Cloud-Based Agricultural Solution: A Case Study of Near Real-Time Regional Agricultural Crop Growth Information in South Africa

Hiestermann, Jens; Ferreira, Stefanus

doi:10.5194/isprs-archives-xlii-3-w2-79-2017

Cited by 5 publications

(7 citation statements)

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“…For example, Segal-Rozenhaimer, et al [75] applied CNN on land cover classification and achieved high classification accuracy of 91%. In addition, cloud-based computing has also contributed to improved land cover/use monitoring because large dataset can be analysed at a fast rate [76,77]. For example, Hiestermann, et al [77] employed cloud-based computing using Google Earth Engine to map crops in South Africa.…”

Section: Land Cover/use Classification With Sentinel-2mentioning

confidence: 99%

“…In addition, cloud-based computing has also contributed to improved land cover/use monitoring because large dataset can be analysed at a fast rate [76,77]. For example, Hiestermann, et al [77] employed cloud-based computing using Google Earth Engine to map crops in South Africa. RF and Maximum likelihood classifiers (MLC) have also been used to produce the high spatial resolution (20 m) land cover map for Africa based on Sentinel-2 data-the CCI Land Cover-S2 prototype [17].…”

Section: Land Cover/use Classification With Sentinel-2mentioning

confidence: 99%

“…These various aspects include forest mapping [126][127][128], carbon assessments [129,130], urban area mapping [131,132] and natural hazards monitoring [41,133,134]. Other vital applications include agricultural [135,136] and water resources monitoring [77,137]. The studies on the implementation of Sentinel-2 in land cover/use monitoring have been reported in many countries across the world, including Europe, Asia, America, and Africa.…”

Section: Specific Applications Of Sentinel-2 In Land Cover/land Use Monitoringmentioning

confidence: 99%

“…Bellemans, et al [181] also reported on a global project (Sentinel-2 for Agriculture) involving African countries, such as Burkina Faso, South Africa, Morocco and Madagascar. The specific applications of Sentinel-2 in agriculture, include crop production monitoring [77,135,182], crop type mapping [183,184], irrigation agriculture monitoring [137], nitrogen content assessment [185] and assessment of crop health [186]. These studies range from small scale monitoring (i.e., field-level) [182,183] to continental level [76].…”

Section: Sentinel-2 For Agricultural Monitoringmentioning

confidence: 99%

“…Due to the high demand for information for agriculture production, Sentinel-2 data has been used for real-time monitoring of agricultural activities [77]. An exciting development in the application of Sentinel-2 in agricultural applications is the increasing use of cloud computing applications (e.g., use of Google Earth Engine) [178].…”

Section: Sentinel-2 For Agricultural Monitoringmentioning

confidence: 99%

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Sentinel-2 Data for Land Cover/Use Mapping: A Review

et al. 2020

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The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

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