Application of Remote Sensing Data in Crop Yield and Quality: Systematic Literature Review
Abstract
Purpose: Covering current state of the art in the field of application of remotely sensed data in crop quality improvement.
Methodology/Approach: Systematic literature review using novel text mining techniques.
Findings: Relevance of topic, measured by number of relevant studies, is rising, best performing input data types and modelling techniques are identified.
Research Limitation/Implication: Review to a certain point of time in a rapidly evolving field of research.
Originality/Value of paper: There was no similar review article on the topic at the time of conducting this research.
References
Ban H., Ahn, J. and Lee, B., 2019. Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions. PLOS ONE, [e-journal] 14(2), e0211874. DOI: 10.1371/journal.pone.0211874.
Basso, B., Liu, L. and Ritchie, J., 2016. A Comprehensive Review of the CERES-Wheat, -Maize and -Rice Models’ Performances. Advances In Agronomy, pp.27-132. DOI: 10.1016/bs.agron.2015.11.004.
Bose, P., Kasabov, N., Bruzzone, L. and Hartono, R., 2016. Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series. IEEE Transactions On Geoscience And Remote Sensing, [e-journal] 54(11), pp.6563-6573. DOI: 10.1109/tgrs.2016.2586602.
Cisternas, I., Velásquez, I., Caro, A. and Rodríguez, A., 2020. Systematic literature review of implementations of precision agriculture. Computers And Electronics In Agriculture, [e-journal] 176, 105626. DOI: 10.1016/j.compag.2020.105626.
Dente, L., Satalino, G., Mattia, F. and Rinaldi, M., 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote Sensing Of Environment, [e-journal] 112(4), pp.1395-1407. DOI: 10.1016/j.rse.2007.05.023.
Eze, E., Girma, A., Zenebe, A., Kourouma, J. and Zenebe, G., 2020. Exploring the possibilities of remote yield estimation using crop water requirements for area yield index insurance in a data-scarce dryland. Journal Of Arid Environments, [e-journal] 183, 104261. DOI: 10.1016/j.jaridenv.2020.104261.
Fang, H., Liang, S. and 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, [e-journal] 32(4), pp.1039-1065. DOI: 10.1080/01431160903505310.
Gontia, N. and Tiwari, K., 2011. Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS. Journal Of The Indian Society Of Remote Sensing, [e-journal] 39(1), pp.27-37. DOI: 10.1007/s12524-011-0065-7.
Holzman, M. and Rivas, R., 2016. Early Maize Yield Forecasting From Remotely Sensed Temperature/Vegetation Index Measurements. IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing, [e-journal] 9(1), pp.507-519. DOI: 10.1109/jstars.2015.2504262.
Jensen, J.R., 1996. Remote sensing of the environment: An Earth Resource Perspective. 3th Ed. USA: Prentice Hall.
Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J. and Huang, J., 2019. A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level. Global Change Biology, [e-journal] 26(3), pp.1754-1766. DOI: 10.1111/gcb.14885.
Jin, H., Li, A., Wang, J. and Bo, Y., 2016a. Improvement of spatially and temporally continuous crop leaf area index by integration of CERES-Maize model and MODIS data. European Journal Of Agronomy, [e-journal] 78, pp.1-12. DOI: 10.1016/j.eja.2016.04.007.
Jin, X., Kumar, L., Li, Z., Xu, X., Yang, G. and Wang, J., 2016b. Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data. Remote Sensing, [e-journal] 8(12), 972. DOI: 10.3390/rs8120972.
Kitchenham, B. and Charters, S., 2007. Guidelines for performing systematic literature reviews in software engineering version 2.3. Keele University and Durham University Joint Report.
Koller, M. and Upadhyaya, S.K., 2005. Prediction of Processing Tomato Yield Using a Crop Growth Model and Remotely Sensed Aerial Images. Transactions Of The ASAE, [e-journal] 48(6), pp.2335-2341. DOI: 10.13031/2013.20072.
Leroux, L., Castets, M., Baron, C., Escorihuela, M., Bégué, A. and Lo Seen, D., 2019. Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. European Journal Of Agronomy, [e-journal] 108, pp.11-26. DOI: 10.1016/j.eja.2019.04.007.
Liaghat, S. and Balasundram, S., 2010. A Review: The Role of Remote Sensing in Precision Agriculture. American Journal Of Agricultural And Biological Sciences, [e-journal] 5(1), pp.50-55. DOI: 10.3844/ajabssp.2010.50.55.
Liaqat, M., Cheema, M., Huang, W., Mahmood, T., Zaman, M. and Khan, M., 2017. Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin. Computers And Electronics In Agriculture, [e-journal] 138, pp.39-47. DOI: 10.1016/j.compag.2017.04.006.
