In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Site
2.2. Spectral Data Acquisition and Processing
2.3. Leaf Water Content
2.4. Spectral Data Analysis
2.4.1. Single Wavelengths for LWC Monitoring
2.4.2. Broadband Reflectance and Vegetation Indices for LWC Monitoring
2.4.3. Narrowband Vegetation Indices for LWC Monitoring
2.4.4. Partial Least Squares Regression (PLSR) for LWC Monitoring
3. Results and Discussion
3.1. Maize Leaf Water Content
3.2. Maize Leaf and Canopy Reflectance
3.3. Single Wavelengths for LWC Monitoring
3.4. Broadband Reflectance and Vegetation Indices for LWC Monitoring
3.5. Narrowband Vegetation Indices for LWC Monitoring
3.6. Partial Least Squares Regression Models for LWC Monitoring
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- United States Department of Agriculture (USDA). World Agricultural Production. Circular Series WAP 8–21 August 2021. 2021. Available online: https://apps.fas.usda.gov/PSDOnline/Circulars/2021/08/production.pdf (accessed on 20 September 2021).
- He, H.; Hu, Q.; Li, R.; Pan, X.; Huang, B.; He, Q. Regional gap in maize production, climate and resource utilization in China. Field Crops Res. 2020, 254, 107830. [Google Scholar] [CrossRef]
- El-Hendawy, S.E.; Al-Suhaibani, N.A.; Elsayed, S.; Hassan, W.M.; Dewir, Y.H.; Refay, Y.; Abdella, K.A. Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates. Agric. Water Manag. 2019, 217, 356–373. [Google Scholar] [CrossRef]
- Meng, Q.; Hou, P.; Wu, L.; Chen, X.; Cui, Z.; Zhang, F. Understanding production potentials and yield gaps in intensive maize production in China. Field Crops Res. 2013, 143, 91–97. [Google Scholar] [CrossRef] [Green Version]
- Ge, X.; Wang, J.; Ding, J.; Cao, X.; Zhang, Z.; Liu, J.; Li, X. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. PeerJ 2019, 7, e6926. [Google Scholar] [CrossRef] [PubMed]
- Shu, M.; Shen, M.; Zuo, J.; Yin, P.; Wang, M.; Xie, Z.; Tang, J.; Wang, R.; Li, B.; Yang, X.; et al. The Application of UAV-Based Hyperspectral Imaging to Estimate Crop Traits in Maize Inbred Lines. Plant Phenomics 2021, 2021, 9890745. [Google Scholar] [CrossRef] [PubMed]
- Mirzaie, M.; Darvishzadeh, R.; Shakiba, A.; Matkan, A.A.; Atzberger, C.; Skidmore, A. Comparative analysis of different uni-and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements. Int. J. Appl. Earth. Obs. 2014, 26, 1–11. [Google Scholar] [CrossRef]
- Ronay, I.; Ephrath, J.E.; Eizenberg, H.; Blumberg, D.G.; Maman, S. Hyperspectral Reflectance and Indices for Characterizing the Dynamics of Crop–Weed Competition for Water. Remote Sens. 2021, 13, 513. [Google Scholar] [CrossRef]
- Kovar, M.; Brestic, M.; Sytar, O.; Barek, V.; Hauptvogel, P.; Zivcak, M. Evaluation of hyperspectral reflectance parameters to assess the leaf water content in soybean. Water 2019, 11, 443. [Google Scholar] [CrossRef] [Green Version]
- Zhou, H.; Zhou, G.; He, Q.; Zhou, L.; Ji, Y.; Lv, X. Capability of leaf water content and its threshold values in reflection of soil–plant water status in maize during prolonged drought. Ecol. Indic. 2021, 124, 107395. [Google Scholar] [CrossRef]
- Finn, M.P.; Lewis, M.; Bosch, D.D.; Giraldo, M.; Yamamoto, K.; Sullivan, D.G.; Kincaid, R.; Luna, R.; Allam, G.K.; Kvien, C.; et al. Remote sensing of soil moisture using airborne hyperspectral data. GISci. Remote Sens. 2011, 48, 522–540. [Google Scholar] [CrossRef]
- Al-Moustafa, T.; Armitage, R.P.; Danson, F.M. Mapping fuel moisture content in upland vegetation using airborne hyperspectral imagery. Remote Sens. Environ. 2012, 127, 74–83. [Google Scholar] [CrossRef]
- Ullah, S.; Skidmore, A.K.; Ramoelo, A.; Groen, T.A.; Naeem, M.; Ali, A. Retrieval of leaf water content spanning the visible to thermal infrared spectra. ISPRS J. Photogramm. Remote Sens. 2014, 93, 56–64. [Google Scholar] [CrossRef]
- Ge, Y.; Atefi, A.; Zhang, H.; Miao, C.; Ramamurthy, R.K.; Sigmon, B.; Yang, J.; Schnable, J.C. High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: A case study with a maize diversity panel. Plant Methods 2019, 15, 66. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zygielbaum, A.I.; Gitelson, A.A.; Arkebauer, T.J.; Rundquist, D.C. Non-destructive detection of water stress and estimation of relative water content in maize. Geophys. Res. Lett. 2009, 36. [Google Scholar] [CrossRef] [Green Version]
