Improving model performance in mapping black-soil resource with machine learning methods and multispectral features

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Improving model performance in mapping black-soil resource with machine learning methods and multispectral features
  • Liu, X. B. et al. Overview of Mollisols in the world: distribution, land use and management. Can. J. Soil Sci., 92(3). (2012).

  • FAO. global status of black soils[R].2022.

  • Suchodoletz, H. V. et al. Distribution of Chernozems and phaeozems in Central Germany during the neolithic period. Quatern. Int. 511, 166–184 (2019).

    Article 
    MATH 

    Google Scholar 

  • Zhang, G., Long, H. & Yang, F. Understanding the formation time of black soils. Innov. Geosci. 1(1), 100010 (2023).

    Article 
    MATH 

    Google Scholar 

  • Li, R. et al. Soil degradation: a global threat to sustainable use of black soils. Pedosphere, (2024).

  • Wang, W., Deng, X. & Yue, H. Black soil conservation will boost China’s grain supply and reduce agricultural greenhouse gas emissions in the future. Environ. Impact Assess. Rev. 106, 107482 (2024).

    Article 

    Google Scholar 

  • Li, X. et al. Soil quality assessment of croplands in the black soil zone of Jilin Province, China: establishing a minimum data set model. Ecol. Ind. 107, 105251 (2019).

    Article 
    CAS 
    MATH 

    Google Scholar 

  • Li, X. et al. Dynamic evaluation of cropland degradation risk by combining multi-temporal remote sensing and geographical data in the Black Soil Region of Jilin Province, China. Appl. Geogr. 154, 102920 (2023).

    Article 
    MATH 

    Google Scholar 

  • Singh, S. et al. Remote sensing applications in soil survey and mapping; a review. Int. J. Geomatics Geosci. 7(2), 192–203 (2016).

    MATH 

    Google Scholar 

  • Stumpf, F. et al. Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping. (2016).

  • Mohammad, R. K. et al. Legacy soil maps as a covariate in digital soil mapping: a case study from Northern Iran. Geoderma: Int. J. Soil. Sci., 279. (2016).

  • Wang, J. L. et al. Balanced fertilization over four decades has sustained soil microbial communities and improved soil fertility and rice productivity in red paddy soil. Sci. Total Environ. 793, 148664 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Wang, L. et al. Improvement of data imbalance for digital soil class mapping in Eastern China. Comput. Electron. Agric. 214, 108322 (2023).

    Article 
    MATH 

    Google Scholar 

  • Sahadevan, A. S., Lyngdoh, R. B. & Ahmad, T. Multi-label sub-pixel classification of red and black soil over sparse vegetative areas using AVIRIS-NG airborne hyperspectral image. Remote Sens. Appl.: Soc. Environ. 29, 100884 (2023).

    Article 
    MATH 

    Google Scholar 

  • Poppiel, R. R. et al. High resolution middle eastern soil attributes mapping via open data and cloud computing. Geoderma 385, 114890 (2021).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Swain, S. R. et al. Estimation of soil texture using Sentinel-2 multispectral imaging data: an ensemble modeling approach. Soil Tillage. Res. 213, 105134 (2021).

    Article 
    MATH 

    Google Scholar 

  • Neyestani, M. Digital mapping of soil classes using spatial extrapolation with imbalanced data. Geoderma Reg. 26, e00422 (2021).

    Article 

    Google Scholar 

  • Baumgardner, M. F. et al. Reflectance properties of soils. Adv. Agron. 38, 1–44 (1986).

    Article 
    MATH 

    Google Scholar 

  • Atemkeng, C. C. et al. Inverse radiative transfer problem for soil properties retrieval from bidirectional reflectance measurements. Results Opt. 11, 100409 (2023).

    Article 
    MATH 

    Google Scholar 

  • BenDor, E., Irons, J. R. & Epema, G. F. Soil Reflectance. remote sensing for the earth sciences manual of remote sensing, (1999).

  • Jin, X. et al. Remote estimation of soil organic matter content in the Sanjiang Plain, Northest China: the optimal band algorithm versus the GRA-ANN model. Agric. For. Meteorol., 218–219 :250–260. (2016).

