A new phenology-based method for mapping wheat and barley using time-series of Sentinel-2 images

Ashourloo, Davoud; Nematollahi, Hamed; Huete, Alfredo; Aghighi, Hossein; Azadbakht, Mohsen; Shahrabi, Hamid Salehi; Goodarzdashti, Salman

doi:10.1016/j.rse.2022.113206

Cited by 26 publications

(15 citation statements)

References 46 publications

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“…Among the ML approaches, random forest (RF) is often used for land use classification.The approach is based on decision trees that can handle a lot of variables [61,62] which was the case in this study [62]. The RF method is a non-parametric ML approach that displays good results when compared to the conventional parametric approaches [43]. We optimized the performance of the RF model by tunning (automatically done) on two significant parameters namely mtry (indicates the number of predictors tested at each tree node) and ntree (displays the number of decision trees runs at each iteration), the accuracy of the classification was enhanced by tunning on the number of ntree (after starting with the default value of 500 trees) using hyperparameter tuning for each year and each site, the justification for such tunning according to the site is that each year has its specific features thus, we adopted the value that leads to the best performance of the classifier.…”

Section: Classification Methodsmentioning

confidence: 99%

“…In that case, irrigated perennials were evergreen orchards (citrus) which makes the distinction with annual crops easier. According to [43], they developed a phenology-based approach to delineate wheat and barley by identifying the heading date using temporal feature of the different S2 bands. Good results were obtained (OA of 76%)across three sites in Iran, and the USA (North California and Idaho).…”

Section: Introductionmentioning

confidence: 99%

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Delineation of Orchard, Vineyard, and Olive Trees Based on Spectral and Phenology Metrics using Time-Series of Sentinel-2 during 2016-2021

Abubakar¹,

Chanzy²,

Flamain³

et al. 2023

Preprint

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The characteristics of the Sentinel-2 mission with a decametric resolution and frequent acquisitions allow to improve the identification of crops. The majority of the studies on crop classification using RS were targeted at herbaceous and gramineous crop classes while fewer results were obtained on woody crops which present a strong variability in management practices that make their identification difficult. Thus, this study aimed to propose a rapid, accurate, and cost-effective analytical approach for the delineation of fruit orchards (OC), vineyards (VY), and olive groves (OL) in the Mediterranean (Southern France) considering two locations. A classification based on phenology metrics (PM) de-rived from temporal Sentinel-2 time series was developed to perform the classification. The PM were computed by fitting a double logistic model on temporal profiles of vegeta-tion indices to delineate OC, VY, and a DC class gathering all remaining surfaces. The generated PM were introduced in a random forest (RF) algorithm to identify woody crops across the two sites. The method was tested on different vegetation indices, the best results being obtained with the leaf area index (LAI). To delineate OL in the DC class, the tem-poral features of the green chlorophyll vegetation index (GCVI) were found to be the most appropriated with a typical drop of the signal during the mid-season (DOY 150-250). As a final result, we obtained an overall accuracy ranging from 89-96% and Kappa of 0.86-0.95 by considering each study site and year (2016-2021), separately. This accuracy is much better than applying the RF algorithm on the LAI times series, which led to a Kappa rang-ing between 0.3 and 0.52 and demonstrates the interest of using phenological traits rather than the raw time series of the RS data. The method can be well reproduced from one year to another. Moreover, it is possible to apply the classification model of a given year to an-other, keeping good accuracy. This is an interesting feature to reduce the burden of col-lecting ground truth information. On the contrary, the use of a classification model cali-brated in one site and applied to another led to a strong degradation of the classification accuracy. Woody crop phenology is dependent on site climatic conditions as well as the cultivar and management practices that can differ from one site to another.

