Evaluation Of Sen2agri System Over Semi-Arid Conditions: A Case Study Of The Haouz Plain In Central Morocco

Moumni, Aicha; Sebbar, Badr-eddine; Simonneaux, Vincent; Ezzahar, Jamal; Lahrouni, A.

doi:10.1109/m2garss47143.2020.9105233

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Remote sensors operate on a variety of basic physical principles, recording the electromagnetic properties of an Earth's surface (i.e., the energy reflected (optical sensors), emitted (passive infrared or microwave thermal sensors), or diffused (active radar sensors)) and, therefore, provide a variety of information on the properties of land cover [1]. e use of optical remote sensing for LC/LU mapping is well established and can be considered effective, yet it exhibits some shortcomings when applied to large scale regions with complex land cover, or where cloud cover is frequent [2][3][4][5]. On the other hand, using SAR data for crop-type discrimination is still facing many challenges, particularly that its recognition accuracy is not high enough [6,7].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

Moumni

Lahrouni

2021

Scientifica

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The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. It is a very heterogeneous zone characterized by mixed crops (tree crops with annual crops, same crop with different phenological states during the same agricultural season, crop rotation, etc.). Therefore, such heterogeneity made the crop-type discrimination more complicated. To overcome these challenges, the present work is the first study in this area which used the fusion of high spatiotemporal resolution Sentinel-1 and Sentinel-2 satellite images for land use and land cover mapping. Three machine learning classifier algorithms, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML), were applied to identify and map crop types in irrigated perimeter. In situ observations of the year 2018, for the R3 perimeter of Haouz plain in central Morocco, were used with satellite data of the same year to perform this work. The results showed that combined images acquired in C-band and the optical range improved clearly the crop-type classification performance (overall accuracy = 89%; Kappa = 0.85) compared to the classification results of optical or SAR data alone.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

Moumni

Lahrouni

2021

Scientifica

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“…Some organizations have developed several automated crop type classification systems based on free satellite data and computing technologies. For instance, Sen2Agri, an automatic system for national crop type mapping developed by Université Catholique de Louvain [23], has been adopted to extract the national-scale crop spatial distribution in countries such as Ukraine [26], Morocco [27], and sub-Saharan countries [23]. Recently, the ESA-initiated WorldCereal project (https://esa-worldcereal.org/en, accessed date: 20 February 2022) aimed to develop an EO-based global cropland monitoring system for mapping cropland and crop types [28].…”

Section: Introductionmentioning

confidence: 99%

An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China

Zeng

Tian

et al. 2022

Remote Sensing

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Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together.

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“…Studies have also been conducted in other parts of the world. In [22], a study was carried out on the plains of Haouz, Morocco, to evaluate Sen2-Agri's potentiality to generate agricultural soil use maps in zones with highly fragmented and heterogeneous land parcels. The crops selected for the classification process were cereals (in winter), melons (in summer) and three types of fruit trees (olives, oranges and apricots).…”

Section: Introductionmentioning

confidence: 99%

Ex Post Analysis of Water Supply Demand in an Agricultural Basin by Multi-Source Data Integration

et al. 2021

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In this work, we present a new methodology integrating data from multiple sources, such as observations from the Landsat-8 (L8) and Sentinel-2 (S2) satellites, with information gathered in field campaigns and information derived from different public databases, in order to characterize the water demand of crops (potential and estimated) in a spatially and temporally distributed manner. This methodology is applied to a case study corresponding to the basin of the Longaví River, located in south-central Chile. Potential and estimated demands, aggregated at different spatio-temporal scales, are compared to the streamflow of the Longaví River, as well as extractions from the groundwater system. The results obtained allow us to conclude that the availability of spatio-temporal information on the water availability and demand pairing allows us to close the water gap—i.e., the difference between supply and demand—allowing for better management of water resources in a watershed.

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Evaluation Of Sen2agri System Over Semi-Arid Conditions: A Case Study Of The Haouz Plain In Central Morocco

Cited by 7 publications

References 8 publications

Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China

Ex Post Analysis of Water Supply Demand in an Agricultural Basin by Multi-Source Data Integration

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