SpringerLink
Log in

Data Fusion in a Data-Rich Era

  • Chapter
  • First Online:
Precision Agriculture: Modelling

Part of the book series: Progress in Precision Agriculture ((PRPRA))

  • 1044 Accesses

Abstract

The application of precision agriculture requires the estimation of space-time variability at a very fine scale. A very wide variety of both remote and proximal sensors is used to supplement the sparse information from sampling. However, this poses problems for the joint analysis of an often huge amount of data with different characteristics. Geostatistical principles to data fusion of heterogeneous spatial data are illustrated. A detailed geostatistical approach to data fusion is also described followed by application to a real case. Finally, in remote sensing, the SAR/optical data fusion techniques will be shown highlighting their uniqueness. In the last decade, SAR/optical data fusion has gained a new impetus, mainly due to two important developments: firstly, the increasing availability of imagery with high spatial and spectral resolution and the global map** of the Earth’s surface; secondly, the implementation of new international space missions with the launch of SAR and optical sensors such as ESA’s Copernicus program.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    PAN image: a single greyscale image.

  2. 2.

    MS image: a colour image with typical 4–12 bands.

  3. 3.

    HS image: a colour image with typical >100–1000 bands.

  4. 4.

    https://e2l-coop.eu/en/projects/h2020-sensagri/

  5. 5.

    https://ipl.uv.es/sensagri/ftp/DELIVERABLES/WP4/SENSAGRI_D4_9_v1.0.pdf

References

  • Adamchuk, V. I., Viscarra Rossel, R. A., Marx, D. B., & Samal, A. K. (2011). Using targeted sampling to process multivariate soil sensing data. Geoderma, 163, 63–73.

    Article  Google Scholar 

  • Atkinson, P. M., & Jeganathan, C. (2010). Estimating the local small support semivariogram for use in superresolution map**. In P. M. Atkinson & C. D. Lloyd (Eds.), geoENV VII—geostatistics for environmental applications (pp. 279–294). Springer.

    Chapter  Google Scholar 

  • Atkinson, P. M., & Tate, N. J. (2000). Spatial scale problems and geostatistical solutions: A review. The Professional Geographer, 52(4), 607–623.

    Article  Google Scholar 

  • Babaeian, E., Sidike, P., Newcomb, M. S., Maimaitijiang, M., White, S. A., Demieville, J., Ward, R. W., Sadeghi, M., LeBauer, D. S., Jones, S. B., Sagan, V., & Tuller, M. (2019). A new optical remote sensing technique for high resolution map** of soil moisture. Frontiers in Big Data, 2, 37.

    Article  Google Scholar 

  • Best, N. G., Ickstadt, K., & Wolpert, R. L. (2000). Spatial Poisson regression for health and exposure data measured at disparate resolutions. Journal of the American Statistical Association, 95, 1076–1088. https://doi.org/10.1080/01621459.2000.10474304

    Article  Google Scholar 

  • Brus, D. J., Bogaert, P., & Heuvelink, G. B. M. (2008). Bayesian maximum entropy prediction of soil categories using a traditional soil map as soft information. European Journal of Soil Science, 59(2), 166e177.

    Article  Google Scholar 

  • Buttafuoco, G., Quarto, R., Quarto, F., Conforti, M., Venezia, A., Vitti, C., & Castrignanò, A. (2019). A geophysical and spectrometric sensor data fusion approach for homogeneous within-field zone delineation. In J. V. Stafford (Ed.), Precision agriculture ’19 (pp. 705–712). Academic.

    Chapter  Google Scholar 

  • Buttafuoco, G., Quarto, R., Quarto, F., et al. (2021). Taking into account change of support when merging heterogeneous spatial data for field partition. Precision Agriculture, 22, 586–607. https://doi.org/10.1007/s11119-020-09781-9

    Article  Google Scholar 

  • Castaldi, F. F., Pelosi, F., Pascucci, S., & Casa, R. (2017). Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agriculture, 18, 76–94.

