2,499
Views
1
CrossRef citations to date
0
Altmetric
Articles

Mapping soil organic matter content using Sentinel-2 synthetic images at different time intervals in Northeast China

, , &
Pages 1094-1107 | Received 26 Dec 2022, Accepted 10 Mar 2023, Published online: 23 Mar 2023

References

  • Angelopoulou, T., N. Tziolas, A. Balafoutis, G. Zalidis, and D. Bochtis. 2019. “Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review.” Remote Sensing 11: 676. doi:10.3390/rs11060676
  • Bahureksa, W., M. M. Tfaily, R. M. Boiteau, R. B. Young, M. N. Logan, A. M. McKenna, and T. Borch. 2021. “Soil Organic Matter Characterization by Fourier Transform ion Cyclotron Resonance Mass Spectrometry (FTICR MS): A Critical Review of Sample Preparation, Analysis, and Data Interpretation.” Environmental Science & Technology 55: 9637–9656. doi:10.1021/acs.est.1c01135
  • Bao, Y., X. Meng, S. Ustin, X. Wang, X. Zhang, H. Liu, and H. Tang. 2020. “Vis-SWIR Spectral Prediction Model for Soil Organic Matter with Different Grouping Strategies.” CATENA 195: 104703. doi:10.1016/j.catena.2020.104703
  • Belgiu, M., and O. Csillik. 2018. “Sentinel-2 Cropland Mapping Using Pixel-Based and Object-Based Time-Weighted Dynamic Time Warping Analysis.” Remote Sensing of Environment 204: 509–523. doi:10.1016/j.rse.2017.10.005
  • Biney, J. K. M., R. Vašát, S. M. Bell, N. M. Kebonye, A. Klement, K. John, and L. Borůvka. 2022. “Prediction of Topsoil Organic Carbon Content with Sentinel-2 Imagery and Spectroscopic Measurements Under Different Conditions Using an Ensemble Model Approach with Multiple pre-Treatment Combinations.” Soil and Tillage Research 220: 105379. doi:10.1016/j.still.2022.105379
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45: 5–32. doi:10.1023/A:1010933404324
  • Chong, L., H.-J. Liu, L.-P. Lu, Z.-R. Liu, F.-C. Kong, and X.-L. Zhang. 2021. “Monthly Composites from Sentinel-1 and Sentinel-2 Images for Regional Major Crop Mapping with Google Earth Engine.” Journal of Integrative Agriculture 20: 1944–1957. doi:10.1016/S2095-3119(20)63329-9
  • Claverie, M., J. Ju, J. G. Masek, J. L. Dungan, E. F. Vermote, J.-C. Roger, S. V. Skakun, and C. Justice. 2018. “The Harmonized Landsat and Sentinel-2 Surface Reflectance Data set.” Remote Sensing of Environment 219: 145–161. doi:10.1016/j.rse.2018.09.002
  • Conforti, M., A. Castrignanò, G. Robustelli, F. Scarciglia, M. Stelluti, and G. Buttafuoco. 2015. “Laboratory-based Vis–NIR Spectroscopy and Partial Least Square Regression with Spatially Correlated Errors for Predicting Spatial Variation of Soil Organic Matter Content.” Catena 124: 60–67. doi:10.1016/j.catena.2014.09.004
  • Coppola, A. I., S. Wagner, S. T. Lennartz, M. Seidel, N. D. Ward, T. Dittmar, C. Santín, and M. W. Jones. 2022. “The Black Carbon Cycle and its Role in the Earth System.” Nature Reviews Earth & Environment 3: 516–532. doi:10.1038/s43017-022-00316-6
  • Cucchi, M., G. P. Weedon, A. Amici, N. Bellouin, S. Lange, H. Müller Schmied, H. Hersbach, and C. Buontempo. 2020. “WFDE5: Bias-Adjusted ERA5 Reanalysis Data for Impact Studies.” Earth System Science Data 12: 2097–2120. doi:10.5194/essd-12-2097-2020
  • Cutler, D. R., T. C. Edwards, Jr., K. H. Beard, A. Cutler, and K. T. Hess. 2007. “Random Forests for Classification in Ecology.” Ecology 88: 2783–2792. doi:10.1890/07-0539.1
