrave, Ann-Kathrin; Kleinschmit, Birgit

doi:10.1016/j.compag.2021.106173

Cited by 18 publications

(11 citation statements)

References 39 publications

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“…Remote sensing images have become an essential tool for many agricultural applications, including precision farming [60], primarily because they can be used to provide valuable information about vegetation without a need for on-site visits [50]. In recent years, the amount of freely accessible remote sensed images has drastically increased, especially thanks to the Copernicus mission operated by the European Space Agency (ESA).…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Sentinel-2 Time Series Using Robust Gaussian Mixture Models -- Application to the Detection of Anomalous Crop Development in wheat and rapeseed crops

Mouret,

Albughdadi,

Duthoit

et al. 2021

Preprint

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Missing data is a recurrent problem in remote sensing, mainly due to cloud coverage for multispectral images and acquisition problems. This can be a critical issue for crop monitoring, especially for applications relying on machine learning techniques, which generally assume that the feature matrix does not have missing values. This paper proposes a Gaussian Mixture Model (GMM) for the reconstruction of parcel-level features extracted from multispectral images. A robust version of the GMM is also investigated, since datasets can be contaminated by inaccurate samples or features (e.g., wrong crop type reported, inaccurate boundaries, undetected clouds, etc). Additional features extracted from Synthetic Aperture Radar (SAR) images using Sentinel-1 data are also used to provide complementary information and improve the imputations. The robust GMM investigated in this work assigns reduced weights to the outliers during the estimation of the GMM parameters, which improves the final reconstruction. These weights are computed at each step of an Expectation-Maximization (EM) algorithm by using outlier scores provided by the isolation forest (IF) algorithm. Experimental validation is conducted on rapeseed and wheat parcels located in the Beauce region (France). Overall, we show that the GMM imputation method outperforms other reconstruction strategies. A mean absolute error (MAE) of 0.013 (resp. 0.019) is obtained for the imputation of the median Normalized Difference Index (NDVI) of the rapeseed (resp. wheat) parcels. Other indicators (e.g., Normalized Difference Water Index) and statistics (for instance the interquartile range, which captures heterogeneity among the parcel indicator) are reconstructed at the same time with good accuracy. In a dataset contaminated by irrelevant samples, using the robust GMM is recommended since the standard GMM imputation can lead to inaccurate imputed values. An application to the monitoring of anomalous crop development in the presence of missing data is finally considered. In this application, using the proposed method leads to the best detection results, especially when SAR data are used jointly with multispectral images. Exploiting the information contained in cloudy multispectral images instead of removing these images is beneficial for this application.

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Section: Introductionmentioning

confidence: 99%

Reconstruction of Sentinel-2 Time Series Using Robust Gaussian Mixture Models -- Application to the Detection of Anomalous Crop Development in wheat and rapeseed crops

Mouret,

Albughdadi,

Duthoit

et al. 2021

Preprint

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“…24 In relation to the agricultural context, the additional class of catch crops would be included at this point. [24][25][26] For this land cover on arable land, the covered investigation period would not have to be extended.…”

Section: Discussionmentioning

confidence: 99%

“…23 For instance, there were studies on monitoring (annual) catch crops 24,25 or own web-based applications for detecting catch crops as an alternative to on site controls. 26 The winter land use types studied by Denize et al 24 are winter crops, grasslands, catch crops, crop residues, and bare soil. Here, stubble fields were missing because "crop residues" is used as a broader term.…”

Section: Introductionmentioning

confidence: 99%

Mapping overwintering grain stubbles using machine-learning methods and image compositions for common agriculture policy-control and water framework directive connected activities

Goihl

2023

J. Appl. Rem. Sens.

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By leaving grain stubble on the field over the winter, farmers can actively contribute to nature conservation. Animals receive food and living space in these areas, endangered herbs too, but at the same time, it is reducing the input of substances into water and function as covered ground to protect the fertile soil from erosion. Remote sensing can deliver information on this type of land cover, which is also important for the agricultural administration. The farmer can be compensated by the government to let stubbles on the fields. The results of this study show that there is a high potential to detect overwintering stubbles fields area for common agriculture policy (CAP)-control to verify agricultural funding with Support Vector Machine (96.5%) by combining remote sensing classification methods with geodata analysis techniques using geoinformation systems (GIS), negative buffering (-20 m), and using threshold settings for correct classified pixel per field. These results give occasion for optimism for the further processing of this land cover by using it as an application for the government to support CAP-and water framework directive-activities.

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“…For example, Adriaan et al used remote-sensing datasets in combination with 10 types of machine-learning methods to extract the spatial distributions of crops, analyzed the various accuracy indicators of the classification results, and found that RF provided the highest accuracy [14]. Based on RF, Schulz et al proposed a new classification method to carry out large-scale agricultural monitoring of fishing crops and obtained an average prediction accuracy of 84% [15]. The introduction of machine learning has made the launching of remote-sensing research much more convenient.…”

Section: Introductionmentioning

confidence: 99%

Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

Zhang²,

Luo

et al. 2022

Remote Sensing

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In recent years, the scale of rural land transfer has gradually expanded, and the phenomenon of non-grain-oriented cultivated land has emerged. Obtaining crop planting information is of the utmost importance to guaranteeing national food security; however, the acquisition of the spatial distribution of crops in large-scale areas often has the disadvantages of excessive calculation and low accuracy. Therefore, the IO-Growth method, which takes the growth stage every 10 days as the index and combines the spectral features of crops to refine the effective interval of conventional wavebands for object-oriented classification, was proposed. The results were as follows: (1) the IO-Growth method obtained classification results with an overall accuracy and F1 score of 0.92, and both values increased by 6.98% compared to the method applied without growth stages; (2) the IO-Growth method reduced 288 features to only 5 features, namely Sentinel-2: Red Edge1, normalized difference vegetation index, Red, short-wave infrared2, and Aerosols, on the 261st to 270th days, which greatly improved the utilization rate of the wavebands; (3) the rise of geographic data processing platforms makes it simple to complete computations with massive data in a short time. The results showed that the IO-Growth method is suitable for large-scale vegetation mapping.

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Large-scale winter catch crop monitoring with Sentinel-2 time series and machine learning–An alternative to on-site controls?

Cited by 18 publications

References 39 publications

Reconstruction of Sentinel-2 Time Series Using Robust Gaussian Mixture Models -- Application to the Detection of Anomalous Crop Development in wheat and rapeseed crops

Reconstruction of Sentinel-2 Time Series Using Robust Gaussian Mixture Models -- Application to the Detection of Anomalous Crop Development in wheat and rapeseed crops

Mapping overwintering grain stubbles using machine-learning methods and image compositions for common agriculture policy-control and water framework directive connected activities

Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

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