    Article  Google Scholar 

  • Castanedo, F. (2013). A review of data fusion techniques. Scientific World Journal. https://doi.org/10.1155/2013/704504

  • Castrignanò, A., & Buttafuoco, G. (2020). Chapter 3: Data processing. In A. Castrignanò, G. Buttafuoco, R. Khosla, A. M. Mouazen, D. Moshou, & O. Naud (Eds.), Agricultural Internet of Things and decision support for precision smart farming (1st ed., pp. 139–182). Academic. ISBN:978-0-12-818373-1.

    Chapter  Google Scholar 

  • Castrignanò, A., Giugliarini, L., Risaliti, R., & Martinelli, N. (2000). Study of spatial relationships among soil physical-chemical properties using Multivariate Geostatistics. Geoderma, 97, 39–60.

    Article  Google Scholar 

  • Castrignanò, A., Costantini, E. A. C., Barbetti, R., & Sollitto, D. (2009). Accounting for extensive topographic and pedologic secondary information to improve soil map**. Catena. https://doi.org/10.1016/j.catena.2008.12.004

  • Castrignanò, A., Wong, M. T. F., Stelluti, M., De Benedetto, D., & Sollitto, D. (2012). Use of EMI, gamma-ray emission and GPS height as multi-sensor data for soil characterisation. Geoderma, 175–176, 78–89.

    Article  Google Scholar 

  • Castrignanò, A., Buttafuoco, G., Quarto, R., Vitti, C., Langella, G., Terribile, F., & Venezia, A. (2017). A combined approach of sensor data fusion and multivariate geostatistics for delineation of homogeneous zones in an agricultural field. Sensors (Switzerland). https://doi.org/10.3390/s17122794

  • Castrignanò, A., Buttafuoco, G., Quarto, R., Parisi, D., Viscarra Rossel, R. A., Terribile, F., Langella, G., & Venezia, A. (2018). A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture. Catena. https://doi.org/10.1016/j.catena.2018.05.011

  • Castrignanò, A., Quarto, R., Venezia, A., & Buttafuoco, G. (2019). A comparison between mixed support kriging and block cokriging for modelling and combining spatial data with different support. Precision Agriculture. https://doi.org/10.1007/s11119-018-09630-w

  • Castrignanò, A., Belmonte, A., Antelmi, I., Quarto, R., Quarto, F., Shaddad, S., Sion, V., Muolo, M. R., Ranieri, N. A., Gadaleta, G., Bartoccetti, E., Riefolo, C., Ruggieri, S., & Nigro, F. (2020). A geostatistical fusion approach using UAV data for probabilistic estimation of Xylella fastidiosa subsp. pauca infection in olive trees. Science of The Total Environment, 752, ISSN 0048-9697. https://doi.org/10.1016/j.scitotenv.2020.141814

    Article  CAS  Google Scholar 

  • Chang, C. Y., Zhou, R., Kira, O., Marri, S., Skovira, J., Gu, L., & Sun, Y. (2020). An Unmanned Aerial System (UAS) for concurrent measurements of solar induced chlorophyll fluorescence and hyperspectral reflectance toward improving crop monitoring. Agricultural and Forest Meteorology, 294, 1–15.

    Article  Google Scholar 

  • Chilès, J. P., & Delfiner, P. (2012). Geostatistics: Modeling spatial uncertainty (2nd ed.). Wiley. https://doi.org/10.1002/9781118136188

    Book  Google Scholar 

  • Conforti, M., Castrignanò, A., Robustelli, G., Scarciglia, F., Stelluti, M., & Buttafuoco, G. (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.

    Article  CAS  Google Scholar 

  • Corwin, D. L., & Scudiero, E. (2016). Field-scale apparent soil electrical conductivity. In S. Logsdon (Ed.), Methods of soil analysis (pp. 1–29). Soil Science Society of America. https://doi.org/10.2136/methods-soil.2015.0038

    Chapter  Google Scholar 

  • Cressie, N. (1996). Change of support and the modifiable areal unit problem. Geographical Systems, 3, 159–180.

