Regional-Scale Topsoil Organic Matter Estimation Based on a Geographic Detector Model Using Landsat Data, Pingtan Island, Fujian, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Soil Sample Collection
2.2.1. Sampling Design
2.2.2. Soil Sample Collection and Lab Analysis
2.3. Environmental Variables
2.3.1. Remote Sensing Variables
2.3.2. Ancillary Data Collection
2.4. Analysis Methods
2.4.1. Sensitivity of Environmental Variables to SOM
2.4.2. SOM Model Using Geostatistical Interpolation
2.4.3. SOM Modeling Using Remote Sensing
2.4.4. Assessment of SOM Model Performance
3. Results and Discussion
3.1. Statistical Characteristics of SOM
3.2. Spatial Characteristics of Environmental Variables
3.3. Sensitivity of Environmental Variables on SOM
3.3.1. Comparative Analysis of Explanatory Power
3.3.2. The Influence of Environmental Factors on SOM
3.3.3. Risk and Ecological Analysis of SOM
3.4. SOM Estimation Model
3.4.1. Accuracy of SOM Estimation Using Interpolation
3.4.2. Accuracy of SOM Estimation Using Remote Sensing
3.5. SOM Mapping and Comparison
4. Conclusions and Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Piccini, C.; Marchetti, A.; Francaviglia, R. Estimation of soil organic matter by geostatistical methods: Use of auxiliary information in agricultural and environmental assessment. Ecol. Ind. 2014, 36, 301–314. [Google Scholar] [CrossRef]
- Zhang, Z.; Yu, D.; Wang, X.; Pan, Y.; Zhang, G.; Shi, X. Influence of the Selection of Interpolation Method on Revealing Soil Organic Carbon Variability in the Red Soil Region, China. Sustainability 2018, 10, 2290. [Google Scholar] [CrossRef]
- Levi, N.; Karnieli, A.; Paz-Kagan, T. Using reflectance spectroscopy for detecting land-use effects on soil quality in drylands. Soil Tillage Res. 2020, 199, 104571. [Google Scholar] [CrossRef]
- Takata, Y.; Funakawa, S.; Akshalov, K.; Ishida, N.; Kosaki, T. Spatial prediction of soil organic matter in northern Kazakhstan based on topographic and vegetation information. Soil Sci. Plant Nutr. 2007, 53, 289–299. [Google Scholar] [CrossRef]
- Angst, G.; Messinger, J.; Greiner, M.; Häusler, W.; Hertel, D.; Kirfel, K.; Kögel-Knabner, I.; Leuschner, C.; Rethemeyer, J.; Mueller, C.W. Soil organic carbon stocks in topsoil and subsoil controlled by parent material, carbon input in the rhizosphere, and microbial-derived compounds. Soil Biol. Biochem. 2018, 122, 19–30. [Google Scholar] [CrossRef]
- Fu, M.; Tian, L.; Dong, G.; Du, R.; Zhou, P.; Wang, M. Modeling on Regional Atmosphere-Soil-Land Plant Carbon Cycle Dynamic System. Sustainability 2016, 8, 303. [Google Scholar] [CrossRef]
- Sainepo, B.M.; Gachene, C.K.; Karuma, A. Assessment of soil organic carbon fractions and carbon management index under different land use types in Olesharo Catchment, Narok County, Kenya. Carb Balance Manag. 2018, 13, 4. [Google Scholar] [CrossRef]
- Paul, O.O.; Sekhon, B.S.; Sharma, S. Spatial variability and simulation of soil organic carbon under different land use systems: Geostatistical approach. Agrofor. Syst. 2018, 93, 1389–1398. [Google Scholar] [CrossRef]
- Duan, L.; Li, Z.; Xie, H.; Yuan, H.; Li, Z.; Zhou, Q. Regional pattern of soil organic carbon density and its influence upon the plough layers of cropland. Land Degrad. Dev. 2020, 31, 2461–2474. [Google Scholar] [CrossRef]
