Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Soil Data
2.2. Sentinel-2 Satellite Images
2.3. Covariates Selection
2.4. Mean Annual Precipitation (MAP) Data
2.5. Random Forest (RF) Model
2.6. Model Training and Validation
2.7. The Transformation of Laser Diffraction Method and Sieve-Pipette Method
3. Results
3.1. Descriptive Statistics
3.2. Analysis of Soil Reflectance Spectral Characteristics of Sentinel-2
3.3. Correlation of Covariates
3.4. Random Forest Model Accuracy Assessment
3.5. Spatial Distribution of Soil Particle-Size Fractions
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Amirian-Chakan, A.; Minasny, B.; Taghizadeh-Mehrjardi, R.; Akbarifazli, R.; Darvishpasand, Z.; Khordehbin, S. Some practical aspects of predicting texture data in digital soil mapping. Soil Tillage Res. 2019, 194, 104289. [Google Scholar] [CrossRef]
- Dunkl, I.; Ließ, M. On the benefits of clustering approaches in digital soil mapping: An application example concerning soil texture regionalization. SOIL 2022, 8, 541–558. [Google Scholar] [CrossRef]
- He, N.P.; Wu, L.; Wang, Y.S.; Han, X.G. Changes in carbon and nitrogen in soil particle-size fractions along a grassland restoration chronosequence in northern China. Geoderma 2009, 150, 302–308. [Google Scholar] [CrossRef]
- Liu, F.; Zhang, G.L.; Song, X.D.; Li, D.C.; Zhao, Y.G.; Yang, J.L.; Wu, H.Y.; Yang, F. High-resolution and three-dimensional mapping of soil texture of China. Geoderma 2020, 361, 114061. [Google Scholar] [CrossRef]
- Zeraatpisheh, M.; Ayoubi, S.; Jafari, A.; Finke, P. Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran. Geomorphology 2017, 285, 186–204. [Google Scholar] [CrossRef]
- Zhang, M.; Shi, W.J.; Xu, Z.W. Systematic comparison of five machine-learning models in classification and interpolation of soil particle size fractions using different transformed data. Hydrol. Earth Syst. Sci. 2020, 24, 2505–2526. [Google Scholar] [CrossRef]
- Shahriari, M.; Delbari, M.; Afrasiab, P.; Pahlavan-Rad, M.R. Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran. CATENA 2019, 182, 104149. [Google Scholar] [CrossRef]
- Zhang, M.; Liu, H.; Zhang, M.; Yang, H.; Jin, Y.; Han, Y.; Tang, H.; Zhang, X.; Zhang, X. Mapping Soil Organic Matter and Analyzing the Prediction Accuracy of Typical Cropland Soil Types on the Northern Songnen Plain. Remote Sens. 2021, 13, 5162. [Google Scholar] [CrossRef]
- Zhang, Y.; Sui, B.; Shen, H.O.; Ouyang, L. Mapping stocks of soil total nitrogen using remote sensing data: A comparison of random forest models with different predictors. Comput. Electron. Agric. 2019, 160, 23–30. [Google Scholar] [CrossRef]
- Gholizadeh, A.; Žižala, D.; Saberioon, M.; Borůvka, L. Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging. Remote Sens. Environ. 2018, 218, 89–103. [Google Scholar] [CrossRef]
- Fathololoumi, S.; Vaezi, A.R.; Alavipanah, S.K.; Ghorbani, A.; Saurette, D.; Biswas, A. Effect of multi-temporal satellite images on soil moisture prediction using a digital soil mapping approach. Geoderma 2021, 385, 114901. [Google Scholar] [CrossRef]
- Baumgardner, M.F.; Silva, L.F.; Biehl, L.L.; Stoner, E.R. Reflectance Properties of Soils. Adv. Agron. 1986, 38, 1–44. [Google Scholar]
- Ben-Dor, E.; Banin, A. Near infrared analysis (NIRA) as a simultaneously method to evaluate spectral featureless constituents in soils. Soil Sci. 1995, 159, 259–270. [Google Scholar] [CrossRef]