    Google Scholar 

  • Cressie, N., Shi, T., & Kang, E. (2010). Fixed rank filtering for spatial-temporal data. Journal of Computational and Graphical Statistics, 19(3), 724–745.

    Article  Google Scholar 

  • De Benedetto, D., Castrignanò, A., Rinaldi, M., Ruggieri, S., Santoro, F., Figorito, B., Gualano, S., Diacono, M., & Tamborrino, R. (2013a). An approach for delineating homogeneous zones by using multi-sensor data. Geoderma. https://doi.org/10.1016/j.geoderma.2012.08.028

  • De Benedetto, D., Castrignano, A., Diacono, M., Rinaldi, M., Ruggieri, S., & Tamborrino, R. (2013b). Field partition by proximal and remote sensing data fusion. Biosystems Engineering. https://doi.org/10.1016/j.biosystemseng.2012.12.001

  • De Benedetto, D., Quarto, R., Castrignanò, A., & Palumbo, D. A. (2015). Impact of data processing and antenna frequency on spatial structure modelling of GPR data. Sensors (Switzerland), 15, 16430–16447. https://doi.org/10.3390/s150716430

    Article  Google Scholar 

  • Evans, R. G., Han, S., Kroeger, M. W., & Schneider, S. M. (1996). Precision center pivot irrigation for efficient use of water and nitrogen. In P. C. Robert, R. H. Rust, & W. E. Larson (Eds.), Proceedings of the 3rd international conference (pp. 75–84). ASA/CSSA/SSSA. https://doi.org/10.2134/1996.precisionagproc3.c8

    Chapter  Google Scholar 

  • Fernández-Quintanilla, C., Peña, J. M., Andújar, D., Dorado, J., Ribeiro, A., & López-Granados, F. (2018). Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops? Weed Research, 58, 259–272.

    Article  Google Scholar 

  • Gherboudj, I., Magagi, R., Berg, A. A., & Toth, B. (2011). Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data. Remote Sensing of Environment, 115(1), 33–43. ISSN:0034-4257.

    Article  Google Scholar 

  • Goodman, J. W. (1976). Some fundamental properties of speckle, JOSA. Optical Society of America, 66(11), 1145–1150.

    Article  Google Scholar 

  • Goovaerts, P. (2008). Kriging and semivariogram deconvolution in the presence of irregular geographical units. Mathematical Geology, 40(1), 101–128.

    Google Scholar 

  • Grose, D. J., Harris, R., Brundson, C., & Kilham, D. (2007). Grid enabling geographically weighted regression. In Proceedings of the 3rd international conference on e-Social Science, Ann Arbor.

    Google Scholar 

  • Hammerling, D. M., Michalak, A. M., O’Dell, C., & Kawa, S. R. (2012). Global CO2 distributions over land from the greenhouse gases observing satellite (GOSAT). Geophysical Research Letters, 39, L08804. https://doi.org/10.1029/2012GL051203

    Article  CAS  Google Scholar 

  • Huang, H. (2005). Depth of investigation for small broadband electromagnetic sensors. Geophysics, 70, G135–G142.

    Article  Google Scholar 

  • Huang, H. C., Cressie, N., & Gabrosek, J. (2002). Fast resolution-consistent spatial prediction of global processes from satellite data. Journal of Computational and Graphical Statistics, 11, 1–26.

    Article  CAS  Google Scholar 

  • Huang, W., Lu, J., Ye, H., Kong, W. A., Mortimer, H., & Shi, Y. (2018). Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis. International Journal of Agricultural and Biological Engineering, 11, 145–151.

    Article  Google Scholar 

  • Jackson, J. E. (2003). User’s guide to principal components. Wiley.

    Google Scholar 

  • Jagalingam, P., & Hegde, A. V. (2015). A review of quality metrics for fused image. In International conference on water resources, coastal and ocean engineering (pp. 133–142).

    Google Scholar 

  • Jones, N. (2014). Computer science: The learning machines. Nature, 505(7482), 146–148, 1.

