Comparing Pan-sharpened Landsat-9 and Sentinel-2 for Land-Use Classification Using Machine Learning Classifiers

Bouslıhım, Yassine; Kharrou, Mohamed Hakim; Miftah, Abdelhalim; Attou, Taha; Bouchaou, Lhoussaine; Chehbouni, Abdelghani

doi:10.1007/s41651-022-00130-0

Cited by 39 publications

(21 citation statements)

References 35 publications

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“…Furthermore, three different ML algorithms were compared: (1) Random Forest (RF), an ensemble learning method that constructs a multitude of decision trees at the training time and outputs the average prediction (for the regression task) of the individual trees [37]. RF is highly recommended for remote sensing applications due to its ability to handle large datasets and its robustness against overfitting, which makes it a powerful tool for land cover classification [38], estimation of soil properties [39], and biomass prediction [40], among other applications. ( 2) XGBoost (Extreme Gradient Boosting) is an efficient and scalable implementation of gradient-boosted decision trees, designed for speed and performance.…”

Section: Data Pre-processing and Machine Learning Algorithmsmentioning

confidence: 99%

Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction

Dahhani,

Raji,

Bouslihim

2024

Remote Sensing

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Exploring soil organic carbon (SOC) mapping is crucial for addressing critical challenges in environmental sustainability and food security. This study evaluates the suitability of the synergistic use of multi-temporal and high-resolution radar and optical remote sensing data for SOC prediction in the Kaffrine region of Senegal, covering over 1.1 million hectares. For this purpose, various scenarios were developed: Scenario 1 (Sentinel-1 data), Scenario 2 (Sentinel-2 data), Scenario 3 (Sentinel-1 and Sentinel-2 combination), Scenario 4 (topographic features), and Scenario 5 (Sentinel-1 and -2 with topographic features). The findings from comparing three different algorithms (Random Forest (RF), XGBoost, and Support Vector Regression (SVR)) with 671 soil samples for training and 281 samples for model evaluation highlight that RF outperformed the other models across different scenarios. Moreover, using Sentinel-2 data alone yielded better results than using only Sentinel-1 data. However, combining Sentinel-1 and Sentinel-2 data (Scenario 3) further improved the performance by 6% to 11%. Including topographic features (Scenario 5) achieved the highest accuracy, reaching an R2 of 0.7, an RMSE of 0.012%, and an RPIQ of 5.754 for the RF model. Applying the RF and XGBoost models under Scenario 5 for SOC mapping showed that both models tended to predict low SOC values across the study area, which is consistent with the predominantly low SOC content observed in most of the training data. This limitation constrains the ability of ML models to capture the full range of SOC variability, particularly for less frequent, slightly higher SOC values.

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Section: Data Pre-processing and Machine Learning Algorithmsmentioning

confidence: 99%

Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction

Dahhani,

Raji,

Bouslihim

2024

Remote Sensing

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“…The average overall accuracy of the data is 84. 61%, and the average Kappa coefficient is .80 (Bouslihim et al, 2022). The DEM datasets with 30 m spatial resolution were obtained from the Shuttle Radar Topography Mission (SRTM).…”

Section: Data Sourcesmentioning

confidence: 99%

Heterogeneity and optimization of ecological security pattern on a mountain town: A case of Tianzhu County in the Hexi Corridor, China

Chen

Chen²,

Yang³

et al. 2023

Front. Environ. Sci.

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As a primary concern in the ecosystem, understanding the impact of spatial heterogeneity of ecological networks on the development of environmental sustainability has overarching significance. The Tibetan Plateau’s edge is a vital contribution to the study of the plateau’s ecological network. However, there are few studies and explorations on the county-level ecological network in the arid region. This study aimed to evaluate ecological network spatial patterns in Tianzhu County and establish an ecological security evaluation index system. We used the minimum cumulative resistance (MCR) model and built an ecological resistance surface model from the exploratory spatial data analysis (ESDA) method. Then, by identifying ecological corridors and ecological nodes from the gravity model, we analyzed the ecological security pattern and proposed specific plans for optimizing the situation. The results showed that 1) county ecological security overall has been lower, and its values showed spatial heterogeneity in each direction, and 2) the Z-Score of the ecological security evaluation index was 70.1893, which shows ecological vulnerability in the arid region has significant spatial autocorrelation. The study identified 156 ecological corridors and 112 ecological nodes, which formed an ecological spatial pattern of “one belt and three zones.” Our analytical framework offers a valuable tool for constructing ecological security patterns in Tianzhu County and selecting “sources” at the regional scale, which can be applied to landscapes and geographical contexts for sustainable development in arid regions.

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“…Second, the proportion of cultivated land for each land block was calculated via remote sensing images (Landsat 5/7/8 TM/ETM). The method of supervised classification is used to extract cultivated land for each year [30]. Using ENVI 5.3 software, we obtained cultivated land grid-level values in the Heze area from 2006 to 2016, reflecting the winter wheat planted area from 2007 to 2017, respectively.…”

Section: Theoretical Gpp Calculationmentioning

confidence: 99%

Remote Sensing Application in Pure Premium Rate-Making of Winter Wheat Crop Insurance

Wang

et al. 2023

Sustainability

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Crop insurance is a crucial way to avoid disaster losses and to guarantee farmers’ basic production income in China and abroad. Securing agricultural production is a critical way to eradicate hunger and reduce poverty and an essential means to achieve the UN Sustainable Development Goals. How to pay out more quickly and fairly after a disaster has become an urgent issue for agricultural insurance. The standard domestic crop insurance rate is determined based on the statistical data of the entire administrative unit and ignores the spatial risk difference of disasters inside the administrative unit. Therefore, obtaining a pure premium based on crops inside the administrative unit is a key problem. Based on remote sensing data and insurance actuarial models, we studied and determined the fair premium rates to insure winter wheat at the farmer level in Heze, Shandong, China. Our study shows that remote sensing data can provide data security for determining a pure premium rate at the level of individual farms, and provide the primary reference for determining farmer-level crop insurance premium rates. The use of remote sensing for determining those rates can improve the customization of crop insurance and reduce farmers’ lower incomes due to exposure to natural disasters, improve farmers’ resilience to risk, and prevent a return to poverty due to disasters, ultimately reaching the UN Sustainable Development goals of eradicating hunger and reducing poverty.

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Comparing Pan-sharpened Landsat-9 and Sentinel-2 for Land-Use Classification Using Machine Learning Classifiers

Cited by 39 publications

References 35 publications

Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction

Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction

Heterogeneity and optimization of ecological security pattern on a mountain town: A case of Tianzhu County in the Hexi Corridor, China

Remote Sensing Application in Pure Premium Rate-Making of Winter Wheat Crop Insurance

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