Skip to main content
SpringerLink
Log in

Data Assimilation of Remote Sensing Data into a Crop Growth Model

  • Chapter
  • First Online:
Precision Agriculture: Modelling

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

Abstract

Data assimilation (DA) is the overarching term for an ensemble of techniques to combine all possible information (models, observations, a priori data and statistics) to obtain the best possible estimate of the state of a system (Zhang & Moore, 2015). Data assimilation has its origins in meteorology and found its way into operational weather forecasting, oceanography and hydrology, but it is also a valuable technique for estimating variables related to crop growth (soil moisture, LAI (leaf area index), biomass, etc.) by combining models and observations of crop variables.

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

Access this chapter

Log in via an institution
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 shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

APSIM:

agricultural production system simulator

CSM :

crop simulation model

DA :

data assimilation

DSSAT :

decision support system for agrotechnology

EVI :

enhanced vegetation index

FAO-WRSI :

Food and Agriculture Organization-water requirement satisfaction index

FAPAR :

fraction of absorbed photosynthetically active radiation

IOT :

Internet of Things

LAI :

leaf area index

NDVI :

normalized difference vegetation index

PAR :

photosynthetically active radiation

RS :

remote sensing

VI :

vegetative index

WDVI :

weighted difference vegetation index

WOFOST :

world food studies

References

  • Aggarwal, P., Shirsath, P., Vyas, S., Arumugam, P., Goroshi, S., Aravind, S., et al. (2020). Application note: Crop-loss assessment monitor–A multi-model and multi-stage decision support system. Computers and Electronics in Agriculture, 175, 105619.

    Article  Google Scholar 

  • Baret, F., Houles, V., & Guerif, M. (2007). Quantification of plant stress using remote sensing observations and crop models: The case of nitrogen management. Journal of Experimental Botany, 58, 869–880. https://doi.org/10.1093/jxb/erl231

    Article  CAS  Google Scholar 

  • Batchelor, W. D., Jones, J. W., Boote, K. J., & Pinnschmidt, H. O. (1993). Extending the use of crop models to study pest damage. Transactions of the ASABE, 36, 551–558.

    Article  Google Scholar 

  • Bouman, B. A. M. (1995). Crop modelling and remote sensing for yield prediction. Netherlands Journal of Agricultural Science, 43, 143–161. https://doi.org/10.18174/njas.v43i2.573

    Article  Google Scholar 

  • Clevers, J., Kooistra, L., & van den Brande, M. (2017). Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sensing, 9(5). https://doi.org/10.3390/rs9050405

  • Clevers, J. G. P. W. (1991). Application of the WDVI in estimating LAI at the generative stage of barley. ISPRS Journal of Photogrammetry and Remote Sensing, 46(1), 37–47. https://doi.org/10.1016/0924-2716(91)90005-G

    Article  Google Scholar 

  • de Wit, A. J. W., & van Diepen, C. A. (2007). Crop model data assimilation with the ensemble Kalman filter for improving regional crop yield forecasts. Agricultural and Forest Meteorology, 146(1–2), 38–56. https://doi.org/10.1016/j.agrformet.2007.05.004

    Article  Google Scholar 

  • Donatelli, M., Magarey, R. D., Bregaglio, S., Willocquet, L., Whish, J. P. M., & Savary, S. (2017). Modelling the impacts of pests and diseases on agricultural systems. Agricultural Systems, 155, 213–224. https://doi.org/10.1016/j.agsy.2017.01.019

    Article  CAS  Google Scholar 

  • Dorigo, W. A., Zurita-Milla, R., de Wit, A. J. W., Brazile, J., Singh, R., & Schaepman, M. E. (2007). A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9(2), 165–193. https://doi.org/10.1016/j.jag.2006.05.003

    Article  Google Scholar 

  • Evensen, G. (2003). The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dynamics, 53, 343–367. https://doi.org/10.1007/s10236-003-0036-9

    Article  Google Scholar 

  • Evers, J. B., van der Werf, W., Stomph, T. J., Bastiaans, L., & Anten, N. P. R. (2019). Understanding and optimizing species mixtures using functional-structural plant modelling. Journal of Experimental Botany, 29;70(9), 2381–2388. https://doi.org/10.1093/jxb/ery288

  • Fang, H., Liang, S., & Hoogenboom, G. (2011). Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation. International Journal of Remote Sensing, 32(4), 1039–1065. https://doi.org/10.1080/01431160903505310

    Article  Google Scholar 

  • Fischer, A., Kergoat, L., & Dedieu, G. (2009). Coupling satellite data with vegetation functional models: Review of different approaches and perspectives suggested by the assimilation strategy. Remote Sensing Reviews, 15(1–4), 283–303. https://doi.org/10.1080/02757259709532343

    Article  Google Scholar 

  • Florin, M. J., McBratney, A. B., Whelan, B. M., & Minasny, B. (2010). Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm. Precision Agriculture, 12(3), 421–438. https://doi.org/10.1007/s11119-010-9184-3

