Abstract
Aboveground biomass is an important indicator used to characterize the growth status of crops, as well as an important physical and chemical parameter in agroecosystems. Aboveground biomass is an important basis for formulating management measures such as fertilization and irrigation. We selected four irrigated wheat fields in a region near Kaifeng, Henan Province, for this study. The terrain in that region was undulating and had spatial differences. We used a low-altitude unmanned aerial vehicle (UAV) remote sensing platform equipped with a multispectral camera, thermal infrared camera, and RGB camera to simultaneously obtain different remote sensing parameters during the key growth stages of wheat. Based on the extracted spectral reflectivity, thermal infrared temperature, and digital elevation information, we calculated the spatial variability of remote sensing parameters and growth indices under different terrain characteristics. We also analyzed the correlations between vegetation indices, temperature parameters, structural topographic parameters and aboveground biomass. Three machine learning methods were used, including the multiple linear regression method (MLR), partial least squares regression method (PLSR) and random forest regression method (RFR). We compared the aboveground biomass (AGB) estimation capability of single-modal data versus multimodal data fusion frameworks. The results showed that slope was an important factor affecting crop growth and aboveground biomass. We therefore analyzed several remote sensing parameters for three different slope scales. We found significant differences among them for soil water content, water content of plants, and aboveground biomass at four growth stages. Based on the strength of their correlation with aboveground biomass, seven vegetation indices (NDVI, GNDVI, NDRE, MSR, OSAVI, SAVI, and MCARI), four canopy structure parameters (CH, VF, CVM, SLOPE) and two temperature parameters (NRCT, CTD) were selected as the final input variables for the model. There was some variability in the accuracy of the models at different growth stages. The average accuracy of the models was anthesis stage > booting stage > filling stage > jointing stage. For the single-modal data framework, the model constructed with the vegetation indices was better than the aboveground biomass model constructed using the temperature or structure parameters, and the highest accuracy was obtained with an RFR model based on vegetation indices at the anthesis stage (R2 = 0.713). For the double modal data fusion approach, the highest accuracy resulted at the anthesis stage, using the structural parameters combined with the vegetation indices of the RFR model (R2 = 0.842). Even higher accuracies were obtained using the multimodal data fusion approach with an RFR model based on vegetation indices, temperature parameters and structure parameters at the anthesis stage (R2 = 0.897). By introducing terrain factors and combining them with the RFR algorithm to effectively integrate multimodal data, the complementary and synergistic effects between different remote sensing information sources could be fully exerted. The accuracy and stability of the aboveground biomass estimation models were effectively improved, and a high-throughput phenotype acquisition method was explored, which provides a reference and basis for real-time monitoring of crop growth and decoding the correlation between genotype and phenotype.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Anatoly, A. G., & Gitelson, M. N. (1998). Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research., 22, 689–692. https://doi.org/10.1016/S0273-1177(97)01133-2
Araus, J. L., & Cairns, J. E. (2014). Field high-throughput phenotyping: The new crop breeding frontier. Trends in Plant Science, 19, 52–61. https://doi.org/10.1016/j.tplants.2013.09.008
Avitabile, V., Baccini, A., Friedl, M. A., & Schmullius, C. (2012). Capabilities and limitations of landsat and land cover data for aboveground woody biomass estimation of uganda. Remote Sensing of Environment, 117, 366–380. https://doi.org/10.1016/j.rse.2011.10.012