- Ge, Y.; Bai, G.; Stoerger, V.; Schnable, J.C. Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Comput. Electron. Agric. 2016, 127, 625–632. [Google Scholar] [CrossRef] [Green Version]
- Wijewardana, C.; Alsajri, F.A.; Irby, J.T.; Krutz, L.J.; Golden, B.; Henry, W.B.; Gao, W.; Reddy, K.R. Physiological assessment of water deficit in soybean using midday leaf water potential and spectral features. J. Plant Interact. 2019, 14, 533–543. [Google Scholar] [CrossRef] [Green Version]
- Braga, P.; Crusiol, L.G.T.; Nanni, M.R.; Caranhato, A.L.H.; Fuhrmann, M.B.; Nepomuceno, A.L.; Neumaier, N.; Farias, J.R.B.; Koltun, A.; Gonçalves, L.S.A.; et al. Vegetation indices and NIR-SWIR spectral bands as a phenotyping tool for water status determination in soybean. Precis. Agric. 2021, 22, 249–266. [Google Scholar] [CrossRef]
- Liu, 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. [Google Scholar] [CrossRef] [Green Version]
- Liu, L.; Zhang, S.; Zhang, B. Evaluation of hyperspectral indices for retrieval of canopy equivalent water thickness and gravimetric water content. Int. J. Remote Sens. 2016, 37, 3384–3399. [Google Scholar] [CrossRef]
- Feng, W.; Qi, S.; Heng, Y.; Zhou, Y.; Wu, Y.; Liu, W.; He, L.; Li, X. Canopy vegetation indices from in situ hyperspectral data to assess plant water status of winter wheat under powdery mildew stress. Front. Plant Sci. 2017, 8, 1219. [Google Scholar] [CrossRef]
- Zhang, J.; Zhang, W.; Xiong, S.; Song, Z.; Tian, W.; Shi, L.; Ma, X. Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content. Plant Methods 2021, 17, 34. [Google Scholar] [CrossRef] [PubMed]
- Zhang, L.; Zhou, Z.; Zhang, G.; Meng, Y.; Chen, B.; Wang, Y. Monitoring the leaf water content and specific leaf weight of cotton (Gossypium hirsutum L.) in saline soil using leaf spectral reflectance. Eur. J. Agron. 2012, 41, 103–117. [Google Scholar] [CrossRef]
- Yi, Q.X.; Bao, A.M.; Wang, Q.; Zhao, J. Estimation of leaf water content in cotton by means of hyperspectral indices. Comput. Electron. Agric. 2013, 90, 144–151. [Google Scholar] [CrossRef]
- Boshkovski, B.; Doupis, G.; Zapolska, A.; Kalaitzidis, C.; Koubouris, G. Hyperspectral Imagery Detects Water Deficit and Salinity Effects on Photosynthesis and Antioxidant Enzyme Activity of Three Greek Olive Varieties. Sustainability 2022, 14, 1432. [Google Scholar] [CrossRef]
- Jackson, T.J.; Chen, D.; Cosh, M.; Li, F.; Anderson, M.; Walthall, C.; Doriaswamy, P.; Hunt, E.R. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sens. Environ. 2004, 92, 475–482. [Google Scholar] [CrossRef]
- García-Haro, F.J.; Campos-Taberner, M.; Moreno, Á.; Tagesson, H.T.; Camacho, F.; Martínez, B.; Sánchez, S.; Piles, M.; Camps-Valls, G.; Yebra, M.; et al. A global canopy water content product from AVHRR/Metop. ISPRS J. Photogramm. Remote Sens. 2020, 162, 77–93. [Google Scholar] [CrossRef]
- Cao, Z.; Wang, Q.; Zheng, C. Best hyperspectral indices for tracing leaf water status as determined from leaf dehydration experiments. Ecol. Indic. 2015, 54, 96–107. [Google Scholar] [CrossRef]
- Ma, S.; Zhou, Y.; Gowda, P.H.; Dong, J.; Zhang, G.; Kakani, V.G.; Wagle, P.; Chen, L.; Flynn, K.C.; Jiang, W. Application of the water-related spectral reflectance indices: A review. Ecol. Indic. 2019, 98, 68–79. [Google Scholar] [CrossRef]
- Song, L.; Jin, J.; He, J. Effects of Severe Water Stress on Maize Growth Processes in the Field. Sustainability 2019, 11, 5086. [Google Scholar] [CrossRef] [Green Version]
- Mahlein, A.-K.; Steiner, U.; Dehne, H.-W.; Oerke, E.-C. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precis. Agric. 2010, 11, 413–431. [Google Scholar] [CrossRef]
- Feng, L.; Zhang, Z.; Ma, Y.; Du, Q.; Williams, P.; Drewry, J.; Luck, B. Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning. Remote Sens. 2020, 12, 2028. [Google Scholar] [CrossRef]
- ESA—The European Space Agency. Sentinel-2 User Guide. Available online: https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi (accessed on 31 March 2021).