  • Demattê, J. A. M., C. T. Fongaro, R. Rizzo, and J. L. Safanelli. 2018. “Geospatial Soil Sensing System (GEOS3): A Powerful Data Mining Procedure to Retrieve Soil Spectral Reflectance from Satellite Images.” Remote Sensing of Environment 212: 161–175. doi:10.1016/j.rse.2018.04.047
  • Dong, J., X. Xiao, M. A. Menarguez, G. Zhang, Y. Qin, D. Thau, C. Biradar, and B. Moore. 2016. “Mapping Paddy Rice Planting Area in Northeastern Asia with Landsat 8 Images, Phenology-Based Algorithm and Google Earth Engine.” Remote Sensing of Environment 185: 142–154. doi:10.1016/j.rse.2016.02.016
  • Dou, X., X. Wang, H. Liu, X. Zhang, L. Meng, Y. Pan, Z. Yu, and Y. Cui. 2019. “Prediction of Soil Organic Matter Using Multi-Temporal Satellite Images in the Songnen Plain, China.” Geoderma 356: 113896. doi:10.1016/j.geoderma.2019.113896
  • Drusch, M., U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, et al. 2012b. “Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services.” Remote Sensing of Environment 120: 25–36. doi:10.1016/j.rse.2011.11.026
  • Drusch, M., U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, C. Isola, P. Laberinti, and P. Martimort. 2012a. “Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services.” Remote Sensing of Environment 120: 25–36. doi:10.1016/j.rse.2011.11.026
  • Emilien, A.-V., C. Thomas, and H. Thomas. 2021. “UAV & Satellite Synergies for Optical Remote Sensing Applications: A Literature Review.” Science of Remote Sensing 3: 100019. doi:10.1016/j.srs.2021.100019
  • Gomes, L. C., R. M. Faria, E. de Souza, G. V. Veloso, C. E. G. Schaefer, and E. I. Fernandes Filho. 2019. “Modelling and Mapping Soil Organic Carbon Stocks in Brazil.” Geoderma 340: 337–350. doi:10.1016/j.geoderma.2019.01.007
  • Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. doi:10.1016/j.rse.2017.06.031
  • Griffiths, P., C. Nendel, and P. Hostert. 2019. “Intra-annual Reflectance Composites from Sentinel-2 and Landsat for National-Scale Crop and Land Cover Mapping.” Remote Sensing of Environment 220: 135–151. doi:10.1016/j.rse.2018.10.031
  • Hengl, T., J. M. de Jesus, R. A. MacMillan, N. H. Batjes, G. B. Heuvelink, E. Ribeiro, A. Samuel-Rosa, B. Kempen, J. G. Leenaars, and M. G. Walsh. 2014. “SoilGrids1km—Global Soil Information Based on Automated Mapping.” PloS one 9: e105992. doi:10.1371/journal.pone.0105992
  • Huang, H., Y. Chen, N. Clinton, J. Wang, X. Wang, C. Liu, P. Gong, et al. 2017. “Mapping Major Land Cover Dynamics in Beijing Using all Landsat Images in Google Earth Engine.” Remote Sensing of Environment 202: 166–176. doi:10.1016/j.rse.2017.02.021
  • Jin, Z., G. Azzari, C. You, S. Di Tommaso, S. Aston, M. Burke, and D. B. Lobell. 2019. “Smallholder Maize Area and Yield Mapping at National Scales with Google Earth Engine.” Remote Sensing of Environment 228: 115–128. doi:10.1016/j.rse.2019.04.016
  • John, K., Y. Bouslihim, K. I. Ofem, L. Hssaini, R. Razouk, P. B. Okon, I. A. Isong, P. C. Agyeman, N. M. Kebonye, and C. Qin. 2022. “Do Model Choice and Sample Ratios Separately or Simultaneously Influence Soil Organic Matter Prediction?” International Soil and Water Conservation Research 10: 470–486. doi:10.1016/j.iswcr.2021.11.003
  • Kulu, E. 2021. “Satellite Constellations-2021 Industry Survey and Trends.” 35th Annual Small Satellite Conference.
