scholarly journals Simulation and Verification of Vertical Heterogeneity Spectral Response of Winter Wheat Based on the mSCOPE Model

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4570
Author(s):  
Linsheng Huang ◽  
Yuanyuan Zhang ◽  
Guijun Yang ◽  
Dong Liang ◽  
Heli Li ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vertical Profile ◽  
Near Infrared ◽  
Spectral Response ◽  
Visible Region ◽  
Operating Efficiency ◽  
Canopy Reflectance ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Vertical Heterogeneity

Vertical heterogeneity of the biochemical characteristics of crop canopy is important in diagnosing and monitoring nutrition, disease, and crop yield via remote sensing. However, the research on vertical isomerism was not comprehensive. Experiments were carried out from the two levels of simulation and verification to analyze the applicability of this recently development model. Effects of winter wheat on spectrum were studied when input different structure parameters (e.g., leaf area index (LAI)) and physicochemical parameters (e.g., chlorophyll content (Chla+b) and water content (Cw)) to the mSCOPE (Soil Canopy Observation, Photochemistry, and Energy fluxes) model. The maximum operating efficiency was 127.43, when the winter wheat was stratified into three layers. Meanwhile, the simulation results also proved that: the vertical profile of LAI had an influence on canopy reflectance in almost all bands; the vertical profile of Chla+b mainly affected the reflectivity of visible region; the vertical profile of Cw only affected the near-infrared reflectance. The verification results showed that the vegetation indexes (VIs) selected of different bands were strongly correlated with the parameters of the canopy. LAI, Chla+b and Cw affected VIs estimation related to LAI, Chla+b and Cw respectively. The Root Mean Square Error (RMSE) of the new-proposed NDVIgreen was the smallest, which was 0.05. Sensitivity analysis showed that the spectrum was more sensitive to changes in upper layer parameters, which verified the rationality of mSCOPE model in explaining the law that light penetration in vertical nonuniform canopy gradually decreases with the increase of layers.

scholarly journals Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies

2019 ◽  
Vol 11 (10) ◽  
pp. 1150 ◽  
Author(s):  
Martin Danner ◽  
Katja Berger ◽  
Matthias Wocher ◽  
Wolfram Mauser ◽  
Tobias Hank
Keyword(s):  
Winter Wheat ◽  
Near Infrared ◽  
Seasonal Pattern ◽  
Pigment Content ◽  
Brown Pigment ◽  
Canopy Reflectance ◽  
Area Index ◽  
Geometrical Properties ◽  
Silage Maize

Decades after release of the first PROSPECT + SAIL (commonly called PROSAIL) versions, the model is still the most famous representative in the field of canopy reflectance modelling and has been widely used to obtain plant biochemical and structural variables, particularly in the agricultural context. The performance of the retrieval is usually assessed by quantifying the distance between the estimated and the in situ measured variables. While this has worked for hundreds of studies that obtained canopy density as a one-sided Leaf Area Index (LAI) or pigment content, little is known about the role of the canopy geometrical properties specified as the Average Leaf Inclination Angle (ALIA). In this study, we exploit an extensive field dataset, including narrow-band field spectra, leaf variables and canopy properties recorded in seven individual campaigns for winter wheat (4x) and silage maize (3x). PROSAIL outputs generally did not represent field spectra well, when in situ variables served as input for the model. A manual fitting of ALIA and leaf water (EWT) revealed significant deviations for both variables (RMSE = 14.5°, 0.020 cm) and an additional fitting of the brown leaf pigments (Cbrown) was necessary to obtain matching spectra at the near infrared (NIR) shoulder. Wheat spectra tend to be underestimated by the model until the emergence of inflorescence when PROSAIL begins to overestimate crop reflectance. This seasonal pattern could be attributed to an attenuated development of ALIAopt compared to in situ measured ALIA. Segmentation of nadir images of wheat was further used to separate spectral contributors into dark background, ears and leaves + stalks. It could be shown that the share of visible fruit ears from nadir view correlates positively with the deviations between field spectral measurement and PROSAIL spectral outputs (R² = 0.78 for aggregation by phenological stages), indicating that retrieval errors increase for ripening stages. An appropriate model parameterization is recommended to assure accurate retrievals of biophysical and biochemical products of interest. The interpretation of inverted ALIA as physical leaf inclinations is considered unfeasible and we argue in favour of treating it as a free calibration parameter.

scholarly journals Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

2019 ◽  
Vol 11 (15) ◽  
pp. 1809 ◽  
Author(s):  
He ◽  
Zhang ◽  
Su ◽  
Lu ◽  
Yao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Visible Region ◽  
Canopy Reflectance ◽  
Area Index ◽  
Red Edge ◽  
Rice Panicles