    Article  CAS  Google Scholar 

  • Kelsall, J., & Wakefield, J. (2002). Modeling spatial variation in disease risk: A geostatistical approach. Journal of the American Statistical Association, 97, 692–701. https://doi.org/10.2307/3085705

    Article  Google Scholar 

  • Keys, R. (1982). Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29, 1153–1160. https://doi.org/10.1109/TASSP.1981.1163711

    Article  Google Scholar 

  • King, G. (1997). A solution to the ecological inference problem. Princeton University Press.

    Google Scholar 

  • Knipper, K. R., Kustas, W. P., Anderson, M. C., Alfieri, J. G., Prueger, J. H., Hain, C. R., Gao, F., Yang, Y., McKee, L. G., Nieto, H., Hipps, L. E., Mar Alsina, M., & Sanchez, L. (2019). Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrigation Science, 37, 431–449.

    Article  Google Scholar 

  • Laben, C. A., & Brower, B. V. (2000). Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. US Patent 6,011,875.

    Google Scholar 

  • Lajaunie, C., & Wackernagel, H. (2000). Geostatistical approaches to change of support problems: Theoretical framework (IMPACT Project Deliverable Nr 19, Technical Report N–30/01/G). Centre de Géostatistique, Ecole des Mines de Paris.

    Google Scholar 

  • Lu, D., Li, G., Moran, E., Dutra, L., & Batistella, M. (2011). A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. GIScience & Remote Sensing, 48, 345–370. https://doi.org/10.2747/1548-1603.48.3.345

    Article  Google Scholar 

  • Mairal, J., Elad, M., & Sapiro, G. (2008). Sparse representation for color image restoration. IEEE Transactions on Image Processing, 17(1), 53–69. https://doi.org/10.1109/TIP.2007.911828

    Article  Google Scholar 

  • Manzione, R. L., & Castrignanò, A. (2019). A geostatistical approach for multi-source data fusion to predict water table depth. Science of the Total Environment, 696, 133763. https://doi.org/10.1016/j.scitotenv.2019.133763

    Article  CAS  Google Scholar 

  • Meyer, Y. (1990). Ondelettes et operateurs I: Ondelettes. Hermann, 215 pp.

    Google Scholar 

  • Mohammed, G. H., Colombo, R., Middleton, E. M., Rascher, U., van der Tole, C., Nedbald, L., Goulas, Y., Pérez-Priego, O., Damm, A., Meroni, M., et al. (2019). Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. Remote Sensing of Environment, 231, 1–39.

    Article  Google Scholar 

  • Muazen, A. B., Alexandridis, T., Buddenbaum, H., Cohen, Y., Moshou, D., Mulla, D., Nawar, S., & Sudduth, A. (2020). Chapter 2: Monitoring. In A. Castrignanò, G. Buttafuoco, R. Khosla, A. M. Mouazen, D. Moshou, & O. Naud (Eds.), Agricultural Internet of Things and decision support for precision smart farming (1st ed., pp. 35–138). Academic. ISBN:978-0-12-818373-1.

    Chapter  Google Scholar 

  • Mulla, D. J. (2017). Spatial variability in precision agriculture. In S. Shashi, H. **ong, & X. Zhou (Eds.), Encyclopedia of GIS (pp. 2118–2125). Springer.

    Chapter  Google Scholar 

  • Nagasubramanian, K., Jones, S., Singh, A. K., Sarkar, S., Singh, A., & Ganapathysubramanian, B. (2019). Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods, 15, 1–10.

    Article  Google Scholar 

  • Neteler, M., & Mitasova, H. (2008). Open source GIS: A GRASS GIS approach (3rd ed.). Kluwer Academic Publishers/Springer.

    Book  Google Scholar 

  • Nguyen, H., Cressie, N., & Braverman, A. (2012). Spatial statistical data fusion for remote sensing applications. Journal of the American Statistical Association, 107(499), 1004–1018.