  • Lamichhane, S., L. Kumar, and B. Wilson. 2019. “Digital Soil Mapping Algorithms and Covariates for Soil Organic Carbon Mapping and Their Implications: A Review.” Geoderma 352: 395–413. doi:10.1016/j.geoderma.2019.05.031
  • Lin, H. 2011. “Three Principles of Soil Change and Pedogenesis in Time and Space.” Soil Science Society of America Journal 75: 2049–2070. doi:10.2136/sssaj2011.0130
  • Liu, S., H. Shen, S. Chen, X. Zhao, A. Biswas, X. Jia, Z. Shi, and J. Fang. 2019. “Estimating Forest Soil Organic Carbon Content Using vis-NIR Spectroscopy: Implications for Large-Scale Soil Carbon Spectroscopic Assessment.” Geoderma 348: 37–44. doi:10.1016/j.geoderma.2019.04.003
  • Luo, C., Y. Wang, X. Zhang, W. Zhang, and H. Liu. 2022a. “Spatial Prediction of Soil Organic Matter Content Using Multiyear Synthetic Images and Partitioning Algorithms.” CATENA 211: 106023. doi:10.1016/j.catena.2022.106023
  • Luo, C., X. Zhang, X. Meng, H. Zhu, C. Ni, M. Chen, and H. Liu. 2022b. “Regional Mapping of Soil Organic Matter Content Using Multitemporal Synthetic Landsat 8 Images in Google Earth Engine.” CATENA 209: 105842. doi:10.1016/j.catena.2021.105842
  • Luo, C., X. Zhang, Y. Wang, Z. Men, and H. Liu. 2022c. “Regional Soil Organic Matter Mapping Models Based on the Optimal Time Window, Feature Selection Algorithm and Google Earth Engine.” Soil and Tillage Research 219: 105325. doi:10.1016/j.still.2022.105325
  • Mahdianpari, M., B. Salehi, F. Mohammadimanesh, S. Homayouni, and E. Gill. 2019. “The First Wetland Inventory map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform.” Remote Sensing 11: 43. doi:10.3390/rs11010043
  • Mahmoudzadeh, H., H. R. Matinfar, R. Taghizadeh-Mehrjardi, and R. Kerry. 2020. “Spatial Prediction of Soil Organic Carbon Using Machine Learning Techniques in Western Iran.” Geoderma Regional 21: e00260. doi:10.1016/j.geodrs.2020.e00260
  • Masek, J. G., M. A. Wulder, B. Markham, J. McCorkel, C. J. Crawford, J. Storey, and D. T. Jenstrom. 2020. “Landsat 9: Empowering Open Science and Applications Through Continuity.” Remote Sensing of Environment 248: 111968. doi:10.1016/j.rse.2020.111968
  • Mendes, W.d.S., J. A. M. Demattê, N. E. Q. Silvero, and L. Rabelo Campos. 2021. “Integration of Multispectral and Hyperspectral Data to map Magnetic Susceptibility and Soil Attributes at Depth: A Novel Framework.” Geoderma 385: 114885. doi:10.1016/j.geoderma.2020.114885
  • Meng, X., Y. Bao, J. Liu, H. Liu, X. Zhang, Y. Zhang, P. Wang, H. Tang, and F. Kong. 2020. “Regional Soil Organic Carbon Prediction Model Based on a Discrete Wavelet Analysis of Hyperspectral Satellite Data.” International Journal of Applied Earth Observation and Geoinformation 89: 102111. doi:10.1016/j.jag.2020.102111
  • Meng, X., Y. Bao, H. Liu, X. Zhang, and X. Wang. 2022a. “A new Digital Soil Mapping Method with Temporal-Spatial-Spectral Information Derived from Multi-Source Satellite Images.” Geoderma 425: 116065. doi:10.1016/j.geoderma.2022.116065
  • Meng, X., Y. Bao, Y. Wang, X. Zhang, and H. Liu. 2022b. “An Advanced Soil Organic Carbon Content Prediction Model via Fused Temporal-Spatial-Spectral (TSS) Information Based on Machine Learning and Deep Learning Algorithms.” Remote Sensing of Environment 280: 113166. doi:10.1016/j.rse.2022.113166
  • Minasny, B., A. B. McBratney, and S. Salvador-Blanes. 2008. “Quantitative Models for Pedogenesis—A Review.” Geoderma 144: 140–157. doi:10.1016/j.geoderma.2007.12.013
  • Moura-Bueno, J. M., R. S. D. Dalmolin, A. ten Caten, A. C. Dotto, and J. A. Demattê. 2019. “Stratification of a Local VIS-NIR-SWIR Spectral Library by Homogeneity Criteria Yields More Accurate Soil Organic Carbon Predictions.” Geoderma 337: 565–581. doi:10.1016/j.geoderma.2018.10.015
  • NASA, J. 2020. NASADEM Merged DEM Global 1 arc Second V001 [Data Set]. NASA EOSDIS Land Processes DAAC. Accessed July 08, 2020. doi:10.5067/MEaSUREs/NASADEM.