- Gomez, C.; Dharumarajan, S.; Féret, J.-B.; Lagacherie, P.; Ruiz, L.; Sekhar, M. Use of Sentinel-2 Time-Series Images for Classification and Uncertainty Analysis of Inherent Biophysical Property: Case of Soil Texture Mapping. Remote Sens. 2019, 11, 565. [Google Scholar] [CrossRef]
- Zhang, M.; Zhang, M.; Yang, H.; Jin, Y.; Zhang, X.; Liu, H. Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine. Remote Sens. 2021, 13, 2934. [Google Scholar] [CrossRef]
- Swain, S.R.; Chakraborty, P.; Panigrahi, N.; Vasava, H.B.; Reddy, N.N.; Roy, S.; Majeed, I.; Das, B.S. Estimation of soil texture using Sentinel-2 multispectral imaging data: An ensemble modeling approach. Soil Tillage Res. 2021, 213, 105134. [Google Scholar] [CrossRef]
- Bittelli, M.; Pellegrini, S.; Olmi, R.; Andrenelli, M.C.; Simonetti, G.; Borrelli, E.; Morari, F. Experimental evidence of laser diffraction accuracy for particle size analysis. Geoderma 2022, 409, 115627. [Google Scholar] [CrossRef]
- Peng, Y.H.; Keating, K.; Myers, D.B. NMR relaxation times for soil texture estimation in the laboratory: A comparison to the laser diffraction and sieve–pipette methods. Eur. J. Soil Sci. 2021, 72, 918–933. [Google Scholar] [CrossRef]
- Yang, J.L.; Zhang, G.L.; Li, D.C.; Pan, J.H. Relationships of Soil Particle Size Distribution between Sieve-Pipette and Laser Diffraction Methods. Acta Pedol. Sin. 2009, 46, 772–780. [Google Scholar]
- Li, H.R.; Liu, B.; Wang, R.X.; Liu, W.; Fang, Y.; Yang, D.L.; Zou, X.Y. Particle-size Distribution Affected by Testing Method. J. Desert Res. 2018, 38, 619–627. [Google Scholar]
- Li, J.; Wan, H.Y.; Shang, S.H. Comparison of interpolation methods for mapping layered soil particle-size fractions and texture in an arid oasis. Catena 2020, 190, 104514. [Google Scholar] [CrossRef]
- Song, X.D.; Yang, F.; Ju, B.; Li, D.C.; Zhao, Y.G.; Yang, J.L.; Zhang, G.L. The influence of the conversion of grassland to cropland on changes in soil organic carbon and total nitrogen stocks in the Songnen Plain of Northeast China. Catena 2018, 171, 588–601. [Google Scholar] [CrossRef]
- Zhang, W.L.; Xu, A.G.; Zhang, R.L.; Ji, H.J. Review of Soil Classification and Revision of China Soil Classification System. Sci. Agric. Sin. 2014, 47, 3214–3230. [Google Scholar]
- Wang, X.; Li, S.; Wang, L.; Zheng, M.; Wang, Z.; Song, K. Effects of cropland reclamation on soil organic carbon in China’s black soil region over the past 35 years. Glob. Chang. Biol. 2023, 29, 5460–5477. [Google Scholar] [CrossRef]
- Main-Knorn, M.; Pflug, B.; Louis, J.; Debaecker, V.; Müller-Wilm, U.; Gascon, F. Sen2Cor for Sentinel-2. Proc. SPIE 2017, 10427, 1042704. [Google Scholar]
- Li, S.J.; Song, K.S.; Wang, S.; Liu, G.; Wen, Z.D.; Shang, Y.X.; Lyu, L.L.; Chen, F.F.; Xu, S.Q.; Tao, H.; et al. Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm. Sci. Total Environ. 2021, 778, 146271. [Google Scholar] [CrossRef]
- Singh, D.; Herlin, I.; Berroir, J.P.; Silva, E.F.; Meirelles, M.S. An approach to correlate NDVI with soil colour for erosion process using NOAA/AVHRR data. Adv. Space Res. 2004, 33, 328–332. [Google Scholar] [CrossRef]
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the 3rd ERTS Symposium, Washington, DC, USA, 1 January 1974. [Google Scholar]
- Nellis, M.D.; Briggs, J.M. Transformed vegetation index for measuring spatial variation in drought impactedbiomass on Konza Prairie, Kansas. Trans. Kans. Acad. Sci. 1992, 95, 93–99. [Google Scholar] [CrossRef]
- Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213. [Google Scholar] [CrossRef]
- Huete, A.R. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 1988, 25, 295–309. [Google Scholar] [CrossRef]
- Marsett, R.C.; Qi, J.G.; Heilman, P.; Biedenbender, S.H.; Watson, M.C.; Amer, S.; Weltz, M.; Goodrich, D.; Marsett, R. Remote Sensing for Grassland Management in the Arid Southwest. Rangel. Ecol. Manag. 1988, 59, 530–540. [Google Scholar] [CrossRef]
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 1979, 8, 127–150. [Google Scholar] [CrossRef]
- Qi, J.; Kerr, Y.; Chehbouni, A. External factor consideration in vegetation index development. In Proceedings of the International Symposium on Physical Measurements and Signatures in Remote Sensing, Val d’Isere, France, 1 January 1994. [Google Scholar]
- Jordan, C.F. Derivation of leaf area index from quality of light on the forest floor. Ecology 1969, 50, 663–666. [Google Scholar] [CrossRef]
- Rock, B.N.; Williams, D.L.; Vogelmann, J.E. Field and airborne spectral characterization of suspected damage in red spruce (picea rubens) from Vermont. In Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN, USA, 1 January 1985. [Google Scholar]
- Pouget, M.; Madeira, J.; Le, F.E.; Kamel, S. Caractéristiques spectrales des surfaces sableuses de la région côtière Nord-Ouest de l’Egypte: Application aux données satellitaires SPOT. In Proceedings of the Caractérisation et Suivi des Milieux Terrestres en Régions Arides et Tropicales, Paris, France, 4–6 December 1990. [Google Scholar]
- Escadafal, R. Remote sensing of arid soil surface color with Landsat thematic mapper. Adv. Space Res. 1989, 9, 159–163. [Google Scholar] [CrossRef]
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- Denisko, D.; Hoffman, M.M. Classification and interaction in random forests. Proc. Natl. Acad. Sci. USA 2018, 115, 1690–1692. [Google Scholar] [CrossRef]
- Ji, W.J.; Adamchuk, V.I.; Chen, S.C.; Mat Su, A.S.; Ismail, A.; Gan, Q.J.; Shi, Z.; Biswas, A. Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study. Geoderma 2019, 341, 111–128. [Google Scholar] [CrossRef]
- Wang, X.; Wang, L.P.; Li, S.J.; Wang, Z.M.; Zheng, M.; Song, K.S. Remote estimates of soil organic carbon using multi-temporal synthetic images and the probability hybrid model. Geoderma 2022, 425, 116066. [Google Scholar] [CrossRef]
- Meng, X.T.; Bao, Y.L.; Wang, Y.; Zhang, X.L.; Liu, H.J. An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning algorithms. Remote Sens. Environ. 2022, 280, 113166. [Google Scholar] [CrossRef]
- Adhikari, K.; Kheir, R.B.; Greve, M.B.; Bøcher, P.K.; Malone, B.P.; Minasny, B.; McBratney, A.B.; Greve, M.H. High-Resolution 3-D Mapping of Soil Texture in Denmark. Soil Sci. Soc. Am. J. 2013, 77, 860–876. [Google Scholar] [CrossRef]
- Demattê, J.A.M.; Ramirez-Lopez, L.; Rizzo, R.; Nanni, M.R.; Fiorio, P.R.; Fongaro, C.T.; Medeiros Neto, L.G.; Safanelli, J.L.; Da, S.; Barros, P.P. Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map. Remote Sens. 2016, 8, 826. [Google Scholar] [CrossRef]
- Chagas, C.D.S.; Junior, W.D.C.; Bhering, S.B.; Filho, B.C. Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions. CATENA 2016, 139, 232–240. [Google Scholar] [CrossRef]
- Gao, C.Y.; Wei, C.F.; Zhang, L.N.; Han, D.X.; Liu, H.X.; Yu, X.F.; Wang, G.P. Historical (1880s–2000s) impact of wind erosion on wetland patches in semi-arid regions: A case study in the western Songnen Plain (China). Aeolian Res. 2019, 38, 13–23. [Google Scholar] [CrossRef]