  • Wang, S. et al. Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: assessing potential of airborne and spaceborne optical soil sensing. Remote Sens. Environ. 271, 112914 (2022).

    Article 
    MATH 

    Google Scholar 

  • Luo, C. et al. Mapping soil organic matter content using Sentinel-2 synthetic images at different time intervals in Northeast China. Int. J. Digit. Earth. 16(1), 1094–1107 (2023).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Lin, L. et al. A new method for multicolor determination of organic matter in moist soil. CATENA 207, 105611 (2021).

    Article 
    CAS 
    MATH 

    Google Scholar 

  • Luo, C. et al. Regional mapping of soil organic matter content using multitemporal synthetic landsat 8 images in google earth engine (CATENA, 2022).

  • Meng, X. T. et al. SOC content of global mollisols at a 30 m spatial resolution from 1984 to 2021 generated by the novel ML-CNN prediction model. Remote Sens. Environ. 300, 113911 (2024).

    Article 
    MATH 

    Google Scholar 

  • Dou, X., Wang, X., Liu, H., Zhang, X. & Cui, Y. Prediction of soil organic matter using multi-temporal Satellite Images in the Songnen Plain, China356 (An International Journal of Soil Science, 2019).

  • Vaudour, E. et al. The impact of acquisition date on the prediction performance of topsoil organic carbon from sentinel-2 for croplands. Remote Sens., 2019(18).

  • Luo, C. et al. Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine. J. Integr. Agric. 20(7), 1944–1957 (2021).

    Article 
    MATH 

    Google Scholar 

  • Luo, C. et al. Mapping of soil organic matter in a typical black soil area using Landsat-8 synthetic images at different time periods. CATENA 231, 107336 (2023).

    Article 
    CAS 
    MATH 

    Google Scholar 

  • Yang, H. et al. Hyper-temporal remote sensing data in bare soil period and terrain attributes for digital soil mapping in the black soil regions of China. CATENA 184, 104259 (2020).

    Article 
    MATH 

    Google Scholar 

  • Meng, X. et al. A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images. Geoderma 425, 116065 (2022).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Dou, P. et al. Remote sensing image classification using an ensemble framework without multiple classifiers. ISPRS J. Photogrammetry Remote Sens. 208, 190–209 (2024).

    Article 
    MATH 

    Google Scholar 

  • Poggio, L. et al. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. Soil, 7. (2021).

  • Xu, X. et al. A remote sensing-based strategy for mapping potentially toxic elements of soils: temporal-spatial-spectral covariates combined with random forest. Environ. Res. 240, 117570 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Zhang, S. et al. Assessing soil thickness in a black soil watershed in northeast China using random forest and field observations. Int. Soil. Water Conserv. Res. 9 (1), 49–57 (2021).

    Article 
    MATH 

    Google Scholar 

  • Taghizadeh-Mehrjardi, R. et al. High-performance soil class delineation via UMAP coupled with machine learning in Kurdistan Province, Iran. Geoderma Reg. 36, e00754 (2024).

    Article 

    Google Scholar 

  • Westhuizen, V. S. & Heuvelink, G. B. M. Hofmeyr. D.P. Multivariate random forest for digital soil mapping. Geoderma 431, 116365 (2023).

    Article 
    MATH 

    Google Scholar 

  • Bi, W. et al. Evolution characteristics of groundwater level and its relation to low-carbon development in southern horqin sandy land, China. Energy Procedia. 152, 809–814 (2018).

    Article 
    MATH 

    Google Scholar 

  • Yuechao, Z. & Kun, L. I. Discussion on the land scale management in Kangping County (Agricultural Science&Technology and Equipment, 2010).

  • Yao, Y. et al. Exchangeable Ca2 + content and soil aggregate stability control the soil organic carbon content in degraded Horqin grassland. Ecol. Ind. 134, 108507 (2022).

    Article 
    CAS 
    MATH 

    Google Scholar 

  • Cui, X. et al. Estimating and mapping the dynamics of soil salinity under different crop types using Sentinel-2 satellite imagery. Geoderma 440, 116738 (2023).

    Article 
    ADS 
    CAS 
    MATH 

    Google Scholar 

  • Zhou, T. et al. 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. Sci. Total Environ. 729, 138244 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Djukic, I. et al. Soil organic-matter Stocks and Characteristics along an Alpine Elevation Gradient (Journal of Plant Nutrition and Soil Science, 2010).

  • Onwuka, B. Effects of soil temperature on Some Soil properties and plant growth. (2016).

  • Cheng, Y. et al. The effect of soil water content and erodibility on losses of available nitrogen and phosphorus in simulated freeze-thaw conditions. CATENA 166, 21–33 (2018).

    Article 
    CAS 
    MATH 

    Google Scholar 

  • Masek, J. G. et al. A Harmonized landsat-sentinel-2 surface reflectance product: a resource for agricultural monitoring (AGU Fall Meeting Abstracts, 2015).

  • Sripada, R. P. et al. Aerial color infrared photography for determining late-season nitrogen requirements in corn. Agron. J. 97 (5), 1511–1514 (2005).

    Article 
    MATH 

    Google Scholar 

  • Riggs, G. A., Hall, D. K. & Salomonson, V. V. A snow index for the landsat thematic mapper and moderate resolution imaging spectroradiometer. IEEE International Geoscience and Remote Sensing Symposium, 1994, 8–12 Aug. 1994. (1994).

  • Huete, A. R. A soil-adjusted Vegetation Index (SAVI) (Remote Sensing of Environment, 1988).

  • Yao, B. et al. Spatiotemporal variation and GeoDetector analysis of NDVI at the northern foothills of the Yinshan Mountains in Inner Mongolia over the past 40 years. Heliyon 10(20), 39309 (2024).

    Article 

    Google Scholar 

  • Birth, G. S. & Mcvey, G. R. Measuring the color of growing turf with a reflectance spectrophotometer. Agron. J. 60(6), 640–643 (1968).

    Article 
    MATH 

    Google Scholar 

  • Liang, Y. I. Study on dynamic change of Yuli oasis plant cover based on RDVI. J. Arid Land. Resour. Environ. 18(6), 66–71 (2004).

    MATH 

    Google Scholar 

  • Luciano, S. et al. Water and nitrogen effects on active canopy sensor vegetation indices. Agron. J. 103(6), 1815 (2011).

    Article 
    MATH 

    Google Scholar 

  • Bin, L. I. et al. Comparative study on the correlations between NDVI, NDMI and LST (Progress in Geography, 2017).

  • Chen, X. L. et al. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 104(2), 133–146 (2006).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Shashikanta, S. S. et al. Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level (Geocarto International, 2015).

  • Sharma, J. et al. Development of a new vegetation modulated soil moisture index for the spatial disaggregation of SMAP soil moisture data product135103594 (Physics and Chemistry of the Earth, 2024).

  • Hateffard, F., Steinbuch, L. & Heuvelink, G. B. M. Evaluating the extrapolation potential of random forest digital soil mapping. Geoderma 441, 116740 (2024).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Breiman, L. Random Forests (Machine Learning, 2001).

  • Deng, F. et al. Union with recursive feature elimination: a feature selection framework to improve the classification performance of multicategory causes of death in colorectal cancer. Lab. Invest. 104(3), 100320 (2024).

    Article 
    PubMed 
    MATH 

    Google Scholar 

  • Tang, Y. et al. Research on the optimization of sample point placement for ground substrate survey based on interpretable machine learning, IEEE, 2023. (2023).

  • Wang, Y. & Li, Y. Mapping the ratoon rice suitability region in China using random forest and recursive feature elimination modeling. Field Crops Res. 301, 109016 (2023).

    Article 
    ADS 
    MATH 

    Google Scholar 

  • Deng, X. et al. An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf. Sci. 340–341, 250–261 (2016).

    Article 
    MATH 

    Google Scholar 

  • Godbole, S. & Sarawagi, S. Discriminative methods for multi-labeled classification: Pacific-Asia conference on knowledge discovery and data mining, (2004).

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