Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements
Keywords:
leaf water content, remote sensing, arbuscular mycorrhizal fungi, drought, cropsAbstract
Leaf water content (LWC) of crops is a suitable parameter for evaluation of plant water status and arbuscular mycorrhizal effect on the host plant under drought stress. Remote sensing technology provides an effective avenue to estimate LWC in crops. However, few LWC retrieval models have been developed specifically for the arbuscular mycorrhizal inoculated crops. In this study, soybean with inoculation and non-inoculation treatments were planted under the severe drought, moderate drought and normal irrigation levels. The LWC changes under different treatments at the 30th, 45th and 64th day after the inoculation were investigated, and the spectral response characteristics of inoculated and non-inoculated soybean leaves under the three drought stresses were analyzed. Five types of spectral variables/indices including: raw spectral reflectance (R), continuum-removed spectral reflectance (RC), difference vegetation index (DVI), normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were applied to determine the best estimator of LWC. The results indicate that LWC decreased as the aggravating of drought stress levels. However, LWC in inoculated leaves was higher than that in the counterparts under the same drought stress level, and the values of raw reflectance measured at inoculated leaves were lower than the non-inoculated leaves, especially around 1900 nm and 1410 nm. These water spectral features were more evident in the corresponding continuum-removed spectral reflectance. The newly proposed DVIC(2280, 1900) index, derived from the continuum-removed spectral reflectance at 2280 nm and the raw spectral reflectance at 1900 nm in DVI type of index, was the most robust for soybean LWC assessment, with R2 value of 0.72 (p < 0.01) and root mean square error (RMSE) and mean absolute error (MAE) of 2.12% and 1.75%, respectively. This study provides a means to monitor the mycorrhizal effect on drought-induced crops indirectly and non-destructively. Keywords: leaf water content, remote sensing, arbuscular mycorrhizal fungi, drought, crops DOI: 10.25165/j.ijabe.20191206.4950 Citation: Kong W P, Huang W J, Zhou X F, Mortimer H, Ma L L, Tang L L, et al. Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements. Int J Agric & Biol Eng, 2019; 12(6): 149–155.References
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[32] Jordan C F. Derivation of leaf-area index from quality of light on forest floor. Ecology, 1969; 50: 663–666.
[2] Zhao J, Chen C. Geography of China. Beijing: Higher Education Press, 2004.
[3] Smith S E, Read D. Mycorrhizal Symbiosis. Cambridge: Academic Press, 2008.
[4] Allen M F. Influence of vesicular-arbuscular mycorrhiza on water movement through buteloua gracilis lag ex steud. New Phytologist, 1982; 91: 191–196.
[5] Aliasgharzad N, Neyshabouri M R, Salimi G. Effects of arbuscular mycorrhizal fungi and bradyrhizobium japonicum on drought stress of soybean. Biologia, 2006; 61: S324–S328.
[6] Grumberg B C, Urcelay C, Shroeder M A, Vargas-Gil S, Luna C M. The role of inoculum identity in drought stress mitigation by arbuscular mycorrhizal fungi in soybean. Biology and Fertility of Soils, 2015; 51: 1–10.
[7] Buschmann C. Structural and functional responses to environmental stresses: Water shortage. Journal of Plant Physiology, 1991; 137: 511.
[8] Clevers J, Kooistra L. Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012; 5: 574–583.
[9] Kong W P, Huang W J, Zhou X F, Song X Y, Casa R Estimation of carotenoid content at the canopy scale using the carotenoid triangle ratio index from in situ and simulated hyperspectral data. Journal of Applied Remote Sensing, 2016; 10(2): 026035.
[10] Zhou X, Huang W, Kong W P, Ye H C, Luo J H, Chen P F. Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements. Adv. Space Res., 2016; 58: 1627–1637.
[11] Haboudane D, Miller J R, Pattey E, Zarco-Tejada P J, Strachan I B. Hyperspectral vegetation indices and novel algorithms for predicting green lai of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 2004; 90: 337–352.
[12] Ahlrichs J S, Bauer M E. Relation of agronomic and multispectral reflectance characteristics of spring wheat canopies. Agron. J., 1983; 75: 987–993.