Luciani, R., Laneve, G. and JahJah, M., 2019. Agricultural Monitoring, an Automatic Procedure for Crop Mapping and Yield Estimation: The Great Rift Valley of Kenya Case. IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing, [e-journal] 12(7), pp.2196-2208. DOI: 10.1109/jstars.2019.2921437.
Ma, G., Huang, J., Wu, W., Fan, J., Zou, J. and Wu, S., 2013. Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield. Mathematical And Computer Modelling, [e-journal] 58(3-4), pp.634-643. DOI: 10.1016/j.mcm.2011.10.038.
Munnaf, M., Haesaert, G., Van Meirvenne, M. and Mouazen, A., 2020. Site-specific seeding using multi-sensor and data fusion techniques: A review. Advances in Agronomy, [e-journal] 161, pp.241-323. DOI: 10.1016/bs.agron.2019.08.001.
Ngie, A. and Ahmed, F., 2018. Estimation of Maize grain yield using multispectral satellite data sets (SPOT 5) and the random forest algorithm. South African Journal Of Geomatics, [e-journal] 7(1), pp.11-30. DOI: 10.4314/sajg.v7i1.2.
Noureldin, N., Aboelghar, M., Saudy, H. and Ali, A., 2013. Rice yield forecasting models using satellite imagery in Egypt. The Egyptian Journal Of Remote Sensing And Space Science, [e-journal] 16(1), pp.125-131. DOI: 10.1016/j.ejrs.2013.04.005.
Pau,l Ch.G., Saha, S. and Hembram, T., 2020. Application of phenology-based algorithm and linear regression model for estimating rice cultivated areas and yield using remote sensing data in Bansloi River Basin, Eastern India. Remote Sensing Applications: Society And Environment, [e-journal] 19, 100367. DOI: 10.1016/j.rsase.2020.100367.
Peng, B., Guan, K., Zhou, W., Jiang, C., Frankenberg, C. and Sun, Y., 2020. Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction. International Journal Of Applied Earth Observation And Geoinformation, [e-journal] 90, 102126. DOI: 10.1016/j.jag.2020.102126.
Prasad, A., Chai, L., Singh, R. and Kafatos, M., 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal Of Applied Earth Observation And Geoinformation, [e-journal] 8(1), pp.26-33. DOI: 10.1016/j.jag.2005.06.002.
Sakamoto, T., Gitelson, A. and Arkebauer, T., 2013. MODIS-based corn grain yield estimation model incorporating crop phenology information. Remote Sensing Of Environment, [e-journal] 131, pp.215-231. DOI: 10.1016/j.rse.2012.12.017.
Shao, Y., Campbell, J., Taff, G. and Zheng, B., 2015. An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data. International Journal Of Applied Earth Observation And Geoinformation, [e-journal] 38, pp.78-87. DOI: 10.1016/j.jag.2014.12.017.
Shrestha, R., Di, L., Yu, E., Kang, L., Shao, Y. and Bai, Y., 2017. Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer. Journal Of Integrative Agriculture, [e-journal] 16(2), pp.398-407. DOI: 10.1016/s2095-3119(16)61502-2.
Solemane, C., Kamsu-Foguem, B., Kamissoko, D. and Traore, D., 2019. Deep neural networks with transfer learning in millet crop images. Computers in Industry. [e-journal] 108, pp.115-120. DOI: 10.1016/j.compind.2019.02.003.
Sun, J., Lai, Z., Di, L., Sun, Z., Tao, J. and Shen, Y., 2020. Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt. IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing, [e-journal] 13, pp.5048-5060. DOI: 10.1109/jstars.2020.3019046.
Villalobos, F.J., Testi, L., Hidalgo, J., Pastor, M. and Orgaz, F., 2006. Modelling potential growth and yield of olive (Olea europaea L.) canopies. Eur. J. Agron. [e-journal] 24, pp.296-303. DOI: 10.1016/j.eja.2005.10.008.
Wang, Y., Chang, K., Chen, R., Lo, J. and Shen, Y., 2010. Large-area rice yield forecasting using satellite imageries. International Journal Of Applied Earth Observation And Geoinformation, [e-journal] 12(1), pp.27-35. DOI: 10.1016/j.jag.2009.09.009.
Yu, B. and Shang, S., 2018. Multi-Year Mapping of Major Crop Yields in an Irrigation District from High Spatial and Temporal Resolution Vegetation Index. Sensors, [e-journal] 18(11), 3787. DOI: 10.3390/s18113787.