- Wang, F.M.; Huang, J.F.; Tang, Y.L.; Wang, X.Z. New vegetation index and its application in estimating leaf area index of rice. Rice Sci. 2007, 14, 195–203. [Google Scholar] [CrossRef]
- Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 1996, 58, 289–298. [Google Scholar] [CrossRef]
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ. 1974, 351, 309. [Google Scholar]
- Gitelson, A.; Merzlyak, M.N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 1994, 143, 286–292. [Google Scholar] [CrossRef]
- Mehdaoui, R.; Anane, M. Exploitation of the red-edge bands of Sentinel 2 to improve the estimation of durum wheat yield in Grombalia region (Northeastern Tunisia). Int. J. Remote Sens. 2020, 41, 8986–9008. [Google Scholar] [CrossRef]
- Gao, B.C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
- Hardisky, M.A.; Klemas, V.; Smart, M. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of spartina alterniflora canopies. Photogramm. Eng. Remote Sens. 1993, 49, 77–83. [Google Scholar]
- Yendrek, C.R.; Tomaz, T.; Montes, C.M.; Cao, Y.; Morse, A.M.; Brown, P.J.; McIntyre, L.M.; Leakey, A.D.B.; Ainsworth, E.A. High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. Plant Physiol. 2017, 173, 614–626. [Google Scholar] [CrossRef]
- Crusiol, L.G.T.; Nanni, M.R.; Furlanetto, R.H.; Sibaldelli, R.N.R.; Cezar, E.; Sun, L.; Foloni, J.S.S.; Mertz-Henning, L.M.; Nepomuceno, A.L.; Neumaier, N.; et al. Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression. Remote Sens. 2021, 13, 977. [Google Scholar] [CrossRef]
- Csajbók, J.; Buday-Bódi, E.; Nagy, A.; Fehér, Z.Z.; Tamás, A.; Virág, I.C.; Bojtor, C.; Forgács, F.; Vad, A.M.; Kutasy, E. Multispectral Analysis of Small Plots Based on Field and Remote Sensing Surveys—A Comparative Evaluation. Sustainability 2022, 14, 3339. [Google Scholar] [CrossRef]
- Prey, L.; Schmidhalter, U. Simulation of satellite reflectance data using high-frequency ground based hyperspectral canopy measurements for in-season estimation of grain yield and grain nitrogen status in winter wheat. ISPRS J. Photogramm. Remote Sens. 2019, 149, 176–187. [Google Scholar] [CrossRef]
- Abdulridha, J.; Ampatzidis, Y.; Roberts, P.; Kakarla, S.C. Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence. Biosyst. Eng. 2020, 197, 135–148. [Google Scholar] [CrossRef]
- Damm, A.; Paul-Limoges, E.; Haghighi, E.; Simmer, C.; Morsdorf, F.; Schneider, F.D.; Tol, C.V.D.; Migliavacca, M.; Rascher, U. Remote sensing of plant-water relations: An overview and future perspectives. J. Plant Physiol. 2018, 227, 3–19. [Google Scholar] [CrossRef]
- Carter, G.A. Primary and secondary effects of water content on the spectral reflectance of leaves. Am. J. Bot. 1991, 78, 916–924. [Google Scholar] [CrossRef]
- Sakamoto, T. Incorporating environmental variables into a MODIS-based crop yield estimation method for United States corn and soybeans through the use of a random forest regression algorithm. ISPRS J. Photogramm. Remote Sens. 2020, 160, 208–228. [Google Scholar] [CrossRef]
Spectral Band | Central Wavelength (nm) | Bandwidth (nm) |
---|---|---|
BLUE | 492.4 | 66 |
GREEN | 559.8 | 36 |
RED | 664.6 | 31 |
RE1 | 704.1 | 15 |
RE2 | 740.5 | 15 |
RE3 | 782.8 | 20 |
NIR | 832.8 | 106 |
SWIR1 | 1373.5 | 31 |
SWIR2 | 1613.7 | 91 |
SWIR3 | 2202.4 | 175 |
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Crusiol, L.G.T.; Sun, L.; Sun, Z.; Chen, R.; Wu, Y.; Ma, J.; Song, C. In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data. Sustainability 2022, 14, 9039. https://doi.org/10.3390/su14159039
Crusiol LGT, Sun L, Sun Z, Chen R, Wu Y, Ma J, Song C. In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data. Sustainability. 2022; 14(15):9039. https://doi.org/10.3390/su14159039
Chicago/Turabian StyleCrusiol, Luís Guilherme Teixeira, Liang Sun, Zheng Sun, Ruiqing Chen, Yongfeng Wu, Juncheng Ma, and Chenxi Song. 2022. "In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data" Sustainability 14, no. 15: 9039. https://doi.org/10.3390/su14159039