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Section: Classification Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delineation of Orchard, Vineyard, and Olive Trees Based on Spectral and Phenology Metrics using Time-Series of Sentinel-2 during 2016-2021

Abubakar¹,

Chanzy²,

Flamain³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…To harness this data effectively, several studies have utilized multi-temporal imagery and vegetation indices, such as the normalized difference vegetation index (NDVI), for the classification of croplands and crop types (Kang et al, 2021;Kumar et al, 2022). This phenology-based approach aids in distinguishing areas cultivated with different crops (Zhu et al, 2021;Ashourloo et al, 2022). However, despite these advances, challenges persist in combining various types of information to accurately distinguish between crop types, often because most studies rely on data from a single remote sensing sensor (Mateo-Sanchis et al, 2019;Berger et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Crop type classification and spatial mapping in River Nile and Northern State, Sudan, using Sentinel-2 satellite data and field observation

Yasin,

Sharif,

Yahia

et al. 2024

J. Degrade. Min. Land Manage.

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Maintaining productive farmland necessitates precise crop mapping and identification. While satellite remote sensing makes it possible to generate such maps, there are still issues to resolve, such as how to choose input data and the best classifier algorithm, especially in areas with scarce field data. Accurate assessments of the land used for farming are a crucial part of national food supply and production accounting in many African countries, and to this end, remote sensing tools are being increasingly put to use. The aim of this study was to assess the potentiality of Sentinel-2 to distinguish and discriminate crop species in the study area and constraints on accurately mapping cropping patterns in the winter season in River Nile and Northern State, Sudan. The research utilized Sentinel-2 Normalized Different Vegetation Index (NDVI) at 10 m resolution, unsupervised and supervised classification method with ground sample and accuracy assessment. The results of the study found that the signatures of grain sorghum, wheat, okra, Vicia faba, alfalfa, corn, haricot, onion, potato, tomato, lupine, tree cover, and garlic have clear distinctions, permitting an overall accuracy of 87.38%, with trees cover, onion, wheat, potato, garlic, alfalfa, tomato, lupine and Vicia faba achieving more than 87% accuracy. Major mislabeling problems occurred primarily in irrigated areas for grain sorghum, okra, corn, and haricot, in wooded areas comprised of small parcels of land. The research found that high-resolution temporal images combined with ground data had potential and utility for mapping cropland at the field scale in the winter.

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“…It can monitor ground information more rapidly and objectively, and it plays an important role in land cover identification and detection [10]. Remote sensing images can monitor the crop growth cycle, and optical remote sensing data can also reflect the spectral band information of crops, which has good applications in the task of WW classification extraction [11]. The use of remote sensing images is the most efficient method for accurately and rapidly obtaining information on the planting of WW over a large area.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net

et al. 2023

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This paper focuses on the problems of omission, misclassification, and inter-adhesion due to overly dense distribution, intraclass diversity, and interclass variability when extracting winter wheat (WW) from high-resolution images. This paper proposes a deep supervised network RAunet model with multi-scale features that incorporates a dual-attention mechanism with an improved U-Net backbone network. The model mainly consists of a pyramid input layer, a modified U-Net backbone network, and a side output layer. Firstly, the pyramid input layer is used to fuse the feature information of winter wheat at different scales by constructing multiple input paths. Secondly, the Atrous Spatial Pyramid Pooling (ASPP) residual module and the Convolutional Block Attention Module (CBAM) dual-attention mechanism are added to the U-Net model to form the backbone network of the model, which enhances the feature extraction ability of the model for winter wheat information. Finally, the side output layer consists of multiple classifiers to supervise the results of different scale outputs. Using the RAunet model to extract the spatial distribution information of WW from GF-2 imagery, the experimental results showed that the mIou of the recognition results reached 92.48%, an improvement of 2.66%, 4.15%, 1.42%, 2.35%, 3.76%, and 0.47% compared to FCN, U-Net, DeepLabv3, SegNet, ResUNet, and UNet++, respectively. The superiority of the RAunet model in high-resolution images for WW extraction was verified in effectively improving the accuracy of the spatial distribution information extraction of WW.

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A new phenology-based method for mapping wheat and barley using time-series of Sentinel-2 images

Cited by 26 publications

References 46 publications

Delineation of Orchard, Vineyard, and Olive Trees Based on Spectral and Phenology Metrics using Time-Series of Sentinel-2 during 2016-2021

Delineation of Orchard, Vineyard, and Olive Trees Based on Spectral and Phenology Metrics using Time-Series of Sentinel-2 during 2016-2021

Crop type classification and spatial mapping in River Nile and Northern State, Sudan, using Sentinel-2 satellite data and field observation

A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net

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