    Article  CAS  Google Scholar 

  • Nowatzki, J., Andres, R., & Kyllo, K. (2004). Agricultural Remote Sensing Basics. NDSU Extension Service Publication. Available online: www.ag.ndsu.nodak.edu. Accessed 23 Sept 2020

    Google Scholar 

  • Olea, R. A. (Ed.). (1991). Geostatistical glossary and multilingual dictionary. Oxford University Press.

    Google Scholar 

  • Oliver, M. A., & Webster, R. (1989). A geostatistical bases for spatial weighting in multivariate classification. Mathematical Geology, 21(1), 15–35.

    Article  Google Scholar 

  • Openshaw, S., & Taylor, P. (1979). A million or so correlation coefficients. In N. Wrigley (Ed.), Statistical methods in the spatial sciences (pp. 127–144). Pion.

    Google Scholar 

  • Palazzi, V., Bonafoni, S., Alimenti, F., Mezzanotte, P., & Roselli, L. (2019). Feeding the world with microwaves: How remote and wireless sensing can help precision agriculture. IEEE Microwave Magazine, 20(12), 72–86.

    Article  Google Scholar 

  • Pardo-Iguzquiza, E., Chica-Olmo, M., & Atkinson, P. M. (2006). Downscaling cokriging for image sharpening. Remote Sensing of Environment, 102(1–2), 86–98.

    Article  Google Scholar 

  • Patrício, D., & Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture, 153, 69–81. https://doi.org/10.1016/j.compag.2018.08.001

    Article  Google Scholar 

  • Piella, G. (2009). Image fusion for enhanced visualization: A variational approach. International Journal of Computer Vision, 83, 1–11.

    Article  Google Scholar 

  • Pohl, C., & Genderen, J. L. V. (1998). Multisensor image fusion in remote sensing: Concepts, methods, and applications. International Journal of Remote Sensing, 19(5), 823–854.

    Article  Google Scholar 

  • Riefolo, C., Castrignanò, A., Colombo, C., Conforti, M., Vitti, C., & Buttafuoco, G. (2019). Investigation of soil surface organic and inorganic carbon contents in a low-intensity farming system using laboratory visible and near-infrared spectroscopy. Archives of Agronomy and Soil Science, 66(10), 1436–1448. https://doi.org/10.1080/03650340.2019.1674446

    Article  CAS  Google Scholar 

  • Rivoirard, J. (2001). Which models for collocated cokriging? Mathematical Geology, 332, 117–131.

    Article  Google Scholar 

  • Sishodia, R., Ray, R., & Singh, S. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12, 3136. https://doi.org/10.3390/rs12193136

    Article  Google Scholar 

  • Souissi, B., & Ouarzeddine, M. (2016). Polarimetric SAR data correction and terrain topography measurement based on the radar target orientation angle. Journal of the Indian Society of Remote Sensing, 44, 335–349. https://doi.org/10.1007/s12524-015-0493-x

    Article  Google Scholar 

  • Sudduth, K. A., Kitchen, N. R., Wiebold, W. J., Batchelor, W. D., Bollero, G. A., Bullock, D. G., Clay, D. G., Palm, H. L., Pierce, F. J., Schuler, R. T., & Thelen, K. D. (2005). Relating apparent electrical conductivity to soil properties across the north-central USA. Computers and Electronics in Agriculture, 46, 263e283.

    Article  Google Scholar 

  • Teke, M., Deveci, H. S., Haliloglu, O., Gürbüz, S. Z., & Sakarya, U. (2013). A short survey of hyperspectral remote sensing applications in agriculture. In Proceedings of the 2013 6th international conference on Recent Advances in Space Technologies (RAST), Istanbul, Turkey (pp. 171–176).

    Google Scholar 

  • Urban, D. L. (2004). Multivariate analysis: Nonhierarchical agglomeration, course notes, multivariate methods for environmental applications. Nicholas School of the Environment and Earth Sciences at Duke University. [online]: www.env.duke.edu/landscape/classes/env358/mv_pooling.pdf. Accessed 21 Jan 2005

    Google Scholar 

  • Viscarra Rossel, R. A., Adamchuk, V. I., Sudduth, K. A., McKenzie, N. J., & Lobsey, C. (2011). Proximal soil sensing. An effective approach for soil measurements in space and time. Advances in Agronomy, 113, 237–282. https://doi.org/10.1016/B978-0-12-386473-4.00010-5

    Article  Google Scholar 

  • Wackernagel, H. (2003). Multivariate geostatistics: An introduction with applications. Springer. ISBN:13:9783540441427.

