Combining multiple UAV-Based indicators for wheat yield estimation, a case study from Germany

References

• Aich, V., Akhundzadah, N. A., Knuerr, A., Khoshbeen, A. J., Hattermann, F., Paeth, H., Scanlon, A., & Paton, E. N. (2017). Climate change in Afghanistan deduced from reanalysis and coordinated regional climate downscaling experiment (cordex)—South Asia simulations. Climate, 5(2), 38. https://doi.org/10.3390/cli5020038
   Google Scholar
• Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., & Thompson, T. (2000). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA.
   Google Scholar
• Barnwal, P., Kotani, K., & Barnwal, & Kotani. (2013). Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India. Ecological Economics, 87, 95–16. https://doi.org/10.1016/j.ecolecon.2012.11.024
   Web of Science ®Google Scholar
• Belton, D., Helmholz, P., Long, J., & Zerihun, A. (2019). Crop height monitoring using a consumer-grade camera and UAV technology. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 87(5), 249–262. https://doi.org/10.1007/s41064-019-00087-8
   Web of Science ®Google Scholar
• Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M. L., & Bareth, G. (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39, 79–87. https://doi.org/10.1016/j.jag.2015.02.012
   Web of Science ®Google Scholar
• Boiarskii, B., & Hasegawa, H. J. J. M. C. M. S. (2019). Comparison of NDVI and NDRE indices to detect differences in vegetation and chlorophyll content. JOURNAL of MECHANICS of CONTINUA and MATHEMATICAL SCIENCES, 4(4), 20–29. https://doi.org/10.26782/jmcms.spl.4/2019.11.00003
   Google Scholar
• Chakrabarti, S., Bongiovanni, T., Judge, J., Zotarelli, L., & Bayer, C. (2014). Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in Agricultural regions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9), 3867–3879. https://doi.org/10.1109/JSTARS.2014.2315999
   Web of Science ®Google Scholar
• Cheng, M., Penuelas, J., McCabe, M. F., Atzberger, C., Jiao, X., Wu, W., & Jin, X. (2022). Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China. Agricultural and Forest Meteorology, 323, 109057. https://doi.org/10.1016/j.agrformet.2022.109057
   Web of Science ®Google Scholar
• Choudhury, R. M., Das, S., Christopher, J., Apan, A., Chapman, S., Menzies, N. W., & Dang, Y. P. (2021). Improving biomass and grain yield prediction of wheat genotypes on sodic soil using integrated high-resolution multispectral, hyperspectral, 3D point cloud, and machine learning techniques. Remote Sensing, 13(17), 3482. https://doi.org/10.3390/rs13173482
   Web of Science ®Google Scholar
• Cook, K. L. (2017). An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection. Geomorphology, 278, 195–208. https://doi.org/10.1016/j.geomorph.2016.11.009
   Web of Science ®Google Scholar
• Elavarasan, D., Vincent, P. M. D. R., Srinivasan, K., & Chang, C.-Y. (2020). A hybrid CFS filter and RF-RFE wrapper-based feature extraction for enhanced agricultural crop yield prediction modeling. Agriculture, 10(9), 400. https://doi.org/10.3390/agriculture10090400
   Google Scholar
• Flessa, H., Ruser, R., Dörsch, P., Kamp, T., Jimenez, M., Munch, J., Beese, F. J. A., & Ecosystems, & Environment. (2002). Integrated evaluation of greenhouse gas emissions (CO2, CH4, N2O) from two farming systems in southern Germany. Agriculture, Ecosystems and Environment, 91(1–3), 175–189. https://doi.org/10.1016/S0167-8809(01)00234-1
   Web of Science ®Google Scholar
• Fuentes, J. E., Moya, F. D., & Montoya, O. D. (2020). Method for estimating solar energy potential based on photogrammetry from unmanned aerial vehicles. Electronics, 9(12), 2144. https://doi.org/10.3390/electronics9122144
   Web of Science ®Google Scholar
• Furukawa, F., Maruyama, K., Saito, Y. K., & Kaneko, M. (2020). Corn height estimation using UAV for yield prediction and crop monitoring. Unmanned Aerial Vehicle: Applications in Agriculture and Environment, 51–69. https://doi.org/10.1007/978-3-030-27157-2_5
   Google Scholar
• García-Martínez, H., Flores-Magdaleno, H., Ascencio-Hernández, R., Khalil-Gardezi, A., Tijerina-Chávez, L., Mancilla-Villa, O. R., & Vázquez-Peña, M. A. (2020). Corn grain yield estimation from vegetation indices, canopy cover, plant density, and a neural network using multispectral and RGB images acquired with unmanned aerial vehicles. Agriculture, 10(7), 277. https://doi.org/10.3390/agriculture10070277
   Web of Science ®Google Scholar
• Gitelson, A. A., Gritz, Y., & Merzlyak, M. N. J. J. O. P. P. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3), 271–282. https://doi.org/10.1078/0176-1617-00887
   PubMed Web of Science ®Google Scholar
• Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, 22(3), 247–252. https://doi.org/10.1016/1011-1344(93)06963-4
   Web of Science ®Google Scholar
• Gitelson, A. A., Viña, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G., & Leavitt, B. J. G. R. L. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(5). https://doi.org/10.1029/2002GL016450
   Web of Science ®Google Scholar
• Green, D. R., Hagon, J. J., Gómez, C., & Gregory, B. J. (2019). Using low-cost UAVs for environmental monitoring, mapping, and modelling: Examples from the coastal zone (coastal management). Elsevier.