Yuping, M., Shili, W., Li, Z., Yingyu, H., Liwei, Z., Yanbo, H. and Futang, W., 2008. Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. International Journal Of Applied Earth Observation And Geoinformation, [e-journal] 10(4), pp.426-437. DOI: 10.1016/j.jag.2007.09.002.
Basso, B., Liu, L. and Ritchie, J., 2016. A Comprehensive Review of the CERES-Wheat, -Maize and -Rice Models’ Performances. Advances In Agronomy, pp.27-132. DOI: 10.1016/bs.agron.2015.11.004.
Bose, P., Kasabov, N., Bruzzone, L. and Hartono, R., 2016. Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series. IEEE Transactions On Geoscience And Remote Sensing, [e-journal] 54(11), pp.6563-6573. DOI: 10.1109/tgrs.2016.2586602.
Cisternas, I., Velásquez, I., Caro, A. and Rodríguez, A., 2020. Systematic literature review of implementations of precision agriculture. Computers And Electronics In Agriculture, [e-journal] 176, 105626. DOI: 10.1016/j.compag.2020.105626.
Dente, L., Satalino, G., Mattia, F. and Rinaldi, M., 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote Sensing Of Environment, [e-journal] 112(4), pp.1395-1407. DOI: 10.1016/j.rse.2007.05.023.
Eze, E., Girma, A., Zenebe, A., Kourouma, J. and Zenebe, G., 2020. Exploring the possibilities of remote yield estimation using crop water requirements for area yield index insurance in a data-scarce dryland. Journal Of Arid Environments, [e-journal] 183, 104261. DOI: 10.1016/j.jaridenv.2020.104261.
Fang, H., Liang, S. and 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, [e-journal] 32(4), pp.1039-1065. DOI: 10.1080/01431160903505310.
Gontia, N. and Tiwari, K., 2011. Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS. Journal Of The Indian Society Of Remote Sensing, [e-journal] 39(1), pp.27-37. DOI: 10.1007/s12524-011-0065-7.
Holzman, M. and Rivas, R., 2016. Early Maize Yield Forecasting From Remotely Sensed Temperature/Vegetation Index Measurements. IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing, [e-journal] 9(1), pp.507-519. DOI: 10.1109/jstars.2015.2504262.
Jensen, J.R., 1996. Remote sensing of the environment: An Earth Resource Perspective. 3th Ed. USA: Prentice Hall.
Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J. and Huang, J., 2019. A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level. Global Change Biology, [e-journal] 26(3), pp.1754-1766. DOI: 10.1111/gcb.14885.
Jin, H., Li, A., Wang, J. and Bo, Y., 2016a. Improvement of spatially and temporally continuous crop leaf area index by integration of CERES-Maize model and MODIS data. European Journal Of Agronomy, [e-journal] 78, pp.1-12. DOI: 10.1016/j.eja.2016.04.007.
Jin, X., Kumar, L., Li, Z., Xu, X., Yang, G. and Wang, J., 2016b. Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data. Remote Sensing, [e-journal] 8(12), 972. DOI: 10.3390/rs8120972.
Kitchenham, B. and Charters, S., 2007. Guidelines for performing systematic literature reviews in software engineering version 2.3. Keele University and Durham University Joint Report.
Koller, M. and Upadhyaya, S.K., 2005. Prediction of Processing Tomato Yield Using a Crop Growth Model and Remotely Sensed Aerial Images. Transactions Of The ASAE, [e-journal] 48(6), pp.2335-2341. DOI: 10.13031/2013.20072.
Leroux, L., Castets, M., Baron, C., Escorihuela, M., Bégué, A. and Lo Seen, D., 2019. Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. European Journal Of Agronomy, [e-journal] 108, pp.11-26. DOI: 10.1016/j.eja.2019.04.007.
Liaghat, S. and Balasundram, S., 2010. A Review: The Role of Remote Sensing in Precision Agriculture. American Journal Of Agricultural And Biological Sciences, [e-journal] 5(1), pp.50-55. DOI: 10.3844/ajabssp.2010.50.55.
Liaqat, M., Cheema, M., Huang, W., Mahmood, T., Zaman, M. and Khan, M., 2017. Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin. Computers And Electronics In Agriculture, [e-journal] 138, pp.39-47. DOI: 10.1016/j.compag.2017.04.006.
Luciani, R., Laneve, G. and JahJah, M., 2019. Agricultural Monitoring, an Automatic Procedure for Crop Mapping and Yield Estimation: The Great Rift Valley of Kenya Case. IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing, [e-journal] 12(7), pp.2196-2208. DOI: 10.1109/jstars.2019.2921437.