- Long, J.; Liu, Y.; Xing, S.; Zhang, L.; Qu, M.; Qiu, L.; Huang, Q.; Zhou, B.; Shen, J. Optimal interpolation methods for farmland soil organic matter in various landforms of a complex topography. Ecol. Ind. 2020, 110, 105926. [Google Scholar] [CrossRef]
- Wu, Z.; Wang, B.; Huang, J.; An, Z.; Jiang, P.; Chen, Y.; Liu, Y. Estimating soil organic carbon density in plains using landscape metric-based regression Kriging model. Soil Tillage Res. 2019, 195, 104381. [Google Scholar] [CrossRef]
- Zhang, Y.; Ji, W.; Saurette, D.D.; Easher, T.H.; Li, H.; Shi, Z.; Adamchuk, V.I.; Biswas, A. Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging. Geoderma 2020, 366, 114253. [Google Scholar] [CrossRef]
- Cambardella, C.A.; Moorman, T.B.; Novak, J.M.; Parkin, T.B.; Karlen, D.L.; Turco, R.F.; Konopka, A.E. Field-Scale Variability of Soil Properties in Central Iowa Soils. Soil Sci. Soc. Am. J. 1994, 58, 1501–1511. [Google Scholar] [CrossRef]
- Boubehziz, S.; Khanchoul, K.; Benslama, M.; Benslama, A.; Marchetti, A.; Francaviglia, R.; Piccini, C. Predictive mapping of soil organic carbon in Northeast Algeria. Catena 2020, 190, 104539. [Google Scholar] [CrossRef]
- Webster, R.; Oliver, M. Geostatistics for Environmental Scientists; John Wiley & Sons: Chichester, UK, 2001. [Google Scholar]
- Stoner, E.R.; Baumgardner, M.F. Characteristic Variations in Reflectance of Surface soils. Soil Sci. Soc. Am. J. 1981, 45, 1161–1165. [Google Scholar] [CrossRef]
- Stevens, A.; Udelhoven, T.; Denis, A.; Tychon, B.; Lioy, R.; Hoffmann, L.; van Wesemael, B. Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy. Geoderma 2010, 158, 32–45. [Google Scholar] [CrossRef]
- Dalmolin, R.S.D.; Gonçalves, C.N.; Klamt, E.; Dick, D.P. Relação entre os constituintes do solo e seu comportamento espectral. Ciênc. Rural 2005, 35, 481–489. [Google Scholar] [CrossRef]
- Krishnan, P.; Alexander, J.D.; Butler, B.J.; Hummel, J.W. Reflectance Technique for Predicting Soil Organic-Matter. Soil Sci. Soc. Am. J. 1980, 44, 1282–1285. [Google Scholar] [CrossRef]
- Alabbas, A.H.; Swain, P.H.; Baumgardner, M.F. Relating Organic-Matter and Clay Content to Multispectral Radiance of Soils. Soil Sci. 1972, 114, 477–485. [Google Scholar] [CrossRef]
- Yan, Y.; Yang, J.; Li, B.; Qin, C.; Ji, W.; Xu, Y.; Huang, Y. High-Resolution Mapping of Soil Organic Matter at the Field Scale Using UAV Hyperspectral Images with a Small Calibration Dataset. Remote Sens. 2023, 15, 1433. [Google Scholar] [CrossRef]
- Wang, S.; Zhuang, Q.; Jin, X.; Yang, Z.; Liu, H. Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data. Remote Sens. 2020, 12, 1115. [Google Scholar] [CrossRef]
- Mallah Nowkandeh, S.; Noroozi, A.A.; Homaee, M. Estimating soil organic matter content from Hyperion reflectance images using PLSR, PCR, MinR and SWR models in semi-arid regions of Iran. Environ. Dev. 2018, 25, 23–32. [Google Scholar] [CrossRef]
- Chen, Y.; Li, Y.; Wang, X.; Wang, J.; Gong, X.; Niu, Y.; Liu, J. Estimating soil organic carbon density in Northern China’s agro-pastoral ecotone using vis-NIR spectroscopy. J. Soils Sediments 2020, 20, 3698–3711. [Google Scholar] [CrossRef]
- Cheng, B.; Jiang, Q.; Wang, K. Application and Progress in Estimating Soil Organic Matter Content Based on Remote Sensing. J. Shandong Agric. Univ. Nat. Sci. 2011, 42, 317–321. [Google Scholar]
