Mapping Soil Organic Matter in Cultivated Land Using Landsat 8 Image and GA-AdaBoost Algorithm Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.1109/jstars.2025.3614884
Soil organic matter (SOM) is essential for maintaining soil structure, nutrient supply, and water regulation in cultivated land, significantly impacting agricultural productivity and the health of agricultural systems. However, developing robust inversion methods for SOM using satellite remote-sensing technology faces challenges due to the spatial heterogeneity of different land use patterns. This study aimed to improve the accuracy of estimating cultivated land SOM from remote-sensing images during the bare-soil period. A total of 15 spectral features were extracted from Landsat 8 image and the recursive feature elimination based on cross validation (RFECV) was applied to identify the optimal feature combination. Multiple machine-learning methods optimized by genetic algorithms (GAs) and particle swarm optimization were compared to identify the best method for estimating SOM. The results revealed that the features screened by RFECV showed improved modeling accuracy over unscreened features and that the highest accuracy of estimating SOM, with R2 of 0.66, root-mean-square error of 5.93 g/kg, and mean absolute error of 4.70 g/kg, was achieved by the GA-optimized adaptive boosting (GA-AdaBoost) method. Therefore, Landsat 8 remote-sensing images acquired during the bare-soil period, the combination of RFECV and the GA-AdaBoost method can achieve the accurate estimation of cultivated land SOM at the regional scale.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2025.3614884
- OA Status
- gold
- References
- 84
- OpenAlex ID
- https://openalex.org/W4414539083
Raw OpenAlex JSON
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https://openalex.org/W4414539083Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jstars.2025.3614884Digital Object Identifier
- Title
-
Mapping Soil Organic Matter in Cultivated Land Using Landsat 8 Image and GA-AdaBoost AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Xuzhou Qu, Shuwen Jiang, Xiaohe Gu, Jingping Zhou, Yuan Tian, Xingyu Liu, Fajian Zong, Mengjie Li, Yong JiList of authors in order
- Landing page
-
https://doi.org/10.1109/jstars.2025.3614884Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/jstars.2025.3614884Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
- References (count)
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84Number of works referenced by this work
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| abstract_inverted_index.in | 15 |
| abstract_inverted_index.is | 4 |
| abstract_inverted_index.of | 25, 46, 58, 72, 143, 148, 152, 159, 183, 194 |
| abstract_inverted_index.on | 88 |
| abstract_inverted_index.to | 42, 54, 94, 114 |
| abstract_inverted_index.SOM | 34, 62, 197 |
| abstract_inverted_index.The | 122 |
| abstract_inverted_index.and | 12, 22, 82, 108, 138, 155, 185 |
| abstract_inverted_index.can | 189 |
| abstract_inverted_index.due | 41 |
| abstract_inverted_index.for | 6, 33, 119 |
| abstract_inverted_index.the | 23, 43, 56, 67, 83, 96, 116, 126, 140, 165, 178, 181, 186, 191, 199 |
| abstract_inverted_index.use | 49 |
| abstract_inverted_index.was | 92, 162 |
| abstract_inverted_index.4.70 | 160 |
| abstract_inverted_index.5.93 | 153 |
| abstract_inverted_index.SOM, | 145 |
| abstract_inverted_index.SOM. | 121 |
| abstract_inverted_index.Soil | 0 |
| abstract_inverted_index.This | 51 |
| abstract_inverted_index.best | 117 |
| abstract_inverted_index.from | 63, 78 |
| abstract_inverted_index.land | 48, 61, 196 |
| abstract_inverted_index.mean | 156 |
| abstract_inverted_index.over | 135 |
| abstract_inverted_index.soil | 8 |
| abstract_inverted_index.that | 125, 139 |
| abstract_inverted_index.were | 76, 112 |
| abstract_inverted_index.with | 146 |
| abstract_inverted_index.(GAs) | 107 |
| abstract_inverted_index.(SOM) | 3 |
| abstract_inverted_index.0.66, | 149 |
| abstract_inverted_index.RFECV | 130, 184 |
| abstract_inverted_index.aimed | 53 |
| abstract_inverted_index.based | 87 |
| abstract_inverted_index.cross | 89 |
| abstract_inverted_index.error | 151, 158 |
