Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence
Abstract
:1. Introduction
2. Materials and Methods
2.1. Field Experiments
2.2. Canopy Spectral Reflectance Measurement
2.3. Vertical Leaf Water Content Distribution Measurement
2.4. Published Spectral Indices
2.5. Construction of New RRD Type of Spectral Indices
2.6. Data Analysis
3. Results
3.1. Vertical Variation of LWC within Wheat Canopies
3.2. Effects of Wheat Spikes on Canopy Spectral Reflectance
3.3. Effects of Wheat Spikes on Relationships between Published Spectral Indices and LWC in Vertical Layers
3.4. Estimation of Vertical LWC Distribution of Wheat Using a Method of Indirect Induction
3.5. Validation of Vertical LWC Distribution Models
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Spectral Index | Formula | Reference |
---|---|---|
Water index (WI) | [14] | |
Normalized difference water index (NDWI) | [15] | |
Moisture stress index (MSI) | [34] | |
Water band index (WBI) | [35] | |
WBI/normalized difference vegetation index (WBI/NDVI) | [14] | |
Normalized difference infrared index (NDII) | [36] | |
Reciprocal of moisture stress index (RMSI) | [36] | |
Simple ratio water index (SRWI) | [16] | |
Maximum difference water index (MDWI) | [37] | |
Composite water index (CWI) | [13] | |
Leaf water index (LWI) | [38] | |
Normalized different water stress index (NDWSI) | [39] | |
Novel image-derived index (NIDI) | [40] | |
Floating-position water band index (FWBI1) | [41] | |
Floating-position water band index (FWBI2) | [42] |
Excellent | Good | Fair | Unsuitable | |
---|---|---|---|---|
RE | <10% | 10%–20% | 20–30% | >30% |
NSE | ≥0.9 | 0.5–0.8 | - | 0.1–0.4 |
Spectral Index | R2entire canopy | R2canopy without spikes | ||||
---|---|---|---|---|---|---|
Upper-Layer | Middle-Layer | Bottom-Layer | Upper-Layer | Middle-Layer | Bottom-Layer | |
WI | 0.39 | 0.47 | 0.29 | 0.53 | 0.61 | 0.4 |
NDWI | 0.36 | 0.44 | 0.28 | 0.53 | 0.61 | 0.4 |
MSI | 0.32 | 0.4 | 0.28 | 0.46 | 0.55 | 0.39 |
WBI | 0.38 | 0.47 | 0.29 | 0.52 | 0.6 | 0.4 |
WBI/NDVI | 0.36 | 0.41 | 0.28 | 0.39 | 0.48 | 0.36 |
NDII | 0.34 | 0.41 | 0.3 | 0.48 | 0.56 | 0.39 |
RMSI | 0.36 | 0.45 | 0.31 | 0.53 | 0.6 | 0.39 |
SRWI | 0.37 | 0.45 | 0.32 | 0.55 | 0.62 | 0.4 |
MDWI | 0.27 | 0.38 | 0.24 | 0.4 | 0.49 | 0.32 |
CWI | 0.46 | 0.38 | 0.44 | 0.11 | 0.17 | 0.09 |
LWI | 0.29 | 0.39 | 0.25 | 0.41 | 0.49 | 0.27 |
NDWSI | 0.44 | 0.5 | 0.37 | 0.58 | 0.65 | 0.41 |
NIDI | 0.32 | 0.34 | 0.26 | 0.08 | 0.12 | 0.06 |
FWBI1 | 0.38 | 0.5 | 0.33 | 0.57 | 0.63 | 0.42 |
FWBI2 | 0.38 | 0.48 | 0.33 | 0.56 | 0.63 | 0.44 |
Spectral Index | Upper-Layer | Middle-Layer | Bottom-Layer |
---|---|---|---|
WI | 0.56 | 0.58 | 0.54 |
NDWI | 0.47 | 0.47 | 0.45 |
MSI | 0.40 | 0.42 | 0.38 |
WBI | 0.55 | 0.58 | 0.54 |
WBI/NDVI | 0.42 | 0.49 | 0.44 |
NDII | 0.42 | 0.44 | 0.41 |
RMSI | 0.41 | 0.41 | 0.40 |
SRWI | 0.46 | 0.47 | 0.45 |
MDWI | 0.47 | 0.50 | 0.46 |
CWI | 0.01 | 0.05 | 0.03 |
LWI | 0.45 | 0.46 | 0.44 |
NDWSI | 0.59 | 0.61 | 0.58 |
NIDI | 0.33 | 0.38 | 0.30 |
FWBI1 | 0.56 | 0.56 | 0.52 |
FWBI2 | 0.53 | 0.54 | 0.50 |
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Kong, W.; Huang, W.; Ma, L.; Tang, L.; Li, C.; Zhou, X.; Casa, R. Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence. Remote Sens. 2021, 13, 4125. https://doi.org/10.3390/rs13204125
Kong W, Huang W, Ma L, Tang L, Li C, Zhou X, Casa R. Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence. Remote Sensing. 2021; 13(20):4125. https://doi.org/10.3390/rs13204125
Chicago/Turabian StyleKong, Weiping, Wenjiang Huang, Lingling Ma, Lingli Tang, Chuanrong Li, Xianfeng Zhou, and Raffaele Casa. 2021. "Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence" Remote Sensing 13, no. 20: 4125. https://doi.org/10.3390/rs13204125