Bareth, G., Bolten, A., Gnyp, M. L., Reusch, S., & Jasper, J. (2016). Comparison of uncalibrated RGBVI with spectrometer-based NDVI derived from UAV sensing systems on field scale. ISPRS- International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences., 8, 837–843. https://doi.org/10.5194/isprsarchives-XLI-B8-837-2016
Batlles, F. J., Bosch, J. L., Tovar, P. J., Martínez, M., Ortega, R., & Miralles, I. (2008). Determination of atmospheric parameters to estimate global radiation in areas of complex topography: Generation of global irradiation map. Energ Convers Manage., 49, 336–345. https://doi.org/10.1016/j.enconman.2007.06.012
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 features 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
Daughtry, C., Walthall, C. L., Kim, M. S., Colstoun, E., & Iii, M. M. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, 229–239. https://doi.org/10.1016/S0034-4257(00)00113-9
Ediriweera, S., Pathirana, S., & Danaher, T. (2014). Estimating above-ground biomass by fusion of LiDAR and multispectral data in subtropical woody plant communities in topographically complex terrain in North-eastern Australia. Journal Forestry Research, 25, 761–771. https://doi.org/10.1007/s11676-014-0485-7
Elarab, M., Ticlavilca, A. M., Torres-Rua, A. F., Maslova, I., & McKee, M. (2015). Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. The International Journal of Applied Earth Observation and Geoinformation, 43, 32–42. https://doi.org/10.1016/j.jag.2015.03.017
Elsayed, S., Rischbeck, P., & Scmidhalter, U. (2015). Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed barley cultivars. Field Crops Research, 177, 148–160. https://doi.org/10.1016/j.fcr.2015.03.010
Elsayed, S., Elhoweity, M., Ibrahim, H. H., Dewir, Y. H., Migdadi, H. M., & Schmidhalter, U. (2017). Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes. Agricultural Water Management, 189, 98–110. https://doi.org/10.1016/j.agwat.2017.05.001
Fang, J. J., Ma, W. Y., Zhao, X. Q., Xue, H., & Li, Z. S. (2012). Lower canopy temperature is associated with higher cytokinin concentration in the flag leaf of wheat. Crop Science, 52, 2743. https://doi.org/10.2135/cropsci2012.03.0163
Geneviève, R., Michael, S., & Frédéric, B. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95–107. https://doi.org/10.1016/0034-4257(95)00186-7
Greaves, H. E., Vierling, L. A., Eitel, J. U. H., Boelman, N. T., Magney, T. S., Prager, C. M., & Griffin, K. L. (2015). Estimating aboveground biomass and leaf area of low-stature arctic shrubs with terrestrial lidar. Remote Sensing of Environment, 164, 26–35. https://doi.org/10.1016/j.rse.2015.02.023
Guo, J. X., Tian, G. L., Zhou, Y., Wang, M., Ling, N., Shen, Q. R., & Guo, S. W. (2016). Evaluation of the grain yield and nitrogen nutrient status of wheat (Triticum aes-tivum L.) using thermal imaging. Field Crops Research, 196, 463–472. https://doi.org/10.1016/j.fcr.2016.08.008
Han, L., Yang, G., Dai, H., Xu, B., Yang, H., Feng, H., Li, Z., & Yang, X. (2019). Modeling maize above-ground aboveground biomass based on machine learning approaches using UAV remote sensing data. Plant Methods, 15, 1–19. https://doi.org/10.1186/s13007-019-0394-z
He, L., Assengb, S., Zhao, G., Wu, D. R., Yang, X. Y., Zhuang, W., Jin, N., & Yu, Q. (2015). Impacts of recent climate warming, cultivar changes, and crop management on winter wheat phenology across the Loess Plateau of China. Agricultural Forest Meteorology, 200, 135–143. https://doi.org/10.1016/j.agrformet.2014.09.011
Hossein, A., Mohsen, A., Davoud, A., Salehi, S. H., & Soheil, R. (2018). Machine learning regression techniques for the silage maize yield prediction using time-series images of landsat 8 oli. IEEE J-Stars, 12, 4563–4577. https://doi.org/10.1109/jstars.2018.2823361
Hou, A., Chen, P., Alloatti, J., Li, D., Mozzoni, L., Zhang, B., & Shi, A. (2009). Genetic variability of seed sugar content in worldwide soybean germplasm collections. Crop Science, 49, 903–912. https://doi.org/10.2135/cropsci2008.05.0256