- Zhao, H.Q.; Zhang, Z.H.; Wang, C.Q. Actuality, dynamic change and the prevention countermeasure of desertification in the Songnan Plain. J. Arid Land Resour. Environ. 2009, 23, 107–113. [Google Scholar]
- Li, F.P.; Li, J.Y.; Xu, Z.X. The Status Quo of Black Soil Degradation and Water and Soil Loss in Northeast China. Res. Soil Water Conserv. 2006, 3, 50–54. [Google Scholar]
- Li, S.L.; Li, H.P.; Lin, Y.; Xiao, B.; Wang, G.P. Effects of Tillage Methods on Wind Erosion in Farmland of Northeastern China. J. Soil Water Conserv. 2019, 33, 110–118+220. [Google Scholar]
- Lyu, Y.H.; Liu, G.H.; Feng, X.M. Environmental Impacts of Soil Water Erosion: A Review of Influence Factors, Hot Research Topics and Evaluation Indices. J. Ecol. Rural Environ. 2011, 27, 93–99. [Google Scholar]
- Zhang, H.L.; Gao, W.S.; Chen, F.; Zhu, W.S. Prospects and present situation of conservation tillage. J. China Agric. Univ. 2005, 1, 16–20. [Google Scholar]
- He, W.Y. Diversity of Farmland Black SoilStoichiometric Characteristics and Microbial Effects of Conservation Tillage on Ecological. Master’s Thesis, Northeast Agricultural University, Harbin, China, 2023. [Google Scholar]
Index | Definition | Reference |
---|---|---|
NDVI | [28] | |
TVI | [29] | |
EVI | [30] | |
SAVI | [31] | |
SATVI | [32] | |
GRVI | [33] | |
MSAVI2 | [34] | |
V | [35] | |
MSI | [36] | |
RI | [37] | |
BI | [38] | |
BI2 | [38] | |
CI | [37] |
Texture | Min (%) | Max (%) | Mean (%) | Median (%) | SD (%) | CV |
---|---|---|---|---|---|---|
Sand | 3.00 | 90.04 | 28.02 | 18.47 | 21.76 | 77.65 |
Silt | 7.60 | 84.79 | 60.34 | 68.50 | 18.80 | 31.15 |
Clay | 2.36 | 22.18 | 11.64 | 12.24 | 3.70 | 31.82 |
Covariates | Soil Particle-Size Fractions (%) | ||
---|---|---|---|
Sand | Silt | Clay | |
Band2 | 0.665 ** | −0.639 ** | −0.664 ** |
Band3 | 0.711 ** | −0.686 ** | −0.693 ** |
Band4 | 0.734 ** | −0.709 ** | −0.714 ** |
Band8 | 0.735 ** | −0.711 ** | −0.710 ** |
Band11 | 0.789 ** | −0.761 ** | −0.775 ** |
Band12 | 0.778 ** | −0.749 ** | −0.770 ** |
NDVI | −0.403 ** | 0.384 ** | 0.417 ** |
TVI | −0.411 ** | 0.392 ** | 0.425 ** |
EVI | −0.325 ** | 0.324 ** | 0.265 ** |
SAVI | 0.489 ** | −0.475 ** | −0.460 ** |
SATVI | −0.778 ** | 0.749 ** | 0.770 ** |
GRVI | 0.187 ** | −0.174 ** | −0.214 ** |
MSAVI2 | −0.420 ** | 0.401 ** | 0.434 ** |
V | −0.359 ** | 0.342 ** | 0.372 ** |
MSI | −0.242 ** | 0.240 ** | 0.204 ** |
RI | −0.370 ** | 0.354 ** | 0.373 ** |
BI | 0.728 ** | −0.703 ** | −0.709 ** |
BI2 | 0.737 ** | −0.712 ** | −0.714 ** |
CI | −0.187 ** | 0.174 ** | 0.214 ** |
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Zheng, M.; Wang, X.; Li, S.; Zhu, B.; Hou, J.; Song, K. Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery. Remote Sens. 2023, 15, 5351. https://doi.org/10.3390/rs15225351
Zheng M, Wang X, Li S, Zhu B, Hou J, Song K. Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery. Remote Sensing. 2023; 15(22):5351. https://doi.org/10.3390/rs15225351
Chicago/Turabian StyleZheng, Miao, Xiang Wang, Sijia Li, Bingxue Zhu, Junbin Hou, and Kaishan Song. 2023. "Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery" Remote Sensing 15, no. 22: 5351. https://doi.org/10.3390/rs15225351
APA StyleZheng, M., Wang, X., Li, S., Zhu, B., Hou, J., & Song, K. (2023). Soil Texture Mapping in Songnen Plain of China Using Sentinel-2 Imagery. Remote Sensing, 15(22), 5351. https://doi.org/10.3390/rs15225351