Estimation of Canopy Water Content by Means of Hyperspectral Indices Based on Drought Stress Gradient Experiments of Maize in the North Plain China

Zhang, Feng; Zhou, Guangsheng

doi:10.3390/rs71115203

Cited by 52 publications

(49 citation statements)

References 42 publications

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“…Different researchers have used reflectance at red and INR wavelengths in detection of nitrogen stress in maize crop (Walburg et al 1982;Blackmer et al 1996; Stone et al 1996). Zhang and Zhou (2015) demonstrated that the canopy water information at the red edge of 680 nm-780 nm. For prediction of leaf area index Zhao et al (2007), used red edge bands (690 nm-710 nm and 750 nm-900 nm), Stagakis et al (2010) combined green (580 nm), red edge (700 nm and 710 nm), and near infrared wavelengths (1003 nm); Haboudane et al (2003) also combined green (550 nm), red (670 nm) and near infrared (800 nm) wavelengths ( Table 2).…”

Section: Selective Spectral Bands In Prediction Of Biophysical Paramementioning

confidence: 99%

Identification of Water and Nitrogen Stress Indicative Spectral Bands Using Hyperspectral Remote Sensing in Maize During Post-Monsoon Season

Naik

Naveen

Sreenivas

et al. 2020

J Indian Soc Remote Sens

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Realization of agricultural crop condition through field survey is quite expensive, time consuming and sometimes not practical for remote locations. Optical remote sensing techniques can provide information on real condition of the crops by observing spectral reflectance at different crop growth phases and is less expensive and less time consuming. Hyperspectral remote sensing provides a unique opportunity for non-destructive, timely and accurate estimation of crop biophysical and biochemical properties. In this study, a field experiment was conducted to identify the water and nitrogen stress indicative spectral bands using ground-based hyperspectral data and to assess the predictive capability of selective bands on yield of maize under water and nitrogen stress environment. The experiment comprised of three irrigation scheduling treatments based on IW/CPE ration of 0.6, 0.8 and 1.2 and three nitrogen level treatments, i.e., 100, 200 and 300 kg of N ha−1, respectively, with three replications in a split plot design. The spectral reflectance was measured before irrigation at tasseling and dough stage of the maize crop using portable field spectroradiometer. The results of stepwise multiple linear regression indicated the highest predicting capability of spectral bands 540 nm, 780 nm and 860 nm for leaf nitrogen and 700 nm, 740 nm and 860 nm for leaf water content. The derived biophysical parameters based on spectral reflectance viz. relative leaf water content (%), leaf area index and leaf nitrogen contentment (%) at tasseling stage of maize crop accounted for 80%, 61% and 66% variation in grain yield, respectively.

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Section: Selective Spectral Bands In Prediction Of Biophysical Paramementioning

confidence: 99%

Identification of Water and Nitrogen Stress Indicative Spectral Bands Using Hyperspectral Remote Sensing in Maize During Post-Monsoon Season

Naik

Naveen

Sreenivas

et al. 2020

J Indian Soc Remote Sens

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“…In this study, spectral vegetation indices linked to measured leaf traits were calculated from the spectral reflectance data collected from the Landsat OLI sensor. In previous work [64][65][66], the selected SVIs have shown sensitivity to stress-induced variations in chlorophyll and also water content, which are all important indicators of tree health. This sensitivity could potentially be used to assess stomatal closure and tree water stress due to insect infestation.…”