Huete, A. R. (1988). Soil-adjust vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309. https://doi.org/10.1016/0034-4257(88)90106-X
Jat, M. L., Singh, Y., Gill, G., Sidhu, H., Aryal, J. P., Stirling, C., & C., and Gerard, B.,. (2015). Laser assisted precision land leveling: Impacts in Irrigated intensive production systems of South Asia. Advances in Soil Science, 13, 323–352. https://doi.org/10.1201/b18759-14
Jayathunga, S., Owari, T., & Tsuyuki, S. (2018). Evaluating the performance of photogrammetric products using fixed-wing UAV imagery over a mixed conifer-broadleaf forest: Comparis on with airborne laser scanning. Remote Sensing, 10, 187. https://doi.org/10.3390/rs10020187
Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., Timlin, D. J., Shim, K. M., Gerber, J. S., Reddy, V. R., & Kim, S. H. (2016). Random forests for global and regional crop yield predictions. PLoS ONE, 11, e0156571. https://doi.org/10.1371/journal.pone.0156571
Juliane, B., Andreas, B., Simon, B., Janis, B., Silas, E., & Georg, B. (2014). Estimating biomass of barley using crop surface models (CSMS) derived from UAV-based RGB imaging. Remote Sensing, 6, 10395–10412. https://doi.org/10.3390/rs61110395
Li, B., Xu, X., Zhang, L., Han, J., & Jin, L. (2020a). Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 161–172. https://doi.org/10.1016/j.isprsjprs.2020.02.013
Li, W., Jiang, J., Weiss, M., Madec, S., Tison, F., Philippe, B., Comar, A., & Baret, F. (2020b). Impact of the reproductive organs on crop BRDF as observed from a UAV. Remote Sensing of Environment. https://doi.org/10.1016/J.RSE.2021.112433
Liu, Y., Liu, S., Li, J., Guo, X., Wang, S., & Lu, J. (2019). Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images. Computers and Electronics in Agriculture, 166, 105026. https://doi.org/10.1016/j.compag.2019.105026
Ma, Y. Y., Guan, H. X., Yang, H. X., Shao, S., Shao, Y. Q., & Liu, H. J. (2021). A new method to improve the accuracy of digital elevation model in northeast China by using terrain, soil and crop Information. Scientia Agricultura Sinica, 54, 1715–1727. https://doi.org/10.3864/j.issn.0578-1752.2021.08.011
Maimaitijiang, M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K., Shavers, E., Fishman, J., Peterson, J., Kadam, S., Burken, J., & Fritschi, F. (2017). Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 43–58. https://doi.org/10.1016/j.isprsjprs.2017.10.011
Maimaitijiang, M., Sagan, V., Sidike, P., Maimaitiyiming, M., Hartling, S., Peterson, K. T., Maw, M. J. W., Shakoor, N., Mockler, T., & Fritschi, F. B. (2019). Vegetation index weighted canopy volume model (CVMVI) for soybean biomass estimation from unmanned aerial system-based RGB imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 27–41. https://doi.org/10.1016/j.isprsjprs.2019.03.003
Maresma, Á., Ariza, M., Martínez, E., Lloveras, J., & Martínez, C. J. A. (2018). Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays L.) from a standard UAV service. Remote Sensing, 10, 368. https://doi.org/10.3390/rs10030368
Marino, S., & Alvino, A. (2020). Agronomic traits analysis of ten winter wheat cultivars clustered by UAV-derived vegetation features. Remote Sensing, 12, 249. https://doi.org/10.3390/rs12020249
Marshall, M., & Thenkabail, P. (2015). Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM plus, and MODIS vegetation indices in crop aboveground biomass estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 205–218. https://doi.org/10.1016/j.isprsjprs.2015.08.001
Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., & Foley, J. A. (2012). Closing yield gaps through nutrient and water management. Nature, 490, 254–257. https://doi.org/10.1038/nature11907
Nawar, S., Buddenbaum, H., Hill, J., & Kozak, J. (2014). Modeling and mapping of soil salinity with reflectance spectroscopy and landsat data using two quantitative methods (PLSR and MARS). Remote Sensing, 6, 10813–10834. https://doi.org/10.3390/rs61110813