Ma, G., Huang, J., Wu, W., Fan, J., Zou, J. and Wu, S., 2013. Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield. Mathematical And Computer Modelling, [e-journal] 58(3-4), pp.634-643. DOI: 10.1016/j.mcm.2011.10.038.
Munnaf, M., Haesaert, G., Van Meirvenne, M. and Mouazen, A., 2020. Site-specific seeding using multi-sensor and data fusion techniques: A review. Advances in Agronomy, [e-journal] 161, pp.241-323. DOI: 10.1016/bs.agron.2019.08.001.
Ngie, A. and Ahmed, F., 2018. Estimation of Maize grain yield using multispectral satellite data sets (SPOT 5) and the random forest algorithm. South African Journal Of Geomatics, [e-journal] 7(1), pp.11-30. DOI: 10.4314/sajg.v7i1.2.
Noureldin, N., Aboelghar, M., Saudy, H. and Ali, A., 2013. Rice yield forecasting models using satellite imagery in Egypt. The Egyptian Journal Of Remote Sensing And Space Science, [e-journal] 16(1), pp.125-131. DOI: 10.1016/j.ejrs.2013.04.005.
Pau,l Ch.G., Saha, S. and Hembram, T., 2020. Application of phenology-based algorithm and linear regression model for estimating rice cultivated areas and yield using remote sensing data in Bansloi River Basin, Eastern India. Remote Sensing Applications: Society And Environment, [e-journal] 19, 100367. DOI: 10.1016/j.rsase.2020.100367.
Peng, B., Guan, K., Zhou, W., Jiang, C., Frankenberg, C. and Sun, Y., 2020. Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction. International Journal Of Applied Earth Observation And Geoinformation, [e-journal] 90, 102126. DOI: 10.1016/j.jag.2020.102126.
Prasad, A., Chai, L., Singh, R. and Kafatos, M., 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal Of Applied Earth Observation And Geoinformation, [e-journal] 8(1), pp.26-33. DOI: 10.1016/j.jag.2005.06.002.
Sakamoto, T., Gitelson, A. and Arkebauer, T., 2013. MODIS-based corn grain yield estimation model incorporating crop phenology information. Remote Sensing Of Environment, [e-journal] 131, pp.215-231. DOI: 10.1016/j.rse.2012.12.017.
Shao, Y., Campbell, J., Taff, G. and Zheng, B., 2015. An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data. International Journal Of Applied Earth Observation And Geoinformation, [e-journal] 38, pp.78-87. DOI: 10.1016/j.jag.2014.12.017.
Shrestha, R., Di, L., Yu, E., Kang, L., Shao, Y. and Bai, Y., 2017. Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer. Journal Of Integrative Agriculture, [e-journal] 16(2), pp.398-407. DOI: 10.1016/s2095-3119(16)61502-2.
Solemane, C., Kamsu-Foguem, B., Kamissoko, D. and Traore, D., 2019. Deep neural networks with transfer learning in millet crop images. Computers in Industry. [e-journal] 108, pp.115-120. DOI: 10.1016/j.compind.2019.02.003.
Sun, J., Lai, Z., Di, L., Sun, Z., Tao, J. and Shen, Y., 2020. Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt. IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing, [e-journal] 13, pp.5048-5060. DOI: 10.1109/jstars.2020.3019046.
Villalobos, F.J., Testi, L., Hidalgo, J., Pastor, M. and Orgaz, F., 2006. Modelling potential growth and yield of olive (Olea europaea L.) canopies. Eur. J. Agron. [e-journal] 24, pp.296-303. DOI: 10.1016/j.eja.2005.10.008.
Wang, Y., Chang, K., Chen, R., Lo, J. and Shen, Y., 2010. Large-area rice yield forecasting using satellite imageries. International Journal Of Applied Earth Observation And Geoinformation, [e-journal] 12(1), pp.27-35. DOI: 10.1016/j.jag.2009.09.009.
Yu, B. and Shang, S., 2018. Multi-Year Mapping of Major Crop Yields in an Irrigation District from High Spatial and Temporal Resolution Vegetation Index. Sensors, [e-journal] 18(11), 3787. DOI: 10.3390/s18113787.
Yuping, M., Shili, W., Li, Z., Yingyu, H., Liwei, Z., Yanbo, H. and Futang, W., 2008. Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. International Journal Of Applied Earth Observation And Geoinformation, [e-journal] 10(4), pp.426-437. DOI: 10.1016/j.jag.2007.09.002.
Authors
Čorňák, A., & Delina, R. (2022). Application of Remote Sensing Data in Crop Yield and Quality: Systematic Literature Review. Quality Innovation Prosperity, 26(3), 22–36. https://doi.org/10.12776/qip.v26i3.1708
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