The emergence of rice panicle substantially changes the spectral reflectance of rice canopy and, as a result, decreases the accuracy of leaf area index (LAI) that was derived from vegetation indices (VIs). From a four-year field experiment with using rice varieties, nitrogen (N) rates, and planting densities, the spectral reflectance characteristics of panicles and the changes in canopy reflectance after panicle removal were investigated. A rice “panicle line”—graphical relationship between red-edge and near-infrared bands was constructed by using the near-infrared and red-edge spectral reflectance of rice panicles. Subsequently, a panicle-adjusted renormalized difference vegetation index (PRDVI) that was based on the “panicle line” and the renormalized difference vegetation index (RDVI) was developed to reduce the effects of rice panicles and background. The results showed that the effects of rice panicles on canopy reflectance were concentrated in the visible region and the near-infrared region. The red band (670 nm) was the most affected by panicles, while the red-edge bands (720–740 nm) were less affected. In addition, a combination of near-infrared and red-edge bands was for the one that best predicted LAI, and the difference vegetation index (DI) (976, 733) performed the best, although it had relatively low estimation accuracy (R2 = 0.60, RMSE = 1.41 m2/m2). From these findings, correcting the near-infrared band in the RDVI by the panicle adjustment factor (θ) developed the PRDVI, which was obtained while using the “panicle line”, and the less-affected red-edge band replaced the red band. Verification data from an unmanned aerial vehicle (UAV) showed that the PRDVI could minimize the panicle and background influence and was more sensitive to LAI (R2 = 0.77; RMSE = 1.01 m2/m2) than other VIs during the post-heading stage. Moreover, of all the assessed VIs, the PRDVI yielded the highest R2 (0.71) over the entire growth period, with an RMSE of 1.31 (m2/m2). These results suggest that the PRDVI is an efficient and suitable LAI estimation index.

Effects of Metal Stress on Visible/Near-Infrared Reflectance Spectra of Vegetation

2011 ◽  
Vol 347-353 ◽  
pp. 2735-2738 ◽  
Author(s):  
Guang Yu Chi ◽  
Yi Shi ◽  
Xin Chen ◽  
Jian Ma ◽  
Tai Hui Zheng
Keyword(s):  
Heavy Metal ◽  
Structural Changes ◽  
Near Infrared ◽  
Leaf Surface ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Surface Characteristics ◽  
Heavy Metal Stress ◽  
Metal Stress ◽  
Near Infrared Reflectance

Vegetation which suffers from heavy metal stresses can cause changes of leaf color, shape and structural changes. The spectral characteristics of vegetation leaves is related to leaf thickness, leaf surface characteristics, the content of water, chlorophyll and other pigments. So the eco-physiology changes of plants can be reflected by spectral reflectance. Studies on the spectral response of vegetation to heavy metal stress can provide a theoretical basis for remote sensing monitoring of metal pollution in soils. In recent decades, there are substantial amounts of literature exploring the effects of heavy metals on vegetation spectra.

CHLOROPHYLL ANALYSIS OF WHOLE RAPESEED KERNELS BY NEAR INFRARED REFLECTANCE

1982 ◽  
Vol 62 (4) ◽  
pp. 875-884 ◽  
Author(s):  
RUSSELL TKACHUK ◽  
F. D. KUZINA
Keyword(s):  
Chlorophyll Content ◽  
Near Infrared ◽  
Good Accuracy ◽  
Visible Region ◽  
Infrared Region ◽  
Multiple Correlation Coefficient ◽  
Multiple Correlation ◽  
Near Infrared Reflectance ◽  
Laboratory Results ◽  
Near Infrared Region

Chlorophyll content was determined in whole rapeseed samples, from Regent and Candle cultivars, by using a reflectance technique in the visible and near infrared region. Chlorophyll content was estimated with good accuracy when predicted results for 42 samples of Regent, 37 samples of Candle and 79 samples for both cultivars combined were compared with standard laboratory results. For Regent, a multiple correlation coefficient (R) of 0.944 and a standard error of estimate (Sy) of 4.7 were obtained when reflectance was measured at six wavelengths. For Candle, R = 0.963 and Sy = 4.4, using another six wavelengths. For both cultivars combined, R = 0.939 and Sy = 4.8, again using a different set of six wavelengths. Wavelengths for predicting chlorophyll were selected from the 630-to 754-nm visible region, and from the 1640- to 2176-nm near infrared region. This reflectance method described for whole rapeseed is rapid, involves no sample preparation, and leaves the seed intact and available for other uses.

scholarly journals Monitoring the Impact of Environmental Manipulation on Peatland Surface by Simple Remote Sensing Indices