[13] Cao Z X, Wang Q, Zheng C L. Best hyperspectral indices for tracing leaf water status as determined from leaf dehydration experiments. Ecol. Indic., 2015; 54: 96–107.
[14] Zarco-Tejada P J, Rueda C A, Ustin S L. Water content estimation in vegetation with modis reflectance data and model inversion methods. Remote Sensing of Environment, 2003; 85: 109–124.
[15] Sims D A, Gamon J A. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: A comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment, 2003; 84: 526–537.
[16] Lars G, Sarita K-S, Timo K, Elina O, Markku K. Imaging lichen water content with visible to mid-wave infrared (400-5500 nm) spectroscopy. Remote Sensing of Environment, 2018; 216: 301–310.
[17] Penuelas J, Pinol J, Ogaya R, Filella I. Estimation of plant water concentration by the reflectance water index wi (r900/r970). International Journal of Remote Sensing, 1997; 18: 2869–2875.
[18] Gao B C. Ndwi - a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing Of Environment, 1996; 58: 257–266.
[19] Wang Q, Li P H. Identification of robust hyperspectral indices on forest leaf water content using prospect simulated dataset and field reflectance measurements. Hydrol. Process., 2012; 26: 1230–1241.
[20] Rasmussen C E, Nickisch H. Gaussian processes for machine learning (gpml) toolbox. Journal of Machine Learning Research, 2010; 11: 3011–3015.
[21] Campos-Taberner M, Garcia-Haro F J, Camps-Valls G, Grau-Muedra G, Nutini F, Crema A, et al. Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote Sensing of Environment, 2016; 187: 102–118.
[22] Upreti D, Huang W J, Kong W P, Pascucci S, Pignatti S, Zhou X F, et al. A comparison of hybrid machine learning algorithms for the retrieval of wheat biophysical variables from sentinel-2. Remote Sens., 2019; 11(5): 481.
[23] Camps-Valls G, Verrelst J, Muñoz J, Laparra V, Mateo F, Gomez-Dans J. A survey on gaussian processes for earth-observation data analysis a comprehensive investigation. Ieee Geoscience And Remote Sensing Magazine, 2016; 4(2): 58–78.
[24] Clark R N, Roush T L. Reflectance spectroscopy- quantitative-analysis techniques for remote-sensing applications. Journal of Geophysical Research, 1984; 89: 6329–6340.
[25] Kokaly R F. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment, 2001; 75: 153–161.
[26] Ortiz N, Armada E, Duque E, Roldán A, Azcón R. Contribution of arbuscular mycorrhizal fungi and/or bacteria to enhancing plant drought tolerance under natural soil conditions: Effectiveness of autochthonous or allochthonous strains. Journal of Plant Physiology, 2015; 174: 87–96.
[27] Danson F M, Steven M D, Malthus T J, Clark J A. High-spectral resolution data for determining leaf water content. International Journal of Remote Sensing, 1992; 13: 461–470.
[28] Ustin S L, Riano D, Hunt E R. Estimating canopy water content from spectroscopy. Israel Journal of Plant Sciences, 2012; 60: 9–23.
[29] Kokaly R F, Skidmore A K. Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm. International Journal of Applied Earth Observation and Geoinformation, 2015; 43: 55–83.
[30] Rouse J W, Haas R H, Scheel J A, Deering D W. Monitoring vegetation systems in the great plains with erts. Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium, 1974; 1: 48–62.
[31] Liu S S, Peng Y, Du W, Le Y, Li L. Remote estimation of leaf and canopy water content in winter wheat with different vertical distribution of water-related properties. Remote Sens., 2015; 7: 4626–4650.
[32] Jordan C F. Derivation of leaf-area index from quality of light on forest floor. Ecology, 1969; 50: 663–666.
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Published
2019-12-04
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Kong, W., Huang, W., Zhou, X., Mortimer, H., Ma, L., Tang, L., & Li, C. (2019). Estimating leaf water content at the leaf scale in soybean inoculated with arbuscular mycorrhizal fungi from in situ spectral measurements. International Journal of Agricultural and Biological Engineering, 12(6), 149–155. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4950
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