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Effects of Soil Moisture on Digital Image Estimation of Soil Organic Carbon
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Ziyang Wang, Hao Jia, Yu Zhao, Meijun Zhang, Meichen Feng, Chao Wang, Wude Yang
Crops | 2026, 42(2) : 238 - 246
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Crops | 2026, 42(2): 238-246
Effects of Soil Moisture on Digital Image Estimation of Soil Organic Carbon
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Ziyang Wang, Hao Jia, Yu Zhao, Meijun Zhang, Meichen Feng, Chao Wang, Wude Yang
Affiliations
  • College of Agronomy, Shanxi Agricultural University, Jinzhong 030801, Shanxi, China
Published: 2026-04-15 doi: 10.16035/j.issn.1001-7283.2026.02.030
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To explore the impact of soil moisture variation on the relationship between soil organic carbon (SOC) and color characteristic parameters, and to construct quantitative SOC prediction models based on color parameters, soil sample images under different soil moisture content (SMC) conditions were acquired by simulating continuous changes of farmland soil moisture to extract color characteristic parameters. Various mathematical transformation methods were employed to optimize these parameters. Combined with correlation analysis and regression models, the influence of soil moisture on the relationship between color characteristics and SOC was quantified, and SOC quantitative estimation models under different moisture conditions were established. The results indicated that SOC was significantly and negatively correlated with color characteristic parameters in RGB, HSV, and CIELab color spaces, with R, L, and V components showing the highest correlation. Reciprocal and logarithmic transformations enhanced these correlations. Soil moisture affected color component values; as SMC increased, most color parameter values decreased, and their correlation with SOC gradually weakened. Critical moisture contents were identified as SMC=15%. Color parameters such as 1/b*, lnb*, 1/S, and lnS effectively mitigated the impact of moisture on SOC prediction models. Under different moisture conditions, the BP neural network regression model outperformed the linear regression model, demonstrating superior predictive capability. This study demonstrates that the color characteristic parameters of digital images can be effectively utilized for the quantitative analysis of SOC.

Soil organic carbon  /  Soil moisture  /  Image processing technology  /  Color characteristic parameters  /  Quantitative relationship
Ziyang Wang, Hao Jia, Yu Zhao, Meijun Zhang, Meichen Feng, Chao Wang, Wude Yang. Effects of Soil Moisture on Digital Image Estimation of Soil Organic Carbon[J]. Crops, 2026 , 42 (2) : 238 -246 . DOI: 10.16035/j.issn.1001-7283.2026.02.030
Year 2026 volume 42 Issue 2
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doi: 10.16035/j.issn.1001-7283.2026.02.030
  • Receive Date:2025-02-05
  • Online Date:2026-04-30
  • Published:2026-04-15
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  • Received:2025-02-05
  • Revised:2025-03-04
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    College of Agronomy, Shanxi Agricultural University, Jinzhong 030801, Shanxi, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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