To obtain accurate spatially continuous reflectance from Unmanned Aerial Vehicle (UAV) remote sensing, UAV data needs to be integrated with the data on the ground. Here, we tested accuracy of two methods to inverse reflectance, Ground-UAV-Linear Spectral Mixture Model (G-UAV-LSMM) and Minimum Noise Fraction-Pixel Purity Index-Linear Spectral Mixture Model (MNF-PPI-LSMM). At wavelengths of 550, 660, 735 and 790 nm, which were obtained by UAV multispectral observations, we calculated the canopy abundance based on the two methods to acquire the inversion reflectance. The correlation of the inversion and measured reflectance values was stronger in G-UAV-LSMM than MNF-PPI-LSMM. We conclude that G-UAV-LSMM is the better model to obtain the canopy inversion reflectance.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Adams JB, Sabol DE, Kapos V et al (1995) Classification of multispectral images based on fractions of endmembers: application to land-cover change in the brazilian amazon. Remote Sens Environ 52:137–154. https://doi.org/10.1016/0034-4257(94)00098-8
An JP, Zhang XW, Bi SQ et al (2019) MdbHLH93, an apple activator regulating leaf senescence, is regulated by ABA and MdBT2 in antagonistic ways. New Phytol 222:735–751. https://doi.org/10.1111/nph.15628
Bian JH, Li AN, Zhang ZJ et al (2017) Monitoring fractional green vegetation cover dynamics over a seasonally inundated alpine wetland using dense time series HJ-1A/B constellation images and an adaptive endmember selection LSMM model. Remote Sens Environ 197:98–114. https://doi.org/10.1016/j.rse.2017.05.031
Cai W (2010) Recognition and area estimation of wheat based on mixed pixel decomposition of MODIS remote sensing data. Dissertation, Shandong Normal University
De Asis AM, Omasa K (2007) Estimation of vegetation parameter for modeling soil erosion using linear spectral mixture analysis of landsat ETM data. ISPRS J Photogramm 62:309–324. https://doi.org/10.1016/j.isprsjprs.2007.05.013
Degerickx J, Roberts DA, Somers B (2019) Enhancing the performance of multiple endmember spectral mixture analysis (MESMA) for urban land cover mapping using airborne lidar data and band selection. Remote Sens Environ 221:260–273. https://doi.org/10.1016/j.rse.2018.11.026
Dennison PE, Roberts DA (2003) Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE. Remote Sens Environ 87:123–135. https://doi.org/10.1016/S0034-4257(03)00135-4
Du HQ, Fan WL, Zhou GM et al (2011) Retrieval of canopy closure and lai of moso bamboo forest using spectral mixture analysis based on real scenario simulation. IEEE T Geosci Remote 49:4328–4340. https://doi.org/10.1109/TGRS.2011.2107327
Fan FL, Deng YB (2015) Enhancing endmember selection in multiple endmember spectral mixture analysis (MESMA) for urban impervious surface area mapping using spectral angle and spectral distance parameters. Int J Appl Earth Observ 36:103–105. https://doi.org/10.1016/j.jag.2014.11.004
Fang HC, Dong YH, Yue XX et al (2019) Mdcol4 interaction mediates crosstalk between uv-b and high temperature to control fruit coloration in apple. Plant Cell Physiol 60:1055–1066. https://doi.org/10.1093/pcp/pcz023
Fernández-Manso A, Quintano C, Roberts D (2012) Evaluation of potential of multiple endmember spectral mixture analysis (MESMA) for surface coal mining affected area mapping in different world forest ecosystems. Remote Sens Environ 127:181–193. https://doi.org/10.1016/j.rse.2012.08.028
Gaston E, Frias JM, Cullen PJ et al (2010) Prediction of polyphenol oxidase activity using visible near-infrared hyperspectral imaging on mushroom (Agaricus bisporus) caps. J Agr Food Chem 58:6226–6233. https://doi.org/10.1021/jf100501q
Green AA, Berman M, Switzer P et al (1988) A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE T Geosci Remote 26:65–74. https://doi.org/10.1109/36.3001
Gu HY, Li HT, Yang JH (2007) The remote sensing image fusion method based on minimum noise fraction. Remote Sens Land Res 2:53–55