   Google Scholar
• Guan, S., Fukami, K., Matsunaka, H., Okami, M., Tanaka, R., Nakano, H., Sakai, T., Nakano, K., Ohdan, H., & Takahashi, K. (2019). Assessing correlation of high-resolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sensing, 11(2), 112. https://doi.org/10.3390/rs11020112
   Web of Science ®Google Scholar
• Guan, S., Fukami, K., Matsunaka, H., Okami, M., Tanaka, R., Nakano, H., Sakai, T., Nakano, K., Ohdan, H., & Takahashi, K. J. R. S. (2019). Assessing correlation of high-resolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sensing, 11(2), 112. https://doi.org/10.3390/rs11020112
   Web of Science ®Google Scholar
• Hassan, M. A., Yang, M., Fu, L., Rasheed, A., Zheng, B., Xia, X., Xiao, Y., & He, Z. J. P. M. (2019). Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat. Plant Methods, 15(1), 1–12. https://doi.org/10.1186/s13007-019-0419-7
   PubMedGoogle Scholar
• Holman, F. H., Riche, A. B., Michalski, A., Castle, M., Wooster, M. J., & Hawkesford, M. J. J. R. S. (2016). High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sensing, 8(12), 1031. https://doi.org/10.3390/rs8121031
   Web of Science ®Google Scholar
• Holzman, M. E., Carmona, F., Rivas, R., Niclòs, R., & J. I. j. o. p., & sensing, r. (2018). Early assessment of crop yield from remotely sensed water stress and solar radiation data. Isprs Journal of Photogrammetry & Remote Sensing, 145, 297–308. https://doi.org/10.1016/j.isprsjprs.2018.03.014
   Web of Science ®Google Scholar
• Huang, J., Sedano, F., Huang, Y., Ma, H., Li, X., Liang, S., Tian, L., Zhang, X., Fan, J., & Wu, W. J. A. (2016). Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology, 216, 188–202. https://doi.org/10.1016/j.agrformet.2015.10.013
   Web of Science ®Google Scholar
• Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., Su, W., Zhang, X., Zhu, D., & Wu, W. (2015). Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204, 106–121. https://doi.org/10.1016/j.agrformet.2015.02.001
   Web of Science ®Google Scholar
• Ihuoma, S. O., & Madramootoo, C. A. J. B. E. (2020). Narrow-band reflectance indices for mapping the combined effects of water and nitrogen stress in field grown tomato crops. Biosystems Engineering, 192, 133–143. https://doi.org/10.1016/j.biosystemseng.2020.01.017
   Web of Science ®Google Scholar
• Jiang, Z., Huete, A. R., Didan, K., & Miura, T. J. R. S. O. E. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 3833–3845. https://doi.org/10.1016/j.rse.2008.06.006
   Web of Science ®Google Scholar
• 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. The European Journal of Agronomy, 92, 141–152. https://doi.org/10.1016/j.eja.2017.11.002
   Web of Science ®Google Scholar
• Kanning, M., Kühling, I., Trautz, D., & Jarmer, T. (2018). High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sensing, 10(12), 2000. https://doi.org/10.3390/rs10122000
   Web of Science ®Google Scholar
• Kanning, M., Kühling, I., Trautz, D., & Jarmer, T. J. R. S. (2018). High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sensing, 10(12), 2000. https://doi.org/10.3390/rs10122000
   Web of Science ®Google Scholar
• Kaur, P., Singh, H., Rao, V., Hundal, S., Sandhu, S., Nayyar, S., Bodapati, B., & Kaur, A. (2015). Agrometeorology of wheat in Punjab state of India Technical Report. Technical Report. https://doi.org/10.13140/RG.2.1.5105.6721
   Google Scholar
• Khalaf, A. Z., & Hameed, A. J. I. J. I. S. R. T. (2020). Orthomosaic from generating 3D models with Photogrammetry. International Journal of Innovative Science and Research Technology, 5(3), 48–60.