  • Nelson, D. W., and L. Sommers. 2013. “A Rapid and Accurate Procedure for Estimation of Organic Carbon in Soils.” Proceedings of the Indiana Academy of Science 84: 456–462.
  • O’Kelly, B. C. 2004. “Accurate Determination of Moisture Content of Organic Soils Using the Oven Drying Method.” Drying Technology 22: 1767–1776. doi:10.1081/DRT-200025642
  • Ou, Y., A. N. Rousseau, L. Wang, and B. Yan. 2017. “Spatio-temporal Patterns of Soil Organic Carbon and pH in Relation to Environmental Factors—A Case Study of the Black Soil Region of Northeastern China.” Agriculture, Ecosystems & Environment 245: 22–31. doi:10.1016/j.agee.2017.05.003
  • Page, K. L., Y. P. Dang, and R. C. Dalal. 2020. “The Ability of Conservation Agriculture to Conserve Soil Organic Carbon and the Subsequent Impact on Soil Physical, Chemical, and Biological Properties and Yield.” Frontiers in Sustainable Food Systems 4: 31. doi:10.3389/fsufs.2020.00031
  • Poggio, L., L. M. De Sousa, N. H. Batjes, G. Heuvelink, B. Kempen, E. Ribeiro, and D. Rossiter. 2021. “SoilGrids 2.0: Producing Soil Information for the Globe with Quantified Spatial Uncertainty.” Soil 7: 217–240. doi:10.5194/soil-7-217-2021
  • Pouladi, N., A. B. Møller, S. Tabatabai, and M. H. Greve. 2019. “Mapping Soil Organic Matter Contents at Field Level with Cubist, Random Forest and Kriging.” Geoderma 342: 85–92. doi:10.1016/j.geoderma.2019.02.019
  • Qi, W., L. Feng, H. Yang, and J. Liu. 2021. “Spring and Summer Potential Flood Risk in Northeast China.” Journal of Hydrology: Regional Studies 38: 100951. doi:10.1016/j.ejrh.2021.100951
  • Qian, S.-E. 2021. “Hyperspectral Satellites, Evolution, and Development History.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 7032–7056. doi:10.1109/JSTARS.2021.3090256
  • Saptoro, A., M. O. Tadé, and H. Vuthaluru. 2012. “A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models.” Chemical Product and Process Modeling 7. doi:10.1515/1934-2659.1645
  • Shafizadeh-Moghadam, H., F. Minaei, H. Talebi-khiyavi, T. Xu, and M. Homaee. 2022. “Synergetic use of Multi-Temporal Sentinel-1, Sentinel-2, NDVI, and Topographic Factors for Estimating Soil Organic Carbon.” CATENA 212: 106077. doi:10.1016/j.catena.2022.106077
  • Silvero, N. E. Q., J. A. M. Demattê, M. T. A. Amorim, N.V.d. Santos, R. Rizzo, J. L. Safanelli, R. R. Poppiel, W. d. S. Mendes, and B. R. Bonfatti. 2021. “Soil Variability and Quantification Based on Sentinel-2 and Landsat-8 Bare Soil Images: A Comparison.” Remote Sensing of Environment 252: 112117. doi:10.1016/j.rse.2020.112117
  • Stevens, A., and L. Ramirez-Lopez. 2014. “An Introduction to the Prospectr Package.” R Package Vignette, Report No.: R Package Version 0.1 3.
