A novel method for automatic potato mapping using time series of Sentinel-2 images

Ashourloo, Davoud; Shahrabi, Hamid Salehi; Azadbakht, Mohsen; Rad, Amir Moeini; Aghighi, Hossein; Radiom, Soheil

doi:10.1016/j.compag.2020.105583

Cited by 37 publications

(22 citation statements)

References 65 publications

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“…Ashourloo et al [58] presented a method for mapping the potato crop in Iran in 2019. They analyzed and used Sentinel-2 images and the machine learning method SVM and Maximum Likelihood (ML).…”

Section: Related Workmentioning

confidence: 99%

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

Pech-May

Aquino-Santos

Ríos-Toledo

et al. 2022

Sensors

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Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.

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Section: Related Workmentioning

confidence: 99%

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

Pech-May

Aquino-Santos

Ríos-Toledo

et al. 2022

Sensors

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“…Besides the Landsat missions, Sentinel-2 is another popular platform for agriculture monitoring. For the potato management, Sentinel-2 imagery has been used for LAI and chlorophyll content estimation ( Herrmann et al, 2011 ; Clevers and Kooistra, 2012 ; Clevers and Gitelson, 2013 ; Clevers et al, 2017 ), radiation utilization assessment ( Peng et al, 2021 ), yield prediction ( Al-Gaadi et al, 2016 ; Gómez et al, 2019 ; Abou Ali et al, 2020 ), and crop mapping ( Ashourloo et al, 2020 ). Launched in 2015 by Copernicus Programme, Sentinel-2 systematically acquires optical imagery with global coverage.…”

Section: Remote Sensing Platforms For Data Collectionmentioning

confidence: 99%

A review of remote sensing for potato traits characterization in precision agriculture

Sun

Zhou

et al. 2022

Front. Plant Sci.

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Potato is one of the most significant food crops globally due to its essential role in the human diet. The growing demand for potato, coupled with severe environmental losses caused by extensive farming activities, implies the need for better crop protection and management practices. Precision agriculture is being well recognized as the solution as it deals with the management of spatial and temporal variability to improve agricultural returns and reduce environmental impact. As the initial step in precision agriculture, the traditional methods of crop and field characterization require a large input in labor, time, and cost. Recent developments in remote sensing technologies have facilitated the process of monitoring crops and quantifying field variations. Successful applications have been witnessed in the area of precision potato farming. Thus, this review reports the current knowledge on the applications of remote sensing technologies in precision potato trait characterization. We reviewed the commonly used imaging sensors and remote sensing platforms with the comparisons of their strengths and limitations and summarized the main applications of the remote sensing technologies in potato. As a result, this review could update potato agronomists and farmers with the latest approaches and research outcomes, as well as provide a selective list for those who have the intentions to apply remote sensing technologies to characterize potato traits for precision agriculture.

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“…However, despite progress, there are recurrent challenges in the use of remote sensing for the purpose of monitoring small crops with satellite remote sensing. With the use of supervised Machine Learning models, getting training data, in this case images labeled with polygons of the crops of interest, is a constant challenge, since it is a costly and time-consuming process [17]. Additionally, the culture methodology, the region where it is being cultivated, and the temperature, among other factors, entail a variability of the characteristics among crops [18], which can cause different reflectance values.…”

Section: Literature Reviewmentioning

confidence: 99%

Remote Sensing and Machine Learning Modeling to Support the Identification of Sugarcane Crops

et al. 2022

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One of the main concerns of agricultural financing institutions is to make sure the loans they grant are used for the stated objective when the loan was requested. Specifically, when Banco Agrario de Colombia grants loans for crop farmers, it schedules verification visits to the cultivation sites to check if the crop stipulated in the loan agreement exists and assess its health. These visits are challenging to make due to the number of visits over vast areas that they need to schedule, lack of trained personnel, and difficulty of access. This article proposes a software tool, based on a machine learning model for processing free satellite imagery, to support the bank's identification of non-compliant crops with the investment plan before making field visits, minimizing the loss of investment by focusing on those areas to prioritize the visits. Sugarcane along the department of Boyacá, Colombia was chosen as the case of study. Free access satellite imagery through the Colombian Data Cube (CDCol) was used and machine learning models were applied on them to classify the land and predict the presence of the crop, a Random Forest model achieved an overall F1-score of 91% using Landsat-8 imagery and a K-nearest Neighbors model achieved an overall F1-score of 98% using Sentinel-2 imagery.

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A novel method for automatic potato mapping using time series of Sentinel-2 images

Cited by 37 publications

References 65 publications

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine

A review of remote sensing for potato traits characterization in precision agriculture

Remote Sensing and Machine Learning Modeling to Support the Identification of Sugarcane Crops

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