   Google Scholar
• Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 46(1), 33–50. https://doi.org/10.2307/1913643
   Web of Science ®Google Scholar
• Koenker, R., & Machado, J. A. (1999). Goodness of fit and related inference processes for quantile regression. Journal of the American Statistical Association, 94(448), 1296–1310. https://doi.org/10.1080/01621459.1999.10473882
   Web of Science ®Google Scholar
• Lastilla, L., Belloni, V., Ravanelli, R., & Crespi, M. J. R. S. (2021). DSM generation from single and cross-sensor multi-view satellite images using the new agisoft metashape: The case studies of Trento and Matera (Italy). Remote Sensing, 13(4), 593. https://doi.org/10.3390/rs13040593
   Web of Science ®Google Scholar
• Li, Y. F., Ata-UI-Karim, S. T., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., & Cao, Q. (2019). Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sensing, 11(15), 1763. https://doi.org/10.3390/rs11151763
   Web of Science ®Google Scholar
• Li, R., LI, C.-J., DONG, Y.-Y., LIU, F., WANG, J.-H., YANG, X.-D., & PAN, Y.-C. (2011). Assimilation of Remote Sensing and crop model for LAI estimation based on ensemble Kaiman filter. Agricultural Sciences in China, 10(10), 1595–1602. https://doi.org/10.1016/S1671-2927(11)60156-9
   Google Scholar
• Liu, B., Asseng, S., Wang, A., Wang, S., Tang, L., Cao, W., Zhu, Y., & Liu, L. J. A. (2017). Modelling the effects of post-heading heat stress on biomass growth of winter wheat. Agricultural and Forest Meteorology, 247, 476–490. https://doi.org/10.1016/j.agrformet.2017.08.018
   Web of Science ®Google Scholar
• Lobell, D. B., Burke, M. B. J. A., & meteorology, f. (2010). On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 150(11), 1443–1452. https://doi.org/10.1016/j.agrformet.2010.07.008
   Web of Science ®Google Scholar
• Mamrutha, H., Sharma, D., Sumanth Kumar, K., Venkatesh, K., Tiwari, V., & Sharma, I. (2017). Influence of diurnal irradiance variation on chlorophyll values in wheat: A comparative study using different chlorophyll meters. National Academy Science Letters, 40(3), 221–224. https://doi.org/10.1007/s40009-017-0544-7
   Web of Science ®Google Scholar
• Marino, S., & Alvino, A. (2020). Agronomic traits analysis of ten winter wheat cultivars clustered by UAV-derived vegetation indices. Remote Sensing, 12(2), 249. https://doi.org/10.3390/rs12020249
   Web of Science ®Google Scholar
• Mishra, A., & Moss, C. (2013). Modeling the effect of off-farm income on farmland values: A quantile regression approach. Economic Modelling, 32, 361–368. https://doi.org/10.1016/j.econmod.2013.02.022
   Web of Science ®Google Scholar
• Murungweni, F. M., Mutanga, O., & Odiyo, J. O. (2020). Rainfall trend and its relationship with normalized difference vegetation index in a restored semi-arid wetland of South Africa. Sustainability, 12(21), 8919. https://doi.org/10.3390/su12218919
   Web of Science ®Google Scholar
• Paliwal, A., & Jain, M. (2020). The accuracy of self-reported crop yield estimates and their ability to train remote sensing algorithms. Frontiers in Sustainable Food Systems, 4, 25. https://doi.org/10.3389/fsufs.2020.00025
   Google Scholar
• Panday, U. S., Shrestha, N., Maharjan, S., Pratihast, A. K., Shrestha, K. L., & Aryal, J. (2020). Correlating the plant height of wheat with above-ground biomass and crop yield using drone imagery and crop surface model, a case study from Nepal. Drones, 4(3), 28. https://doi.org/10.3390/drones4030028
   Google Scholar
• Possoch, M., Bieker, S., Hoffmeister, D., Bolten, A., Schellberg, J., & Bareth, G. J. T. I. A. O. T. P. (2016). Multi-temporal crop surface models combined with the RGB vegetation index from UAV-based images for forage monitoring in grassland. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 991–998. https://doi.org/10.5194/isprs-archives-XLI-B1-991-2016
   Google Scholar
• Radhakrishna, R., & Toutenburg, H. (1995). Linear models. Linear Models: Least Squares and Alternatives, 5–21. https://doi.org/10.1007/978-1-4899-0024-1
   Google Scholar
• Reynolds, M. P., Gutiérrez-Rodrı́guez, M., Larqué-Saavedra, A. J. F. C. R., & Larqué-Saavedra, A. (2000). Photosynthesis of wheat in a warm, irrigated environment: I: Genetic diversity and crop productivity. Field Crops Research, 66(1), 37–50. https://doi.org/10.1016/S0378-4290(99)00077-5
   Web of Science ®Google Scholar
• Siebert, S., & Ewert, F. (2012). Spatio-temporal patterns of phenological development in Germany in relation to temperature and day length. Agricultural and Forest Meteorology, 152, 44–57. https://doi.org/10.1016/j.agrformet.2011.08.007
   Web of Science ®Google Scholar
• Song, Y., Wang, J., Shang, J., & Liao, C. J. R. S. (2020). Using UAV-Based SOPC derived LAI and SAFY model for biomass and yield estimation of Winter wheat. Remote Sensing, 12(15), 2378. https://doi.org/10.3390/rs12152378
   Web of Science ®Google Scholar
• Sullivan, M. (2018). Statistics: Informed decisions using data. Prentice Hall/Pearson.