- Wang, D.; Li, X.; Zou, D.; Wu, T.; Xu, H.; Hu, G.; Li, R.; Ding, Y.; Zhao, L.; Li, W.; et al. Modeling soil organic carbon spatial distribution for a complex terrain based on geographically weighted regression in the eastern Qinghai-Tibetan Plateau. Catena 2020, 187, 104399. [Google Scholar] [CrossRef]
- Mirchooli, F.; Kiani-Harchegani, M.; Khaledi Darvishan, A.; Falahatkar, S.; Sadeghi, S.H. Spatial distribution dependency of soil organic carbon content to important environmental variables. Ecol. Ind. 2020, 116, 106473. [Google Scholar] [CrossRef]
- Paul, S.S.; Dowell, L.; Coops, N.C.; Johnson, M.S.; Krzic, M.; Geesing, D.; Smukler, S.M. Tracking changes in soil organic carbon across the heterogeneous agricultural landscape of the Lower Fraser Valley of British Columbia. Sci. Total Environ. 2020, 732, 138994. [Google Scholar] [CrossRef]
- Zhou, Y.; Hartemink, A.E.; Shi, Z.; Liang, Z.Z.; Lu, Y.L. Land use and climate change effects on soil organic carbon in North and Northeast China. Sci. Total Environ. 2019, 647, 1230–1238. [Google Scholar] [CrossRef]
- Lu, W.; Lu, D.S.; Wang, G.X.; Wu, J.S.; Huang, J.Q.; Li, G.Y. Examining soil organic carbon distribution and dynamic change in a hickory plantation region with Landsat and ancillary data. Catena 2018, 165, 576–589. [Google Scholar] [CrossRef]
- Li, X.Y.; Shang, B.B.; Wang, D.Y.; Wang, Z.M.; Wen, X.; Kang, Y.D. Mapping soil organic carbon and total nitrogen in croplands of the Corn Belt of Northeast China based on geographically weighted regression kriging model. Comput. Geosci. 2020, 135, 104392. [Google Scholar] [CrossRef]
- Tan, X.; Guo, P.T.; Wu, W.; Li, M.F.; Liu, H.B. Prediction of soil properties by using geographically weighted regression at a regional scale. Soil Res. 2017, 55, 318–331. [Google Scholar] [CrossRef]
- Costa, E.M.; Tassinari, W.D.; Pinheiro, H.S.K.; Beutler, S.J.; dos Anjos, L.H.C. Mapping Soil Organic Carbon and Organic Matter Fractions by Geographically Weighted Regression. J. Environ. Qual. 2018, 47, 718–725. [Google Scholar] [CrossRef]
- Fotheringham, A.S.; Brunsdon, C.; Charlton, M.E. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships; Wiley: Chichester, UK, 2002. [Google Scholar]
- Zhang, Z.P.; Ding, J.L.; Wang, J.Z.; Ge, X.Y. Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices. Catena 2020, 185, 104257. [Google Scholar] [CrossRef]
- Camera, C.; Zomeni, Z.; Noller, J.S.; Zissimos, A.M.; Christoforou, I.C.; Bruggeman, A. A high resolution map of soil types and physical properties for Cyprus: A digital soil mapping optimization. Geoderma 2017, 285, 35–49. [Google Scholar] [CrossRef]
- Pudelko, A.; Chodak, M. Estimation of total nitrogen and organic carbon contents in mine soils with NIR reflectance spectroscopy and various chemometric methods. Geoderma 2020, 368, 114306. [Google Scholar] [CrossRef]
- Shifaw, E.; Sha, J.M.; Li, X.M.; Bao, Z.C.; Zhou, Z.L. An insight into land-cover changes and their impacts on ecosystem services before and after the implementation of a comprehensive experimental zone plan in Pingtan island, China. Land Use Policy 2019, 82, 631–642. [Google Scholar] [CrossRef]
- HJ/T 106-2004; Technical Specifications for Soil Environmental Monitoring. Ministry of Ecology and Environmentthe People’s Republic of China: Beijing, China, 2004.
