Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
Above-ground biomass (AGB) is a core indicator for evaluating grassland ecosystem health and carbon storage. Traditional ground-based AGB measurements are labor-intensive and ill suited for large-scale monitoring. This study addresses this gap by developing a Multilayer Perceptron (MLP) model integr...
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2025-06-01
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| author | Zhiguo Wang Shuai Ma Yongguang Zhai Pingping Huang Xiangli Yang Jianhao Cui Qimuge Eridun |
| author_facet | Zhiguo Wang Shuai Ma Yongguang Zhai Pingping Huang Xiangli Yang Jianhao Cui Qimuge Eridun |
| author_sort | Zhiguo Wang |
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| description | Above-ground biomass (AGB) is a core indicator for evaluating grassland ecosystem health and carbon storage. Traditional ground-based AGB measurements are labor-intensive and ill suited for large-scale monitoring. This study addresses this gap by developing a Multilayer Perceptron (MLP) model integrating Landsat 9 OLI/TIRS imagery acquired on 15 August 2024, with ground data from 78 sampling points (62 training, 16 testing). Incorporating fourteen multi-source features (seven vegetation indices, e.g., Modified Vegetation Index (MVI) and Green Chlorophyll Index (CIg); four meteorological variables; three soil properties), all data were standardized via z-score normalization before training. The MLP model, optimized via six-fold cross-validation, achieved an R<sup>2</sup> of 0.765 and RMSE of 38.066 g/m<sup>2</sup>, outperforming XGBoost (R<sup>2</sup> = 0.723, RMSE = 41.354 g/m<sup>2</sup>) with a statistically significant 5.8% accuracy improvement (<i>p</i> < 0.05). Spatial analysis revealed a north-to-south AGB gradient, strongly correlated with precipitation gradients (250–350 mm/year) and soil organic carbon (R = 0.428). These findings provide a robust framework for climate-adaptive grassland management and carbon assessment in semi-arid regions. |
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| publishDate | 2025-06-01 |
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| spelling | doaj-art-462bf8f491944f349319dc7fb8afa92b2025-08-20T02:32:56ZengMDPI AGApplied Sciences2076-34172025-06-011511628010.3390/app15116280Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass EstimationZhiguo Wang0Shuai Ma1Yongguang Zhai2Pingping Huang3Xiangli Yang4Jianhao Cui5Qimuge Eridun6College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, ChinaCollege of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaCollege of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, ChinaGrassland Workstation, Xiwuzhumuqin Banner, Xilingol League 026200, ChinaAbove-ground biomass (AGB) is a core indicator for evaluating grassland ecosystem health and carbon storage. Traditional ground-based AGB measurements are labor-intensive and ill suited for large-scale monitoring. This study addresses this gap by developing a Multilayer Perceptron (MLP) model integrating Landsat 9 OLI/TIRS imagery acquired on 15 August 2024, with ground data from 78 sampling points (62 training, 16 testing). Incorporating fourteen multi-source features (seven vegetation indices, e.g., Modified Vegetation Index (MVI) and Green Chlorophyll Index (CIg); four meteorological variables; three soil properties), all data were standardized via z-score normalization before training. The MLP model, optimized via six-fold cross-validation, achieved an R<sup>2</sup> of 0.765 and RMSE of 38.066 g/m<sup>2</sup>, outperforming XGBoost (R<sup>2</sup> = 0.723, RMSE = 41.354 g/m<sup>2</sup>) with a statistically significant 5.8% accuracy improvement (<i>p</i> < 0.05). Spatial analysis revealed a north-to-south AGB gradient, strongly correlated with precipitation gradients (250–350 mm/year) and soil organic carbon (R = 0.428). These findings provide a robust framework for climate-adaptive grassland management and carbon assessment in semi-arid regions.https://www.mdpi.com/2076-3417/15/11/6280above-ground biomassmultilayer perceptronremote sensinggrasslandsmachine learning |
| spellingShingle | Zhiguo Wang Shuai Ma Yongguang Zhai Pingping Huang Xiangli Yang Jianhao Cui Qimuge Eridun Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation Applied Sciences above-ground biomass multilayer perceptron remote sensing grasslands machine learning |
| title | Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation |
| title_full | Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation |
| title_fullStr | Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation |
| title_full_unstemmed | Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation |
| title_short | Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation |
| title_sort | remote sensing based multilayer perceptron model for grassland above ground biomass estimation |
| topic | above-ground biomass multilayer perceptron remote sensing grasslands machine learning |
| url | https://www.mdpi.com/2076-3417/15/11/6280 |
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