| abstract_inverted_index.faces | 39 |
| abstract_inverted_index.image | 81 |
| abstract_inverted_index.land, | 17 |
| abstract_inverted_index.study | 52 |
| abstract_inverted_index.swarm | 110 |
| abstract_inverted_index.total | 71 |
| abstract_inverted_index.using | 35 |
| abstract_inverted_index.water | 13 |
| abstract_inverted_index.during | 66, 177 |
| abstract_inverted_index.health | 24 |
| abstract_inverted_index.images | 65, 175 |
| abstract_inverted_index.matter | 2 |
| abstract_inverted_index.method | 118, 188 |
| abstract_inverted_index.robust | 30 |
| abstract_inverted_index.scale. | 201 |
| abstract_inverted_index.showed | 131 |
| abstract_inverted_index.(RFECV) | 91 |
| abstract_inverted_index.Landsat | 79, 172 |
| abstract_inverted_index.achieve | 190 |
| abstract_inverted_index.applied | 93 |
| abstract_inverted_index.feature | 85, 98 |
| abstract_inverted_index.genetic | 105 |
| abstract_inverted_index.highest | 141 |
| abstract_inverted_index.improve | 55 |
| abstract_inverted_index.method. | 170 |
| abstract_inverted_index.methods | 32, 102 |
| abstract_inverted_index.optimal | 97 |
| abstract_inverted_index.organic | 1 |
| abstract_inverted_index.period, | 180 |
| abstract_inverted_index.period. | 69 |
| abstract_inverted_index.results | 123 |
| abstract_inverted_index.spatial | 44 |
| abstract_inverted_index.supply, | 11 |
| abstract_inverted_index.However, | 28 |
| abstract_inverted_index.Multiple | 100 |
| abstract_inverted_index.absolute | 157 |
| abstract_inverted_index.accuracy | 57, 134, 142 |
| abstract_inverted_index.accurate | 192 |
| abstract_inverted_index.achieved | 163 |
| abstract_inverted_index.acquired | 176 |
| abstract_inverted_index.adaptive | 167 |
| abstract_inverted_index.boosting | 168 |
| abstract_inverted_index.compared | 113 |
| abstract_inverted_index.features | 75, 127, 137 |
| abstract_inverted_index.identify | 95, 115 |
| abstract_inverted_index.improved | 132 |
| abstract_inverted_index.modeling | 133 |
| abstract_inverted_index.nutrient | 10 |
| abstract_inverted_index.particle | 109 |
| abstract_inverted_index.regional | 200 |
| abstract_inverted_index.revealed | 124 |
| abstract_inverted_index.screened | 128 |
| abstract_inverted_index.spectral | 74 |
| abstract_inverted_index.systems. | 27 |
| abstract_inverted_index.bare-soil | 68, 179 |
| abstract_inverted_index.different | 47 |
| abstract_inverted_index.essential | 5 |
| abstract_inverted_index.extracted | 77 |
| abstract_inverted_index.impacting | 19 |
| abstract_inverted_index.inversion | 31 |
| abstract_inverted_index.optimized | 103 |
| abstract_inverted_index.patterns. | 50 |
| abstract_inverted_index.recursive | 84 |
| abstract_inverted_index.satellite | 36 |
| abstract_inverted_index.Therefore, | 171 |
| abstract_inverted_index.algorithms | 106 |
| abstract_inverted_index.challenges | 40 |
| abstract_inverted_index.cultivated | 16, 60, 195 |
| abstract_inverted_index.developing | 29 |
| abstract_inverted_index.estimating | 59, 120, 144 |
| abstract_inverted_index.estimation | 193 |
| abstract_inverted_index.regulation | 14 |
| abstract_inverted_index.structure, | 9 |
| abstract_inverted_index.technology | 38 |
| abstract_inverted_index.unscreened | 136 |
| abstract_inverted_index.validation | 90 |
| abstract_inverted_index.GA-AdaBoost | 187 |
| abstract_inverted_index.combination | 182 |
| abstract_inverted_index.elimination | 86 |
| abstract_inverted_index.maintaining | 7 |
| abstract_inverted_index.GA-optimized | 166 |
| abstract_inverted_index.agricultural | 20, 26 |
| abstract_inverted_index.combination. | 99 |
| abstract_inverted_index.g/kg, | 154, 161 |
| abstract_inverted_index.optimization | 111 |
| abstract_inverted_index.productivity | 21 |
| abstract_inverted_index.(GA-AdaBoost) | 169 |
| abstract_inverted_index.heterogeneity | 45 |
| abstract_inverted_index.significantly | 18 |
| abstract_inverted_index.remote-sensing | 37, 64, 174 |
| abstract_inverted_index.machine-learning | 101 |
| abstract_inverted_index.root-mean-square | 150 |
| abstract_inverted_index.<italic>R</italic><sup>2</sup> | 147 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.5691251 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |