Skip to main content
SpringerLink
Log in

Area-to-point regression Kriging approach fusion of Landsat 8 OLI and Sentinel 2 data for assessment of soil macronutrients at Anaimalai, Coimbatore

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Spectral indices-based soil prediction models derived from multispectral datasets are too intricate in terms of accuracy as well as resolution. Complications arise while incorporating multispectral datasets for regional-scale spatial assessment of soil macronutrients. Sporadically satellite image fusion techniques have been used for soil nutrient interpolation to circumvent the complications. The fusion of multispectral bands encompasses precise soil information that cannot be observed as accurate with single satellite dataset. In this study, fusion of near infrared regions of Landsat 8 Operational Land Imager and Sentinel 2 has been observed for its contribution on soil macronutrient assessments. Area-to-point regression Kriging (ATPRK) approach is followed in fusing the two satellite imagery and in situ soil spectral have used for the validation of the resultant. Comparative statistical analysis on Landsat 8 OLI band 5 (wavelength: 845–885 nm), Sentine-2 band 8,8A (wavelength: 785–900 nm) datasets and fused satellite bands provides R2 values of 0.8209, 0.8436, and 0.8763 respectively. Regression models y = (0.25006 ± 0.00754) + (0.0000313)x, y = (0.25252 ± 0.0062) + (0.0000810)x, and y = (0.23715 ± 0.0062) + (0.0001210)x for nitrogen, phosphorus, and potassium respectively aids for soil macronutrient interpolation and assessments. Computations reveals the ranges of nitrogen, phosphorus and potassium that floats from 48 to 295 kg/ha, 5.0 to 37 kg/ha, and 32 to 455 kg/ha in the study area. Fusion of satellite imagery by ATPRK approaches in soil macronutrient study at regional scale brings the novelty of the study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Adiri, Z., Lhissou, R., El Harti, A., Jellouli, A., & Chakouri, M. (2020). Recent advances in the use of public domain satellite imagery for mineral exploration: A review of Landsat-8 and Sentinel-2 applications. Ore Geology Reviews, 117, 103332. https://doi.org/10.1016/j.oregeorev.2020.103332

    Article  Google Scholar 

  • Armannsson, S. E., Ulfarsson, M. O., Sigurdsson, J., Nguyen, H. V., & Sveinsson, J. R. (2021). A comparison of optimized Sentinel-2 super-resolution methods using wald’s protocol and Bayesian optimization. Remote Sensing, 13(11), 2192. https://doi.org/10.3390/rs13112192

    Article  Google Scholar 

  • Bolton, D. K., Gray, J. M., Melaas, E. K., Moon, M., Eklundh, L., & Friedl, M. A. (2020). Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. Remote Sensing of Environment, 240, 111685. https://doi.org/10.1016/j.rse.2020.111685

    Article  Google Scholar 

  • Brede, B., Verrelst, J., Gastellu-Etchegorry, J. P., Clevers, J. G., Goudzwaard, L., Den Ouden, J., Verbesselt, J., & Herold, M. (2020). Assessment of workflow feature selection on forest LAI prediction with sentinel-2A MSI, landsat 7 ETM+ and Landsat 8 OLI. Remote Sensing, 12(6), 915. https://doi.org/10.3390/rs12060915

    Article  Google Scholar 

  • Chen, B., Jing, L., & Yufang, J. (2021). Deep learning for feature-level data fusion: Higher resolution reconstruction of historical Landsat archive Remote Sensing, 13(2), 167. https://doi.org/10.3390/rs13020167

  • Dar, A., Zahir, Z. A., Iqbal, M., Mehmood, A., Javed, A., Hussain, A., & Ahmad, M. (2021). Efficacy of rhizobacterial exopolysaccharides in improving plant growth, physiology, and soil properties. Environmental Monitoring and Assessment, 193(8), 1–15. https://doi.org/10.1007/s10661-021-09286-6

    Article  CAS  Google Scholar 

  • Demelash Beyene, M. (2021). Crop Field Classification using fusion approach of unmanned aerial vehicle (UAV) and Sentinel 2A satellite data: The case of Oda Dhawata Kebele Cluster farmland, Oromia Region, Ethiopia (Doctoral dissertation, Addis Ababa University). http://hdl.handle.net/123456789/3517

  • ED Chaves, M., CA Picoli, M., & D. Sanches, I. (2020). Recent applications of Landsat 8/OLI and Sentinel-2/MSI for land use and land cover mapping: A systematic review. Remote Sensing, 12(18), 3062. https://doi.org/10.3390/rs12183062

  • Elbasiouny, H., Elbehiry, F., El-Ramady, H., & Brevik, E. C. (2020). Phosphorus availability and potential environmental risk assessment in alkaline soils. Agriculture, 10(5), 172. https://doi.org/10.3390/agriculture10050172

    Article  CAS  Google Scholar 

  • Guimarães, C. C. B., Demattê, J. A., de Azevedo, A. C., Dalmolin, R. S. D., ten Caten, A., Sayão, V. M., da Silva, R. C., Poppiel, R. R., de Sousa Mendes, W., Salazar, D. F. U., & e Souza, A. B. (2021). Soil weathering behavior assessed by combined spectral ranges: Insights into aggregate analysis. Geoderma, 402, 115154. https://doi.org/10.1016/j.geoderma.2021.115154

    Article  CAS  Google Scholar 

  • Gujre, N., Agnihotri, R., Rangan, L., Sharma, M. P., & Mitra, S. (2021). Deciphering the dynamics of glomalin and heavy metals in soils contaminated with hazardous municipal solid wastes. Journal of Hazardous Materials, 416, 125869. https://doi.org/10.1016/j.jhazmat.2021.125869

    Article  CAS  Google Scholar 

  • Jagadala, K., & Sahoo, J. P. (2020). Critical limit of boron in acid laterite soil for cultivation of sunflower (Helianthus annus L.). IJCS, 8(3), 2510–2513. https://doi.org/10.22271/chemi.2020.v8.i3aj.9588

  • Jewan, S. Y. Y., Pagay, V., Billa, L., Tyerman, S. D., Gautam, D., Sparkes, D., Gautam, D., Sparkes, D., Chai, H.H., & Singh, A. (2021). The feasibility of using a low-cost near-infrared, sensitive, consumer-grade digital camera mounted on a commercial UAV to assess Bambara groundnut yield. International Journal of Remote Sensing, 1-31. https://doi.org/10.1080/01431161.2021.1974116

  • Kaman, S., & Makandar, A. (2021). Remote sensing of satellite images using digital image processing techniques: A survey. International Research Journal of Modernization in Engineering Technology and Science, 3(1). Retrieved July 7, 2021, from https://www.irjmets.com/uploadedfiles/paper/volume_3/issue_7_july_2021/15221/final/fin_irjmets1632817612.pdf

  • Komolafe, A. A., Olorunfemi, I. E., Oloruntoba, C., & Akinluyi, F. O. (2021). Spatial prediction of soil nutrients from soil, topography and environmental attributes in the northern part of Ekiti State, Nigeria. Remote Sensing Applications: Society and Environment, 21, 100450. https://doi.org/10.1016/j.rsase.2020.100450

    Article  Google Scholar 

  • Leno, N., Sudharmaidevi, C. R., Byju, G., Thampatti, K. C. M., Krishnaprasad, P. U., Jacob, G., & Gopinath, P. P. (2021). Thermochemical digestate fertilizer from solid waste: Characterization, labile carbon dynamics, dehydrogenase activity, water holding capacity and biomass allocation in banana. Waste Management, 123, 1–14. https://doi.org/10.1016/j.wasman.2021.01.002

    Article  CAS  Google Scholar 

  • Li, R., Xu, M., Chen, Z., Gao, B., Cai, J., Shen, F., He, X., Zhuang, Y., & Chen, D. (2021). Phenology-based classification of crop species and rotation types using fused MODIS and Landsat data: The comparison of a random-forest-based model and a decision-rule-based model. Soil and Tillage Research, 206, 104838. https://doi.org/10.1016/j.still.2020.104838

    Article  Google Scholar 

  • Lin, C., Zhu, A. X., Wang, Z., Wang, X., & Ma, R. (2020). The refined spatiotemporal representation of soil organic matter based on remote images fusion of Sentinel-2 and Sentinel-3. International Journal of Applied Earth Observation and Geoinformation, 89, 102094. https://doi.org/10.1016/j.jag.2020.102094

    Article  Google Scholar 

  • Liu, X., & Wang, M. (2020). Super-resolution of VIIRS-measured ocean color products using deep convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 59(1), 114–127. https://doi.org/10.1109/TGRS.2020.2992912

    Article  Google Scholar 

  • Miran, N., Rasouli Sadaghiani, M. H., Feiziasl, V., Sepehr, E., Rahmati, M., & Mirzaee, S. (2021). Predicting soil nutrient contents using Landsat OLI satellite images in rain-fed agricultural lands, northwest of Iran. Environmental Monitoring and Assessment, 193(9), 1–12. https://doi.org/10.1007/s10661-021-09397-0

    Article  CAS  Google Scholar 

  • Moravec, D., Komárek, J., López-Cuervo Medina, S., & Molina, I. (2021). Effect of atmospheric corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV sensors. Remote Sensing, 13(18), 3550. https://doi.org/10.3390/rs13183550

    Article  Google Scholar 

  • Odebiri, O., Mutanga, O., Odindi, J., Naicker, R., Masemola, C., & Sibanda, M. (2021). Deep learning approaches in remote sensing of soil organic carbon: A review of utility, challenges, and prospects. Environmental Monitoring and Assessment, 193(12), 1–18. https://doi.org/10.1007/s10661-021-09561-6

    Article  Google Scholar 

  • Ozsahin, E., & Ozdes, M. (2022). Agricultural land suitability assessment for agricultural productivity based on GIS modeling and multi-criteria decision analysis: The case of Tekirdağ province. Environmental Monitoring and Assessment, 194(41), 1–19. https://doi.org/10.1007/s10661-021-09663-1

    Article  Google Scholar 

  • Papenfus, M., Schaeffer, B., Pollard, A. I., & Loftin, K. (2020). Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for US lakes and reservoirs. Environmental Monitoring and Assessment, 192(12), 1–22. https://doi.org/10.1007/s10661-020-08631-5

    Article  CAS  Google Scholar 

  • Prada, M., Cabo, C., Hernández-Clemente, R., Hornero, A., Majada, J., & Martínez-Alonso, C. (2020). Assessing canopy responses to thinnings for sweet chestnut coppice with time-series vegetation indices derived from landsat-8 and sentinel-2 imagery. Remote Sensing, 12(18), 3068. https://doi.org/10.3390/rs12183068

    Article  Google Scholar 

  • Priem, F., Jilge, M., Heiden, U., Somers, B., & Canters, F. (2021, March). Towards a generic spectral library for urban mapping applications. In EARSeL Joint Workshop 2021 Liège (pp. 53–54). EARSeL. https://cris.vub.be/ws/portalfiles/portal/70696865/Abstract_Book_Earsel_Liege_2021.pdf

  • Rajah, P., Odindi, J., & Mutanga, O. (2018). Feature level image fusion of optical imagery and synthetic aperture radar (SAR) for invasive alien plant species detection and mapping. Remote Sensing Applications: Society and Environment, 10, 198–208. https://doi.org/10.1016/j.rsase.2018.04.007

    Article  Google Scholar 

  • Rathore, V. S., Nathawat, M. S., & Ray, P. C. (2008). Influence of neotectonic activity on groundwater salinity and playa development in the Mendha river catchment, western India. International Journal of Remote Sensing, 29(13), 3975–3986. https://doi.org/10.1080/01431160801891861

    Article  Google Scholar 

  • Scheffler, D., Frantz, D., & Segl, K. (2020). Spectral harmonization and red edge prediction of Landsat-8 to Sentinel-2 using land cover optimized multivariate regressors. Remote Sensing of Environment, 241, 111723. https://doi.org/10.1016/j.rse.2020.111723

    Article  Google Scholar 

  • Shen, H., Meng, X., & Zhang, L. (2016). An integrated framework for the spatio–temporal–spectral fusion of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7135–7148. https://doi.org/10.1109/TGRS.2016.2596290

    Article  Google Scholar 

  • Shao, Z., Cai, J., Fu, P., Hu, L., & Liu, T. (2019). Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product. Remote Sensing of Environment, 235, 111425. https://doi.org/10.1016/j.rse.2019.111425

    Article  Google Scholar 

  • Shrestha, B., Ahmad, S., & Stephen, H. (2021). Fusion of Sentinel-1 and Sentinel-2 data in mapping the impervious surfaces at city scale. Environmental Monitoring and Assessment, 193(9), 1–21. https://doi.org/10.1007/s10661-021-09321-6

    Article  Google Scholar 

  • Shrivastava, A., Nayak, C. K., Dilip, R., Samal, S. R., Rout, S., & Ashfaque, S. M. (2021). Automatic robotic system design and development for vertical hydroponic farming using IoT and big data analysis. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.07.294

  • Song, X. P., Huang, W., Hansen, M. C., & Potapov, P. (2021). An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping. Science of Remote Sensing, 3, 100018. https://doi.org/10.1016/j.still.2020.104838

    Article  Google Scholar 

  • Stokes, M. A. (1996). An introduction to tree-ring dating. University of Arizona Press.

    Google Scholar 

  • Taddia, Y., Russo, P., Lovo, S., & Pellegrinelli, A. (2020). Multispectral UAV monitoring of submerged seaweed in shallow water. Applied Geomatics, 12(1), 19–34. https://doi.org/10.1007/s12518-019-00270-x

    Article  Google Scholar 

  • Wang, Q., Blackburn, G. A., Onojeghuo, A. O., Dash, J., Zhou, L., Zhang, Y., & Atkinson, P. M. (2017). Fusion of Landsat 8 OLI and Sentinel-2 MSI data. IEEE Transactions on Geoscience and Remote Sensing, 55(7), 3885–3899. https://doi.org/10.1109/TGRS.2017.2683444

    Article  Google Scholar 

  • Wang, Q., Wang, L., Wei, C., Jin, Y., Li, Z., Tong, X., & Atkinson, P. M. (2021). Filling gaps in Landsat ETM+ SLC-off images with Sentinel-2 MSI images. International Journal of Applied Earth Observation and Geoinformation, 101, 102365. https://doi.org/10.1016/j.jag.2021.102365

    Article  Google Scholar 

  • Wiyantoko, B., Maulidatunnisa, V., & Purbaningtias, T. E. (2021, September). Method performance of K2O analysis in flake potassium fertilizer using flame photometer. In AIP Conference Proceedings (Vol. 2370, No. 1, p. 030008). AIP Publishing LLC. https://doi.org/10.1063/5.0062537

  • Xu, Y., Fan, H., & Dang, L. (2021). Monitoring coal seam fires in Xinjiang using comprehensive thermal infrared and time series InSAR detection. International Journal of Remote Sensing, 42(6), 2220–2245. https://doi.org/10.1080/01431161.2020.1823045

    Article  Google Scholar 

  • Zhang, Y., Ling, F., Wang, X., Foody, G. M., Boyd, D. S., Li, X., Du, Y., & Atkinson, P. M. (2021). Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record. Remote Sensing of Environment, 261, 112470. https://doi.org/10.1016/j.rse.2021.112470

    Article  Google Scholar 

  • Zheng, H., Zhou, X., He, J., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2020). Early season detection of rice plants using RGB, NIR-GB and multispectral images from unmanned aerial vehicle (UAV). Computers and Electronics in Agriculture, 169, 105223. https://doi.org/10.1016/j.compag.2020.105223

    Article  Google Scholar 

  • Zhou, J., Qiu, Y., Chen, J., & Chen, X. (2021). A geometric misregistration resistant data fusion approach for adding red-edge (RE) and short-wave infrared (SWIR) bands to high spatial resolution imagery. Science of Remote Sensing, 100033. https://doi.org/10.1016/j.srs.2021.100033

Download references

Acknowledgements

We the authors grateful to support in the form of fellowship and encouragement received from SRM Institute of Science and Technology, Kattankulathur. It is pleasure to extend the acknowledgment to Vickram Muthu Rathinasabari for their valuable field assistance.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, India

    Dhayalan Vaithiyanathan & Karuppasamy Sudalaimuthu

Authors
  1. Dhayalan Vaithiyanathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karuppasamy Sudalaimuthu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Dhayalan V: Literature review, experimental design, analyzed, and interpreted the data Karuppasamy Sudalaimuthu: statistical analysis with graphical outcomes, conceptualization, drafting, and supervision.

Corresponding author

Correspondence to Karuppasamy Sudalaimuthu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vaithiyanathan, D., Sudalaimuthu, K. Area-to-point regression Kriging approach fusion of Landsat 8 OLI and Sentinel 2 data for assessment of soil macronutrients at Anaimalai, Coimbatore. Environ Monit Assess 194, 916 (2022). https://doi.org/10.1007/s10661-022-10571-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-022-10571-1

Keywords

Search

Navigation