Section: Spectral Vegetation Indicesmentioning

confidence: 99%

Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation

et al. 2019

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In this study, the early stage of European spruce bark beetle (Ips typographus, L.) infestation (so-called green attack) is investigated using Landsat-8 optical and thermal data. We conducted an extensive field survey in June and the beginning of July 2016, to collect field data measurements from several infested and healthy trees in the Bavarian Forest National Park (BFNP), Germany. In total, 157 trees were selected, and leaf traits (i.e. stomatal conductance, chlorophyll fluorescence, and water content) were measured. Three Landsat-8 images from May, July, and August 2016 were studied, representing an early stage, advanced stage, and post-infestation, respectively. Spectral vegetation indices (SVIs) sensitive to the measured traits were calculated from the optical domain (VIS, NIR, and SWIR), and canopy surface temperature (CST) was calculated from the thermal infrared band using the mono-window algorithm. The leaf traits were used to examine the impact of bark beetle infestation on the infested trees and to explore the link between these traits and remote sensing data (CST and SVIs). The differences between healthy and infested samples regarding measured leaf traits were assessed using Student’s t test. The relative importance of the CST and SVIs for estimating measured leaf traits was evaluated based on the variable importance in projection (VIP) obtained from the partial least squares regression (PLSR) analysis. A temporal comparison was then made for SVIs with a VIP > 1, including CST, using statistical significance tests. The clustering method using a principal components analysis (PCA) was used to examine visually how well the two groups of sample plots (healthy and infested) are separated in 2-D space based on principal component scores. Finally, linear regression (LR) was used to generate the leaf traits maps using the SVI that have highest VIP score and then used to produce a stress map for the study area. The results revealed that all measured leaf traits were significantly different (p < 0.05) between healthy versus infested samples. Moreover, the study showed that CST was superior to the SVIs in detecting subtle canopy changes due to bark beetle infestation for the three months considered in this study. The results showed that CST is an essential variable for estimating measured leaf traits with VIP > 1, improving the results of clustering when used with other SVIs. Likewise, the stress map produced by CST and leaf traits well presented the infestation areas at the green attacked stage. The new insight offered by this study is that the stress induced by the early stage of bark beetle infestation is more pronounced by Landsat-8 thermal bands than the SVIs calculated from its optical bands. The potential of CST in detecting the green attack stage would have positive implications for forest practice.

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“…For whole canopies of plants under field conditions, near-infrared (NIR, 700-1100 nm) spectral indices are useful water stress indicators [80,81]. Thus, field-measured hyperspectral remote sensing data is being successfully used to estimate leaf water content and leaf water potential in vineyards [82][83][84][85], cotton fields [86] and maize [87], among other crops.…”

Section: Nir Spectroscopymentioning

confidence: 99%

Plant-Based Methods for Irrigation Scheduling of Woody Crops

Fernández

2017

Horticulturae

131

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Abstract:The increasing world population and expected climate scenarios impel the agricultural sector towards a more efficient use of water. The scientific community is responding to that challenge by developing a variety of methods and technologies to increase crop water productivity. Precision irrigation is intended to achieve that purpose, through the wise choice of the irrigation system, the irrigation strategy, the method to schedule irrigation, and the production target. In this review, the relevance of precision irrigation for a rational use of water in agriculture, and methods related to the use of plant-based measurements for both the assessment of plant water stress and irrigation scheduling, are considered. These include non-automated, conventional methods based on manual records of plant water status and gas exchange, and automated methods where the related variable is recorded continuously and automatically. Thus, the use of methodologies based on the Scholander chamber and portable gas analysers, as well as those of systems for measuring sap flow, stem diameter variation and leaf turgor pressure, are reviewed. Other methods less used but with a potential to improve irrigation are also considered. These include those based on measurements related to the stem and leaf water content, and to changes in electrical potential within the plant. The use of measurements related to canopy temperature, both for direct assessment of water stress and for defining zones with different irrigation requirements, is also addressed. Finally, the importance of choosing the production target wisely, and the need for economic analyses to obtain maximum benefit of the technology related to precision irrigation, are outlined.

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Estimation of Canopy Water Content by Means of Hyperspectral Indices Based on Drought Stress Gradient Experiments of Maize in the North Plain China

Cited by 52 publications

References 42 publications

Identification of Water and Nitrogen Stress Indicative Spectral Bands Using Hyperspectral Remote Sensing in Maize During Post-Monsoon Season

Identification of Water and Nitrogen Stress Indicative Spectral Bands Using Hyperspectral Remote Sensing in Maize During Post-Monsoon Season

Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation

Plant-Based Methods for Irrigation Scheduling of Woody Crops

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