    Article  Google Scholar 

  • Folberth, C., Elliott, J., Muller, C., Balkovic, J., Chryssanthacopoulos, J., Izaurralde, R. C., Jones, C. D., Khabarov, N., Liu, W., Reddy, A., Schmid, E., Skalsky, R., Yang, H., Arneth, A., Ciais, P., Deryng, D., Lawrence, P. J., Olin, S., Pugh, T. A. M., Ruane, A. C., & Wang, X. (2019). Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble. PLoS One, 14(9), e0221862. https://doi.org/10.1371/journal.pone.0221862

    Article  CAS  Google Scholar 

  • Frère, M., & Popov, G. (1986). Early agrometeorological crop yield forecasting. The Food and Agriculture Organization of the United Nations.

    Google Scholar 

  • Galelli, S., Gandolfi, C., Soncini-Sessa, R., & Agostani, D. (2010). Building a metamodel of an irrigation district distributed-parameter model. Agricultural Water Management, 97(2), 187–200. https://doi.org/10.1016/j.agwat.2009.09.007

    Article  Google Scholar 

  • Gumma, M. K., Kadiyala, M. D. M., Panjala, P., Ray, S. S., Akuraju, V. R., Dubey, S., Smith, A. P., Das, R., & Whitbread, A. M. (2021). Assimilation of remote sensing data into crop growth model for yield estimation: A case study from India. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-021-01341-6

  • Hebbar, K. B., Venugopalan, M. V., Seshasai, M. V. R., Rao, K. V., Patil, B. C., Prakash, A. H., et al. (2008). Predicting cotton production using Infocrop-cotton simulation model, remote sensing and spatial agro-climatic data. Current Science, 1570–1579.

    Google Scholar 

  • Huang, J., Gómez-Dans, J. L., Huang, H., Ma, H., Wu, Q., Lewis, P. E., Liang, S., Chen, Z., Xue, J.-H., Wu, Y., Zhao, F., Wang, J., & Xie, X. (2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, 276–277. https://doi.org/10.1016/j.agrformet.2019.06.008

  • Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G., & Wang, J. (2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92, 141–152. https://doi.org/10.1016/j.eja.2017.11.002

    Article  Google Scholar 

  • Jonard, F., Bogena, H., Caterina, D., Garré, S., Klotzsche, A., Monerris, A., Schwank, M., & von Hebel, C. (2019). Ground-based soil moisture determination. In Observation and measurement of ecohydrological processes (pp. 29–70). Ecohydrology. https://doi.org/10.1007/978-3-662-48297-1_2

    Chapter  Google Scholar 

  • Jones, J. W., et al. (1998). Decision support system for agrotechnology transfer: DSSAT v3. In G. Y. Tsuji, G. Hoogenboom, & P. K. Thornton (Eds.), Understanding options for agricultural production. Systems approaches for sustainable agricultural development (Vol. 7). Springer. https://doi.org/10.1007/978-94-017-3624-4_8

    Chapter  Google Scholar 

  • Jongschaap, R. E. E. (2006). Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen. European Journal of Agronomy, 24(4), 316–324. https://doi.org/10.1016/j.eja.2005.10.009

    Article  Google Scholar 

  • Kasampalis, D., Alexandridis, T., Deva, C., Challinor, A., Moshou, D., & Zalidis, G. (2018). Contribution of remote sensing on crop models: A review. Journal of Imaging, 4(4). https://doi.org/10.3390/jimaging4040052

  • Li, Z., Jin, X., Zhao, C., Wang, J., Xu, X., Yang, G., Li, C., & Shen, J. (2015). Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing. European Journal of Agronomy, 71, 53–62. https://doi.org/10.1016/j.eja.2015.08.006

    Article  Google Scholar 

  • Liu, H., & Chahl, J. S. (2021). Proximal detecting invertebrate pests on crops using a deep residual convolutional neural network trained by virtual images. Artificial Intelligence in Agriculture, 5, 13–23. https://doi.org/10.1016/j.aiia.2021.01.003

    Article  CAS  Google Scholar 

  • Maas, S. J. (1992). GRAMI: A crop model growth that can use remotely sensed information; USDA-ARS. ISSN: 1052-5386.