2018 ◽  
Vol 23 ◽  
pp. 00030 ◽  
Author(s):  
Anshu Rastogi ◽  
Subhajit Bandopadhyay ◽  
Marcin Stróżecki ◽  
Radosław Juszczak
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Environmental Manipulation ◽  
Chlorophyll Index ◽  
The Impact ◽  
Remote Sensing Indices

The behaviour of nature depends on the different components of climates. Among these, temperature and rainfall are two of the most important components which are known to change plant productivity. Peatlands are among the most valuable ecosystems on the Earth, which is due to its high biodiversity, huge soil carbon storage, and its sensitivity to different environmental factors. With the rapid growth in industrialization, the climate change is becoming a big concern. Therefore, this work is focused on the behaviour of Sphagnum peatland in Poland, subjected to environment manipulation. Here it has been shown how a simple reflectance based technique can be used to assess the impact of climate change on peatland. The experimental setup consists of four plots with two kind of manipulations (control, warming, reduced precipitation, and a combination of warming and reduced precipitation). Reflectance data were measured twice in August 2017 under a clear sky. Vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), near-infrared reflectance of vegetation (NIRv), MERIS terrestrial chlorophyll index (MTCI), Green chlorophyll index (CIgreen), Simple Ration (SR), and Water Band Index (WBI) were calculated to trace the impact of environmental manipulation on the plant community. Leaf Area Index of vascular plants was also measured for the purpose to correlate it with different VIs. The observation predicts that the global warming of 1°C may cause a significant change in peatland behaviour which can be tracked and monitored by simple remote sensing indices.

scholarly journals The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

2019 ◽  
Vol 11 (17) ◽  
pp. 2050 ◽  
Author(s):  
Andrew Revill ◽  
Anna Florence ◽  
Alasdair MacArthur ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Chlorophyll Content ◽  
Management Practices ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Crop Management ◽  
Area Index ◽  
Spectral Bands ◽  
The Mean ◽  
Sentinel 2

Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency’s (ESA) Sentinel-2 satellite—delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres—could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements—the mean R2 and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R2 and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two—the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices.

Light Reflectance and Remote Sensing of Weeds in Agronomic and Horticultural Crops

Weed Science ◽  
1985 ◽  
Vol 33 (4) ◽  
pp. 569-581 ◽  
Author(s):  
R. M. Menges ◽  
P. R. Nixon ◽  
A. J. Richardson
Keyword(s):  
Remote Sensing ◽  
Aerial Photography ◽  
Near Infrared ◽  
Visible Region ◽  
Surface Characteristics ◽  
Palmer Amaranth ◽  
Canopy Reflectance ◽  
Weed Species ◽  
Crop Species ◽  
Reflectance Data

Plant canopy reflectance over the 0.45- to 1.25-μm wavelength (WL) of weed species and crops was recorded with a field spectroradiometer to evaluate the possible use of remote sensing to distinguish weeds from crops. Weed and weed-crop species reflectance differences were generally greater at the 0.85 μm WL in the near-infrared spectral region than at the 0.55 μm WL in the visible region, indicating that color infrared (CIR) aerial photography may be useful to detect weed populations in crops. Canopy reflectance data were more directly related to photographic differences in weed-crop images than were single leaf or inflorescence reflectance data. Aerial photography at altitudes of 610 to 3050 m distinguished climbing milkweed (Sarcostemma cyancboides♯ SAZCY) in orange [Citrus sinensis(L.) Osbeck. ‘Valencia’) trees; ragweed parthenium (Parthenium hysterophorusL. ♯ PTNHY) in carrot (Daucus carotaL., var.sativa‘Long Imperator’); johnsongrass [Sorghum halepense(L.) Pers. ♯ SORHA) in cotton (Gossypium hirsutumL. ‘CP 3774’) and in sorghum (Sorghum bicolorL. Moench. ‘Oro’); London rocket (Sisymbrium irioL. ♯ SSYIR) in cabbage; and Palmer amaranth (Amaranthus palmeriS. Wats. ♯ AMAPA) in cotton. Johnsongrass was also detectable with CIR film in maturing grain sorghum from 18 290 m. Detection of weed species in crops was aided by differential stages of inflorescence and senescence, and by the chlorophyll content, color, area, intercellular space, and surface characteristics of the leaves. Discrete plant community areas were determined by computer-based image analyses from a 1:8000-scale positive transparency with the efficiency of 82, 81, 68, and 100% for Palmer amaranth, johnsongrass, sorghum, and cotton, respectively. The computer analyses should permit discrete aerial surveys of weed-crop communities that are necessary for integrated crop management systems.

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