- Zheng, G.; Li, L.; Chen, J.; Hu, F. Teaching research on determination of soil organic matter content in analytical chemistry experiment. Exp. Technol. Manag. 2018, 35, 203–207. [Google Scholar]
- Bao, S. Soil in Agricultural Chemistry, 3rd ed.; China Agriculture Press: Beijing, China, 2000. [Google Scholar]
- Silatsa, F.B.T.; Yemefack, M.; Tabi, F.O.; Heuvelink, G.B.M.; Leenaars, J.G.B. Assessing countrywide soil organic carbon stock using hybrid machine learning modelling and legacy soil data in Cameroon. Geoderma 2020, 367, 114260. [Google Scholar] [CrossRef]
- Rouse, J.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS; NASA Speceial Publication: New York, NY, USA, 1973; Volume 351, p. 309. [Google Scholar]
- Richardson, A.J.; Wiegand, C. Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sens. 1977, 43, 1541–1552. [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]
- Qi, J.; Chehbouni, A.; Huete, A.R.; Kerr, Y.H.; Sorooshian, S. A Modified Soil Adjusted Vegetation Index. Remote Sens. Environ. 1994, 48, 119–126. [Google Scholar] [CrossRef]
- Tang, Q.; Li, W.; Chen, W.; Wang, N. Roughness analysis of Luzhou City under different DEM resolutions. Rural Econ. Technol. 2017, 28, 17–19. [Google Scholar]
- Su, X.; Wie, W.H.; Guo, W.Q.; Wang, S.Y.; Wang, G.Y.; Wu, W.; Ye, W. Analyzing the impact of relief amplitude to loess landslides based on SRTM DEM in Tianshui Prefectur. Glac. Frozen Soil 2017, 39, 616–622. [Google Scholar]
- Moore, I.D. Soil attribute prediction using terrain analysis. Soil Sci. Soc. Am. J. 1993, 57, 443–452. [Google Scholar] [CrossRef]
- Yang, H.; He, N.; Li, S.; Yu, G.; Gao, Y.; Wang, R. Impact of Land Cover on Temperature and Moisture Sensitivity of Soil Organic Matter Mineralization in Subtropical Southeastern China. J. Res. Ecol. 2016, 7, 85–91. [Google Scholar]
- Qin, Z.H.; Zhang, M.; Arnon, K.; Pedro, B. Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data. Acta Geogr. Sin. 2001, 56, 446–456. [Google Scholar]
- Qin, Z.-H.; Li, W.J.; Zhang, M.-H.; Karnieli, A.; Berliner, P. Estimating of the essential atomospheric parameters of mono-window algorithm for land surface temperature retrieval from Landsat TM6. Remote Sens. Land Res. 2003, 15, 37–43. [Google Scholar]
- Song, C.; Qin, Z.; Wang, F. An effective method for LST decomposition based on the linear spectral mixing model. J. Infrared Millim. Waves 2015, 34, 497–504. [Google Scholar]
- Song, X.; Li, L.-D.; Kou, C.-L.; Chen, J. Soil nutrient distribution and its relations with topography in Huangshui River drainage basin. Chin. J. Appl. Ecol. 2011, 22, 3163–3168. [Google Scholar]
- Yang, Q.; Wu, W.; Liu, H. Prediction of spatial distribution of soil available iron in typical hilly farmland using terrain attributes and random forest model. Chin. J. Eco-Agric. 2018, 26, 422–431. [Google Scholar]
- Wang, J.F.; Li, X.H.; Christakos, G.; Liao, Y.L.; Zhang, T.; Gu, X.; Zheng, X.Y. Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China. Int. J. Geogr. Inf. Sci. 2010, 24, 107–127. [Google Scholar] [CrossRef]
- Wang, J.-F.; Hu, Y. Environmental health risk detection with GeogDetector. Environ. Model. Softw. 2012, 33, 114–115. [Google Scholar] [CrossRef]
- Rodríguez Martín, J.A.; Álvaro-Fuentes, J.; Gabriel, J.L.; Gutiérrez, C.; Nanos, N.; Escuer, M.; Ramos-Miras, J.J.; Gil, C.; Martín-Lammerding, D.; Boluda, R. Soil organic carbon stock on the Majorca Island: Temporal change in agricultural soil over the last 10 years. Catena 2019, 181, 104087. [Google Scholar] [CrossRef]