   Google Scholar
• Tao, H., Feng, H., Xu, L., Miao, M., Yang, G., Yang, X., & Fan, L. (2020). Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images. Sensors, 20(4), 1231. https://doi.org/10.3390/s20041231
   PubMed Web of Science ®Google Scholar
• Tareghian, R., & Rasmussen, P. (2013). Analysis of arctic and Antarctic sea ice extent using quantile regression. International Journal of Climatology, 33(5), 1079–1086. https://doi.org/10.1002/joc.3491
   Web of Science ®Google Scholar
• Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71(2), 158–182. https://doi.org/10.1016/S0034-4257(99)00067-X
   Web of Science ®Google Scholar
• Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
   Web of Science ®Google Scholar
• Vilar, L., Camia, A., & San-Miguel-Ayanz, J. (2015). A comparison of remote sensing products and forest fire statistics for improving fire information in Mediterranean Europe. European Journal of Remote Sensing, 48(1), 345–364. https://doi.org/10.5721/EuJRS20154820
   Web of Science ®Google Scholar
• Viljanen, N., Honkavaara, E., Näsi, R., Hakala, T., Niemeläinen, O., & Kaivosoja, J. J. A. (2018). A novel machine learning method for estimating biomass of grass swards using a photogrammetric canopy height model, images and vegetation indices captured by a drone. Agriculture, 8(5), 70. https://doi.org/10.3390/agriculture8050070
   Google Scholar
• Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). ‘Structure-from-motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300–314. https://doi.org/10.1016/j.geomorph.2012.08.021
   Web of Science ®Google Scholar
• Wetterdienst, D. (2022). Climate Data for Direct Download. Retrieved September 23, 2022 from https://www.dwd.de/EN/ourservices/cdc/cdc_ueberblick-klimadaten_en.html
   Google Scholar
• Yuan, W., Li, J., Bhatta, M., Shi, Y., Baenziger, P. S., & Ge, Y. J. S. (2018). Wheat height estimation using LiDAR in comparison to ultrasonic sensor and UAS. Sensors, 18(11), 3731. https://doi.org/10.3390/s18113731
   PubMed Web of Science ®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. The European Journal of Agronomy, 121, 126159. https://doi.org/10.1016/j.eja.2020.126159
   Web of Science ®Google Scholar
• Zeng, Y., Hao, D., Huete, A., Dechant, B., Berry, J., Chen, J. M., Joiner, J., Frankenberg, C., Bond-Lamberty, B., Ryu, Y. J. N. R. E., Xiao, J., Asrar, G. R., & Chen, M. (2022). Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth and Environment, 3(7), 477–493. https://doi.org/10.1038/s43017-022-00298-5
   Google Scholar
• Zhang, Z., Zhou, N., Xing, Z., Liu, B., Tian, J., Wei, H., Gao, H., & Zhang, H. (2022). Effects of temperature and radiation on yield of spring wheat at different latitudes. Agriculture, 12(5), 627. https://doi.org/10.3390/agriculture12050627
   Google Scholar
• Zhang, Z., Zhou, N., Xing, Z., Liu, B., Tian, J., Wei, H., Gao, H., & Zhang, H. J. A. (2022). Effects of temperature and radiation on yield of spring wheat at different latitudes. Agriculture, 12(5), 627. https://doi.org/10.3390/agriculture12050627
   Google Scholar