    Book  Google Scholar 

  • Wald, L., Ranchin, T., & Mangolini, M. (1997). Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogrammetric Engineering and Remote Sensing, 63, 691–699.

    Google Scholar 

  • Wang, Z., Ziou, D., Armenakis, C., Li, D., & Li, Q. (2005). A comparative analysis of image fusion methods. IEEE Transactions on Geoscience and Remote Sensing, 43(6), 1391–1402.

    Article  Google Scholar 

  • Wikle, C. K. (2010). Low-rank representation for spatial processes. In A. E. Gelfand, P. Diggle, P. Guttorp, & M. Fuentes (Eds.), Handbook of spatial statistics (pp. 107–118). CRC Press.

    Chapter  Google Scholar 

  • Wong, D. W. S. (1996). Aggregation effects in geo-referenced data. In D. Griffiths (Ed.), Advanced spatial statistics (pp. 83–106). CRC Press.

    Google Scholar 

  • Yoo, E. H., & Kyriakidis, P. C. (2009). Area-to-point kriging in spatial hedonic pricing models. Journal of Geographical Systems, 11(4), 381–406.

    Article  Google Scholar 

  • Young, L. J., & Gotway, C. A. (2007). Linking spatial data from different sources: The effect of change of support. Stochastic Environmental Research and Risk Assessment, 21, 589–600.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., González-Dugo, M. V., & Fereres, E. (2016). Seasonal stability of chlorophyll fluorescence quantified from airborne hyperspectral imagery as an indicator of net photosynthesis in the context of precision agriculture. Remote Sensing of Environment, 179, 89–103.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Camino, C., Beck, P. S. A., Calderon, R., Hornero, A., Hernández-Clemente, R., Kattenborn, T., Montes-Borrego, M., Susca, L., Morelli, M., et al. (2018). Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4, 432–439.

    Article  CAS  Google Scholar 

  • Zhang, J. (2010). Multi-source remote sensing data fusion: Status and trends. International Journal of Image and Data Fusion, 1, 5–24. https://doi.org/10.1080/19479830903561035

    Article  Google Scholar 

  • Zhu, X., Cai, F., Tian, J., & Williams, T. K. A. (2018). Spatiotemporal fusion of multisource remote sensing data: Literature survey, taxonomy, principles, applications, and future directions. Remote Sensing, 10, 527.

    Article  Google Scholar 

Download references

Conflict of Interest

The authors declare that there are no conflicts of interest.

Author information

Authors and Affiliations

  1. Gabriele D’Annunzio University of Chieti – Pescara, Chieti Scalo, (CH), Italy

    Annamaria Castrignanò

  2. CNR-IREA National Research Council—Institute for Electromagnetic Sensing of the Environment, Bari, Italy

    Antonella Belmonte

Authors
  1. Annamaria Castrignanò
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonella Belmonte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Annamaria Castrignanò .

Editor information

Editors and Affiliations

  1. Department of Agroecology, iClimate, Centre for Circular Bioeconomy (CBIO), Aarhus University, Tjele, Denmark

    Davide Cammarano

  2. Agrosystems Research, Wageningen University & Research, Wageningen, The Netherlands

    Frits K. van Evert

  3. Agrosystems Research, Wageningen University & Research, Wageningen, The Netherlands

    Corné Kempenaar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Castrignanò, A., Belmonte, A. (2023). Data Fusion in a Data-Rich Era. In: Cammarano, D., van Evert, F.K., Kempenaar, C. (eds) Precision Agriculture: Modelling. Progress in Precision Agriculture. Springer, Cham. https://doi.org/10.1007/978-3-031-15258-0_7

Download citation

Publish with us

Policies and ethics

Search

Navigation