Han PL, Dong YH, Gu KD et al (2019) The apple U-box E3 ubiquitin ligase MdPUB29 contributes to activate plant immune response to the fungal pathogen Botryosphaeria dothidea. Planta 249:1177–1188. https://doi.org/10.1007/s00425-018-03069-z
Hu BX, Miller JR, Chen JM et al (2004) Retrieval of the canopy leaf area index in the boreas flux tower sites using linear spectral mixture analysis. Remote Sens Environ 89:176–188. https://doi.org/10.1016/j.rse.2002.06.003
Jin LF, Lu SH, Zhu XH (1986) RS-II 4-Channel spectrometer and its specification. J Remote Sens 1:129–131
Le Maire G, François C, Dufrêne E (2004) Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens Environ 89:1–28. https://doi.org/10.1016/j.rse.2003.09.004
Lee ZP, Carder KL (2004) Absorption spectrum of phytoplankton pigments derived from hyperspectral remote-sensing reflectance. Remote Sens Environ 89:361–368. https://doi.org/10.1016/j.rse.2003.10.013
Lee JB, Woodyatt AS, Berman M (1990) Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform. IEEE T Geosci Remote 28:295–304. https://doi.org/10.1109/36.54356
Liu J, Yao GQ (2009) Research on the methods of unmixing the mixed pixels. Computer Know Tech 5:3499–3500
Liu SS, Li LT, Gao WH et al (2018) Diagnosis of nitrogen status inwinter oilseed rape (Brassica napus L.) using in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images. Comput Electron Agr 151:185–195. https://doi.org/10.1016/j.compag.2018.05.026
Quintano C, Fernández-Manso A, Roberts DA (2013) Multiple endmember spectral mixture analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries. Remote Sens Environ 136:76–88. https://doi.org/10.1016/j.rse.2013.04.017
Shanmugam P, Ahn YH, Sanjeevi S (2006) A comparison of the classification of wetland characteristics by linear spectral mixture modelling and traditional hard classifiers on multispectral remotely sensed imagery in southern India. Ecol Model 194:379–394. https://doi.org/10.1016/j.ecolmodel.2005.10.033
Thorp KR, French AN, Rango A (2013) Effect of image spatial and spectral characteristics on mapping semi-arid rangeland vegetation using multiple endmember spectral mixture analysis (MESMA). Remote Sens Environ 132:120–130. https://doi.org/10.1016/j.rse.2013.01.008
Townshend JRG, Huang C, Kalluri SNV et al (2000) Beware of per-pixel characterization of land cover. Int J Remote Sens 21:839–843. https://doi.org/10.1080/014311600210641
Wang L, Zhao GX, Zhu XC et al (2012) Quantitative remote sensing retrieval of apple tree canopy reflectance at blossom stage in hilly area. Chin J Appl Ecol 23:2233–2241
Wang L, Zhao GX, Zhu XC et al (2013) Satellite remote sensing retrieval of canopy nitrogen nutritional status of apple trees at blossom stage. Chin J Appl Ecol 24:2863–2870
Wei CW, Huang JF, Wang XZ et al (2017) Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves. Remote Sens Environ 195:56–66. https://doi.org/10.1016/j.rse.2017.03.042
Zeng Y, Schaepman ME, Wu BF et al (2009) Quantitative forest canopy structure assessment using an inverted geometric-optical model and upscaling. Int J Remote Sens 30:1385–1406. https://doi.org/10.1080/01431160802395276
Zhu HL (2005) Linear spectral unmixing assisted by probability guided and minimum residual exhaustive search for subpixel classification. Int J Remote Sens 26:5585–5601. https://doi.org/10.1080/01431160500181408
This research was supported by the National Key Research and Development Program of China, 2017YFE0122500; National Natural Science Foundation of China, 41671346; Funds of Shandong “Double Tops” Program, SYL2017XTTD02; Shandong major scientific and technological innovation project: Research demonstration and extension of orchard irrigation and fertilization in accurate management, 2018CXGC0209; The Taishan Scholar Assistance Program from Shandong Provincial Government.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Yu, R., Zhu, X., Bai, X. et al. Inversion reflectance by apple tree canopy ground and unmanned aerial vehicle integrated remote sensing data. J Plant Res (2021). https://doi.org/10.1007/s10265-020-01249-1
- Apple tree canopy
- Remote sensing