  • Strobl, C., J. Malley, and G. Tutz. 2009. “An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests.” Psychological Methods 14: 323–348. doi:10.1037/a0016973
  • Svetnik, V., A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, and B. P. Feuston. 2003. “Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling.” Journal of Chemical Information and Computer Sciences 43: 1947–1958. doi:10.1021/ci034160g
  • Tajik, S., S. Ayoubi, and M. Zeraatpisheh. 2020. “Digital Mapping of Soil Organic Carbon Using Ensemble Learning Model in Mollisols of Hyrcanian Forests, Northern Iran.” Geoderma Regional 20: e00256. doi:10.1016/j.geodrs.2020.e00256
  • Teluguntla, P., P. S. Thenkabail, A. Oliphant, J. Xiong, M. K. Gumma, R. G. Congalton, K. Yadav, and A. Huete. 2018. “A 30-m Landsat-Derived Cropland Extent Product of Australia and China Using Random Forest Machine Learning Algorithm on Google Earth Engine Cloud Computing Platform.” Isprs Journal of Photogrammetry and Remote Sensing 144: 325–340. doi:10.1016/j.isprsjprs.2018.07.017
  • Tomppo, E., O. Antropov, and J. Praks. 2019. “Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment.” Remote Sensing 11: 2480. doi:10.3390/rs11212480
  • Wang, S., K. Guan, C. Zhang, D. Lee, A. J. Margenot, Y. Ge, J. Peng, W. Zhou, Q. Zhou, and Y. Huang. 2022a. “Using Soil Library Hyperspectral Reflectance and Machine Learning to Predict Soil Organic Carbon: Assessing Potential of Airborne and Spaceborne Optical Soil Sensing.” Remote Sensing of Environment 271: 112914. doi:10.1016/j.rse.2022.112914
  • Wang, X., L. Wang, S. Li, Z. Wang, M. Zheng, and K. Song. 2022b. “Remote Estimates of Soil Organic Carbon Using Multi-Temporal Synthetic Images and the Probability Hybrid Model.” Geoderma 425: 116066. doi:10.1016/j.geoderma.2022.116066
  • Wang, L., X. Wang, D. Wang, B. Qi, S. Zheng, H. Liu, C. Luo, H. Li, L. Meng, and X. Meng. 2021. “Spatiotemporal Changes and Driving Factors of Cultivated Soil Organic Carbon in Northern China’s Typical Agro-Pastoral Ecotone in the Last 30 Years.” Remote Sensing 13: 3607. doi:10.3390/rs13183607
  • Wu, X., R. Zhu, Y. Long, and W. Zhang. 2022. “Spatial Trend and Impact of Snowmelt Rate in Spring Across China’s Three Main Stable Snow Cover Regions Over the Past 40 Years Based on Remote Sensing.” Remote Sensing 14: 4176. doi:10.3390/rs14174176
  • Xiong, J., P. S. Thenkabail, M. K. Gumma, P. Teluguntla, J. Poehnelt, R. G. Congalton, K. Yadav, and D. Thau. 2017. “Automated Cropland Mapping of Continental Africa Using Google Earth Engine Cloud Computing.” Isprs Journal of Photogrammetry and Remote Sensing 126: 225–244. doi:10.1016/j.isprsjprs.2017.01.019
  • Yang, Y., R. Chen, G. Liu, Z. Liu, and X. Wang. 2022. “Trends and Variability in Snowmelt in China Under Climate Change.” Hydrology and Earth System Sciences 26: 305–329. doi:10.5194/hess-26-305-2022
  • You, N., and J. Dong. 2020. “Examining Earliest Identifiable Timing of Crops Using all Available Sentinel 1/2 Imagery and Google Earth Engine.” ISPRS Journal of Photogrammetry and Remote Sensing 161: 109–123. doi:10.1016/j.isprsjprs.2020.01.001
  • You, N., J. Dong, J. Huang, G. Du, G. Zhang, Y. He, T. Yang, Y. Di, and X. Xiao. 2021. “The 10-m Crop Type Maps in Northeast China During 2017–2019.” Scientific Data 8: 41. doi:10.1038/s41597-021-00827-9
  • Zhou, T., Y. Geng, J. Chen, J. Pan, D. Haase, and A. Lausch. 2020. “High-resolution Digital Mapping of Soil Organic Carbon and Soil Total Nitrogen Using DEM Derivatives, Sentinel-1 and Sentinel-2 Data Based on Machine Learning Algorithms.” Science of The Total Environment 729: 138244. doi:10.1016/j.scitotenv.2020.138244