Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Nguyen, Minh Duc; Baez-Villanueva, Oscar M.; Bui, Duong Du; Nguyen, Phong Tung; Ribbe, Lars

doi:10.3390/rs12020281

Cited by 60 publications

(23 citation statements)

References 47 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike other similar studies relying on third-party software [75,76,[116][117][118][119][120][121], we performed the entire workflow process inside the GEE cloud platform and used the JavaScript language to recall variables and functions in the code editor. This is a point of novelty of our research in this field.…”

Section: Discussionmentioning

confidence: 99%

“…The former need to be atmospherically corrected, in order to obtain useful reflectance values. This correction cannot be carried out in the GEE JavaScript-based environment, but requires third-party applications or Python API [74][75][76]. To adhere to the research objectives, we used level 2A images, which are already atmospherically corrected.…”

Section: Image Pre-processing 221 Satellite Data Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

et al. 2021

View full text Add to dashboard Cite

The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Image Pre-processing 221 Satellite Data Selectionmentioning

confidence: 99%

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…A time-series of 53 days are available between 1 January 2019 and 27 December 2019, with zero cloud coverage in the area, formed by the T30SXH tile and orbit number 051 found in the EPSG:32630 reference system [34]. The product type is S2MSI2A, whose main characteristics are processing level 2A, ortho-rectified and Universal Transverse Mercator (UTM) geocoded, BOA [35], and multi-spectral reflectance [36].…”

Section: Remote Sensing Datamentioning

confidence: 99%

Sentinel-2 Images and Machine Learning as Tool for Monitoring of the Common Agricultural Policy: Calasparra Rice as a Case Study

López-Andreu¹,

Erena²,

Domínguez-Gómez³

et al. 2021

Agronomy

View full text Add to dashboard Cite

The European Commission introduces the Control by Monitoring through new technologies to manage Common Agricultural Policy funds through the Regulation 2018/746. The advances in remote sensing have been considered one of these new technologies, mainly since the European Space Agency designed the Copernicus Programme. The Sentinel-1 (radar range) and Sentinel-2 (optical range) satellites have been designed for monitoring agricultural problems based on the characteristics they provide. The data provided by the Sentinel 2 missions, together with the emergence of different scientific disciplines in artificial intelligence —especially machine learning— offer the perfect basis for identifying and classifying any crop and its phenological state. Our research is based on developing and evaluating a pixel-based supervised classification scheme to produce accurate rice crop mapping in a smallholder agricultural zone in Calasparra, Murcia, Spain. Several models are considered to obtain the most suitable model for each element of the time series used; pixel-based classification is performed and finished with a statistical treatment. The highly accurate results obtained, especially across the most significant vegetative development dates, indicate the benefits of using Sentinel-2 data combined with Machine Learning techniques to identify rice crops. It should be noted that it was possible to locate rice crop areas with an overall accuracy of 94% and standard deviation of 1%, which could be increased to 96% (±1%) if we focus on the months of the crop’s highest development state. Thanks to the proposed methodology, the on-site inspections carried out, 5% of the files, have been replaced by remote sensing evaluations of 100% of the analyzed season files. Besides, by adjusting the model input data, it is possible to detect unproductive or abandoned plots.

show abstract

“…The data acquired by Sentinel satellites constellation is managed by the European Commission and distributed by the European Space Agency (ESA) via the Copernicus Open Access Hub. The images can be accessed and downloaded for free at https://scihub.copernicus.eu/dhus/#/home for regions located within 84 • N and 56 • S. Sentinel-2 images are frequently applied in many fields, particularly for water management and land protection purposes [43], as well as the management of agricultural plots [44], the identification and monitoring of crops [45] and for the risk management of floods [46], forest fires [47], and coastal water contaminant [48]. Additional applications include geological mapping [49] and the monitoring of geological waste [50].…”

Section: Multispectral Sentinel-2a Imagementioning

confidence: 99%

Fusion of Remote Sensing and Applied Geophysics for Sinkholes Identification in Tabular Middle Atlas of Morocco (the Causse of El Hajeb): Impact on the Protection of Water Resource

et al. 2020

View full text Add to dashboard Cite

The Causse of El Hajeb belongs to the Tabular Middle Atlas (TMA), in which thousands of karst landforms have been identified. Among them, collapse dolines and dissolution sinkholes have been highlighted as a source of environmental risks and geo-hazards. In particular, such sinkholes have been linked to the degradation of water quality in water springs located in the junction of the TMA and Saïss basin. Furthermore, the developments of collapse dolines in agricultural and inhabited areas enhance the risk of life loss, injury, and property damage. Here, the lack of research on newly formed cavities has exacerbated the situation. The limited studies using remote sensing or geophysical methods to determine the degree of karstification and vulnerability of this environment fail to provide the spatial extent and depth location of individual karst cavities. In order to contribute to the effort of sinkhole risk reduction in TMA, we employed remote sensing and geophysical surveys to integrate electrical resistivity tomography (ERT) and self-potential (SP) for subsurface characterization of four sinkholes identified in the Causse of El Hajeb. The results revealed the existence of sinkholes, both visible and non-accessible at the surface, in carbonate rocks. The sinkholes exhibited distinct morphologies, with depths reaching 35 m. Topography, geographic coordinates and land cover information extracted on remote sensing data demonstrated that these cavities were developed in depressions in which agricultural activities are regularly performed. The fusion of these methods benefits from remote sensing in geophysical surveys, particularly in acquisition, georeferencing, processing and interpretation of geophysical data. Furthermore, our proposed method allows identification of the protection perimeter required to minimize the risks posed by sinkholes.

show abstract

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Cited by 60 publications

References 47 publications

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

Sentinel-2 Images and Machine Learning as Tool for Monitoring of the Common Agricultural Policy: Calasparra Rice as a Case Study

Fusion of Remote Sensing and Applied Geophysics for Sinkholes Identification in Tabular Middle Atlas of Morocco (the Causse of El Hajeb): Impact on the Protection of Water Resource

Contact Info

Product

Resources

About