Nicolas, V., Valentine, L., Sébastien, M., Evelyne, C., Sylvain, L., & Regnard, J. L. (2014). Stress indicators based on airborne thermal imagery for field phenotyping a heterogeneous tree population for response to water constraints. Journal of Experimental Botany, 65, 5429–5442. https://doi.org/10.1093/jxb/eru309
Nie, X. D., Guo, W., Huang, B., Zhuo, M. N., Li, D. Q., Li, Z. W., & Yuan, Z. J. (2019). Effects of soil properties, topography and landform on the understory aboveground biomass of a pine forest in a subtropical hilly region. CATENA, 176, 104–111. https://doi.org/10.1016/j.catena.2019.01.007
Purushothaman, R., Thudi, M., Krishnamurthy, L., Upadhyayaa, H. D., Kashiwagib, J., Gowdaa, C. L. L., & Varshney, R. K. (2015). Association of mid-reproductive stage canopy temperature depression with the molecular markers and grain yields of chickpea (Cicer arietinum L.) germplasm under terminal drought. Field Crops Research, 174, 1–11. https://doi.org/10.1016/j.fcr.2015.01.007
Qi, J., Kerr, Y., Moran, M., Weltz, M., Huete, A., Sorooshian, S., & Bryant, R. (2000). Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region. Remote Sensing of Environment, 73, 18–30. https://doi.org/10.1016/S0034-4257(99)00113-3
Ramos, A., Osco, L. P., Furuya, D., Gonalves, W. N., & Pistori, H. (2020). A random forest ranking approach to predict yield in maize with UAV-based vegetation spectral indices. Computers and Electronics in Agriculture, 178, 105791. https://doi.org/10.1016/j.compag.2020.105791
Rischbeck, P., Elsayed, S., Mistele, B., Barmeier, G., Heil, K., & Scmidhalter, U. (2016). Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley. European Journal of Agronomy, 78, 44–59. https://doi.org/10.1016/j.eja.2016.04.013
Roth, L., Aasen, H., Walter, A., & Liebisch, F. (2018). Extracting leaf area index using viewing geometry effects—a new perspective on high-resolution unmanned aerial system photography. ISPRS J Photogramm, 414, 161–175. https://doi.org/10.1016/j.isprsjprs.2018.04.012
Sagan, V., Maimaitijiang, M., Sidike, P., Eblimit, K., Peterson, K. T., Hartling, S., Esposito, F., Khanal, K., Newcomb, M., & Pauli, D. (2019). UAV-based high resolution thermal imaging for vegetation monitoring, and plant Phenotyping using ICI 8640 P, FLIR Vue pro R 640, and thermoMap cameras. Remote Sensing, 11, 330. https://doi.org/10.3390/rs11030330
Shi, Y. Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., Rajan, N., Rouze, G., Morgan, C. L. S., Neely, H. L., Rana, A., Bagavathiannan, M. V., Henrickson, J., Bowden, E., Valasek, J., Olsenholler, J., Bishop, M. P., Sheridan, R., Putman, E. B., … Yang, C. H. (2016). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS ONE, 11, e0159781. https://doi.org/10.1371/journal.pone.0159781
Sun, L. N., Wang, M. B., & Fan, X. H. (2020). Spatial pattern and driving factors of biomass carbon density for natural and planted coniferous forests in mountainous terrain, eastern Loess Plateau of China. For Ecosyst, 7, 9. https://doi.org/10.1186/s40663-020-0218-7
Takyu, M., Aiba, S. I., & Kitayama, K. (2003). Changes in aboveground biomass, productivity and decomposition along topographical gradients under different geological conditions in tropical lower montane forests on mount Kinabalu, Borneo. Oecologia, 134, 397–404. https://doi.org/10.1007/s00442-002-1115-1
Tao, H., Feng, H., Xu, L., Miao, M., Long, H., Yue, J., Li, Z., Yang, G., Yang, X., & Fan, L. (2020). Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data. Sensors, 20, 1296. https://doi.org/10.3390/s20051296
Torres-Sanchez, J., Pena, J. M., Castro, A. I., & Lopez-Granados, F. (2014). Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture, 103, 104–113. https://doi.org/10.1016/j.compag.2014.02.009
Valtera, M., Šamonil, S., & M., Janda, P. (2015). Effects of topography and forest stand dynamics on soil morphology in three natural Picea abies mountain forests. Plant and Soil, 392, 57–69. https://doi.org/10.1007/s11104-015-2442-4
Wang, L., & G., Tian, Y, G., Yao, X., Zhu, Y., Cao, W, X. (2014). Predicting grain yield and protein content in wheat by fusing multisensory and multi-temporal remote-sensing images. Field Crops Res, 164, 178–188. https://doi.org/10.1016/j.fcr.2014.05.001