- Chen, L.; Ren, C.; Li, L.; Wang, Y.; Zhang, B.; Wang, Z.; Li, L. A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content. ISPRS Int. J. Geo-Inf. 2019, 8, 174. [Google Scholar] [CrossRef]
- Lark, R.M. Towards soil geostatistics. Spat. Stat. 2012, 1, 92–99. [Google Scholar] [CrossRef]
- Mitran, T.; Mishra, U.; Lal, R.; Rabisankar, T.; Sreenivas, K. Spatial distribution of soil carbon stocks in a semi-arid region of India. Geod. Reg. 2018, 15, e00192. [Google Scholar] [CrossRef]
- Gong, P.; Li, X.; Wang, J.; Bai, Y.; Chen, B.; Hu, T.; Liu, X.; Xu, B.; Yang, J.; Zhang, W.; et al. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sens. Environ. 2020, 236, 111510. [Google Scholar] [CrossRef]
Indices | Formulas | References |
---|---|---|
Normalized deviation vegetation index (NDVI) | [43] | |
Differential vegetation index (DVI) | [44] | |
Ratio vegetation index (RVI) | [45] | |
Modified soil adjusted vegetation index (MSAVI) | [46] | |
Roughness | [47] | |
Relief | [48] | |
Compound topographic index (CTI) | [49] | |
Stream power index (SPI) | [49] |
Indices | Forest | Grassland | Farmland | Construction | Water Body | Unused Land | Sum | User’s Accuracy |
---|---|---|---|---|---|---|---|---|
Forest | 120 | 5 | 3 | 2 | 0 | 0 | 130 | 92.31% |
Grassland | 4 | 48 | 2 | 1 | 0 | 0 | 55 | 87.27% |
Farmland | 5 | 6 | 80 | 9 | 0 | 0 | 100 | 80.00% |
Construction | 0 | 2 | 5 | 55 | 0 | 0 | 62 | 88.71% |
Waterbody | 1 | 0 | 0 | 0 | 35 | 0 | 36 | 97.22% |
Unused land | 1 | 0 | 2 | 3 | 1 | 30 | 37 | 81.08% |
Sum | 131 | 61 | 92 | 70 | 36 | 30 | 420 | - |
Producer’s Accuracy | 91.60% | 78.69% | 86.96% | 78.57% | 97.22% | 1 | - | - |
Item | Max | Min | Mean | Std. Dev | Kurtosis | Skewness | Coefficient of Variance | Distribution Type |
---|---|---|---|---|---|---|---|---|
SOM (%) | 4.35 | 0.07 | 1.03 | 0.95 | 4.69 | 2.12 | 0.91 | NN |
LOG (SOM) | 0.64 | −1.14 | −0.13 | 0.374 | 0.47 | −0.18 | 0.14 | ND |
Model | Nugget s(C0) | Sill s(C0 + C) | Range (km) | Nugget Effect (%) | Coefficients of Determination (R2) | Residual Sum of Squares (RSS) |
---|---|---|---|---|---|---|
Gaussian | 0.0001 | 0.1382 | 0.0294 | 0.1 | 0.622 | 8.103 × 10−³ |
linear | 0.082 | 0.155 | 0.104 | 52.8 | 0.242 | 0.0161 |
exponential | 0.0001 | 0.1442 | 0.057 | 0.1 | 0.565 | 9.525 × 10−³ |
spherical | 0.0001 | 0.1402 | 0.0430 | 0.1 | 0.590 | 8.760 × 10−³ |
Models | Levels | |||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
OK | 46 | 77 | 64 | 38 | 13 | 1 |
GWRK | 3364 | 6600 | 9230 | 6746 | 2212 | 849 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Fang, J.; Li, X.; Sha, J.; Dong, T.; Shang, J.; Shifaw, E.; Su, Y.-C.; Wang, J. Regional-Scale Topsoil Organic Matter Estimation Based on a Geographic Detector Model Using Landsat Data, Pingtan Island, Fujian, China. Sustainability 2023, 15, 8511. https://doi.org/10.3390/su15118511
Fang J, Li X, Sha J, Dong T, Shang J, Shifaw E, Su Y-C, Wang J. Regional-Scale Topsoil Organic Matter Estimation Based on a Geographic Detector Model Using Landsat Data, Pingtan Island, Fujian, China. Sustainability. 2023; 15(11):8511. https://doi.org/10.3390/su15118511
Chicago/Turabian StyleFang, Junjun, Xiaomei Li, Jinming Sha, Taifeng Dong, Jiali Shang, Eshetu Shifaw, Yung-Chih Su, and Jinliang Wang. 2023. "Regional-Scale Topsoil Organic Matter Estimation Based on a Geographic Detector Model Using Landsat Data, Pingtan Island, Fujian, China" Sustainability 15, no. 11: 8511. https://doi.org/10.3390/su15118511