    Google Scholar 

  • Maas, S. J. (1988). Using satellite data to improve model estimates of crop yield. Agronomy Journal, 80(4), 655–662. https://doi.org/10.2134/agronj1988.00021962008000040021x

    Article  Google Scholar 

  • Mladenova, I. E., Bolten, J. D., Crow, W., Sazib, N., & Reynolds, C. (2020). Agricultural drought monitoring via the assimilation of SMAP soil moisture retrievals into a global soil water balance model. Frontiers in Big Data, 3. https://doi.org/10.3389/fdata.2020.00010

  • Moreira, F. F., Oliveira, H. R., Volenec, J. J., Rainey, K. M., & Brito, L. F. (2020). Integrating high-throughput phenotyping and statistical genomic methods to genetically improve longitudinal traits in crops. Frontiers in Plant Science, 11, 681. https://doi.org/10.3389/fpls.2020.00681

    Article  Google Scholar 

  • Moulin, S., Bondeau, A., & Delecolle, R. (2010). Combining agricultural crop models and satellite observations: From field to regional scales. International Journal of Remote Sensing, 19(6), 1021–1036. https://doi.org/10.1080/014311698215586

    Article  Google Scholar 

  • Peng, B., Guan, K., Tang, J., Ainsworth, E. A., Asseng, S., Bernacchi, C. J., Cooper, M., Delucia, E. H., Elliott, J. W., Ewert, F., Grant, R. F., Gustafson, D. I., Hammer, G. L., Jin, Z., Jones, J. W., Kimm, H., Lawrence, D. M., Li, Y., Lombardozzi, D. L., Marshall-Colon, A., Messina, C. D., Ort, D. R., Schnable, J. C., Vallejos, C. E., Wu, A., Yin, X., & Zhou, W. (2020). Towards a multiscale crop modelling framework for climate change adaptation assessment. Nature Plants, 6(4), 338–348. https://doi.org/10.1038/s41477-020-0625-3

    Article  Google Scholar 

  • Pierce, F. J., & Nowak, P. (1999). Aspects of precision agriculture. In Advances in agronomy (Advances in agronomy) (Vol. 67, pp. 1–85). https://doi.org/10.1016/s0065-2113(08)60513-1

  • Thessler, S., Kooistra, L., Teye, F., Huitu, H., & Bregt, A. K. (2011). Geosensors to support crop production: Current applications and user requirements. Sensors, 11(7), 6656–6684. https://doi.org/10.3390/s110706656

    Article  Google Scholar 

  • Van Evert, F. K., Gaitán-Cremaschi, D., Fountas, S., & Kempenaar, C. (2017). Can precision agriculture increase the profitability and sustainability of the production of potatoes and olives? Sustainability, 9(10), 1863. https://doi.org/10.3390/su9101863

    Article  Google Scholar 

  • Wagner, M. P., Slawig, T., Taravat, A., & Oppelt, N. (2020). Remote sensing data assimilation in dynamic crop models using particle swarm optimization. ISPRS International Journal of Geo-Information, 9(2). https://doi.org/10.3390/ijgi9020105

  • Wu, S., Yang, P., Ren, J., Chen, Z., & Li, H. (2021). Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm. Remote Sensing of Environment, 255. https://doi.org/10.1016/j.rse.2020.112276

  • Xie, Y., Wang, P., Bai, X., Khan, J., Zhang, S., Li, L., & Wang, L. (2017). Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-wheat model. Agricultural and Forest Meteorology, 246, 194–206. https://doi.org/10.1016/j.agrformet.2017.06.015

    Article  Google Scholar 

  • Xu, W., Jiang, H., & Huang, J. (2011). Regional crop yield assessment by combination of a crop growth model and phenology information derived from MODIS. Sensor Letters, 9(3), 981–989. https://doi.org/10.1166/sl.2011.1388

    Article  Google Scholar 

  • Yang, S., Zheng, L., He, P., Wu, T., Sun, S., & Wang, M. (2021). High-throughput soybean seeds phenotyping with convolutional neural networks and transfer learning. Plant Methods, 17(Maro), 50. https://doi.org/10.1186/s13007-021-00749-y

    Article  Google Scholar 

  • Yu, D., Zha, Y., Shi, L., Jin, X., Hu, S., Yang, Q., Huang, K., & Zeng, W. (2020). Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations. European Journal of Agronomy, 121. https://doi.org/10.1016/j.eja.2020.126159

  • Zhang, Z., & Moore, J. C. (2015). Chapter 9: Data assimilation. In Z. Zhang & J. C. Moore (Eds.), Mathematical and physical fundamentals of climate change (pp. 291–311). Elsevier. ISBN 9780128000663. https://doi.org/10.1016/B978-0-12-800066-3.00009-7

Download references

Conflict of Interest

The authors declare that there are no conflicts of interest.

Author information

Authors and Affiliations

  1. Agrosystems Research, Wageningen University & Research, Wageningen, the Netherlands

    Keiji Jindo

  2. Center for Southeast Asia Studies, Kyoto University, Kyoto, Japan

    Osamu Kozan

  3. Environmental Research, Wageningen University & Research, Wageningen, the Netherlands

    Allard de Wit

Authors
  1. Keiji Jindo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Osamu Kozan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Allard de Wit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Keiji Jindo .

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

Jindo, K., Kozan, O., de Wit, A. (2023). Data Assimilation of Remote Sensing Data into a Crop Growth Model. 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_8

Download citation

Publish with us

Policies and ethics

Search

Navigation