Wang, C., Nie, S., Xi, X. H., Luo, S. Z., & Sun, X. F. (2017). Estimating the aboveground biomass of maize with hyperspectral and LiDAR data. Remote Sensing, 11, 1–12. https://doi.org/10.3390/rs9010011
Weiss, M., Jacob, B., & Diveiller, G. (2020). Remote sensing for agricultural applications: a meta-review. Remote Sensing of Environment, 236, 111402. https://doi.org/10.1016/j.rse.2019.111402
Xu, L., Zhan, X., Yu, T., Nie, L., Huang, J., Cui, K., Wang, F., Li, Y., & Peng, S. (2018). Yield performance of direct-seeded, double-season rice using varieties with short growth durations in central China. Field Crops Research, 227, 49–55. https://doi.org/10.1016/j.fcr.2018.08.002
Xu, J., Ma, J., Tang, Y., Wu, W., Shao, J., Wu, W., Wei, S., Liu, Y., Wang, Y., & Guo, H. (2020). Estimation of sugarcane yield using a machine learning approach based on UAV-LiDAR data. Remote Sensing, 12, 2823. https://doi.org/10.3390/rs12172823
Yu, N., Li, L. J., Scmitz, N., Tiaz, L. F., Greenberg, J. A., & Diers, B. W. (2016). Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform. Remote Sensing of Environment, 187, 91–101. https://doi.org/10.1016/j.rse.2016.10.005
Yue, J., Yang, G., Tian, Q., Feng, H., Xu, K., & Zhou, C. (2019). Estimate of winter-wheat above-ground aboveground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation features. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 226–244. https://doi.org/10.1016/j.isprsjprs.2019.02.022
Zaman-Allah, M., Vergara, O., Araus, J. L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P. J., Hornero, A., Albà, A. H., Das, B., Craufurd, P., Olsen, M., Prasanna, B. M., & Cairns, J. (2015). Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods, 11, 35. https://doi.org/10.1186/s13007-015-0078-2
Zarco-Tejada, P. J., González-Dugo, V., & Berni, J. (2012). Fluorescence, temperature and narrow-band indices acquired from a uav platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment, 117, 322–337. https://doi.org/10.1016/j.rse.2011.10.007
Zha, H., Miao, Y., Wang, T., Li, Y., Zhang, J., Sun, W., Feng, Z., & Kusnierek, K. (2020). Improving unmanned aerial vehicle remote sensing-based rice nitrogen nutrition index prediction with machine learning. Remote Sensing, 12, 215. https://doi.org/10.3390/rs12020215
Zhang, X. L., Xu, M. Y., Liu, H. J., Meng, L. H., Qiu, Z. C., Pan, Y., & Xie, Y. H. (2017). Remote sensing inversion models and validation of aboveground biomass in soybean with introduction of terrain factors in black soil area. Trans CSAE, 33, 168–173. https://doi.org/10.11975/j.issn.1002-6819.2017.16.022
Zhu, G. L., Liu, Y. B., Ju, W. M., & Chen, J. M. (2013). Evaluation of topographic effects on four commonly used vegetation indices. Journal of Remote Sensing, 17, 210–234.
Acknowledgements
This work was supported by grants from the Thirteenth Five-year Plan of National Key Research Project of China (2018YFD0300701), the National Agriculture Technology Research System of China (CARS-03-01-22) and the research start-up fund to young talents of Henan Agricultural University (30601648).
Funding
The National Agriculture Technology Research System of China, CARS-03-01-22, Tiancai Guo
Author information
Authors and Affiliations
Contributions
WF, S-HZ, US: conceived and designed the research. S-HZ, LH, J-ZD and S-LZ: analyzed the data and wrote the manuscript. T-CY, C-YW and T-CG: provided data and data acquisition capacity.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
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 (e.g. a society or other partner) 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.
About this article
Cite this article
Zhang, SH., He, L., Duan, JZ. et al. Aboveground wheat biomass estimation from a low-altitude UAV platform based on multimodal remote sensing data fusion with the introduction of terrain factors. Precision Agric 25, 119–145 (2024). https://doi.org/10.1007/s11119-023-10062-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-023-10062-4