Evaluating the influence of the Red Edge band from RapidEye sensor in quantifying leaf area index for hydrological applications specifically focussing on plant canopy interception

2017 ◽  
Vol 100 ◽  
pp. 73-80 ◽  
Author(s):  
Timothy Dube ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda ◽  
Cletah Shoko ◽  
Abel Chemura
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Canopy ◽  
Canopy Interception ◽  
Area Index ◽  
Red Edge ◽  
Edge Band ◽  
Hydrological Applications

Red-Edge Band Vegetation Indices for Leaf Area Index Estimation From Sentinel-2/MSI Imagery

2020 ◽  
Vol 58 (2) ◽  
pp. 826-840 ◽  
Author(s):  
Yuanheng Sun ◽  
Qiming Qin ◽  
Huazhong Ren ◽  
Tianyuan Zhang ◽  
Shanshan Chen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Area Index ◽  
Red Edge ◽  
Index Estimation ◽  
Edge Band ◽  
Sentinel 2

scholarly journals Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 505
Author(s):  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
Water Vapor ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
Linear Regression Models ◽  
Area Index ◽  
Estimation Performance ◽  
Red Edge ◽  
Sentinel 2

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study was to identify the optimal Sentinel-2 bands for LAI estimation and to derive Vegetation Indices (VI) that are well correlated with LAI. Linear regression models between time series of Sentinel-2 imagery and field-measured LAI showed that Sentinel-2 Band-8A—Narrow Near InfraRed (NIR) is more accurate for LAI estimation than the traditionally used Band-8 (NIR). Band-5 (Red edge-1) showed the lowest performance out of all red edge bands in tomato and cotton. A novel finding was that Band 9 (Water vapor) showed a very high correlation with LAI. Bands 1, 2, 3, 4, 5, 11, and 12 were saturated at LAI ≈ 3 in cotton and tomato. Bands 6, 7, 8, 8A, and 9 were not saturated at high LAI values in cotton and tomato. The tomato, cotton, and wheat LAI estimation performance of ReNDVI (R2 = 0.79, 0.98, 0.83, respectively) and two new VIs (WEVI (Water vapor red Edge Vegetation Index) (R2 = 0.81, 0.96, 0.71, respectively) and WNEVI (Water vapor narrow NIR red Edge Vegetation index) (R2 = 0.79, 0.98, 0.79, respectively)) were higher than the LAI estimation performance of the commonly used NDVI (R2 = 0.66, 0.83, 0.05, respectively) and other common VIs tested in this study. Consequently, reNDVI, WEVI, and WNEVI can facilitate more accurate agricultural monitoring than traditional VIs.

scholarly journals Performance of LAI-2200 Plant Canopy Analyzer on Leaf Area Index of Jatropha Nut Estimation

2016 ◽  
Vol 15 (4) ◽  
pp. 191-197 ◽  
Author(s):  
B.P. Lena ◽  
M.V. Folegatti ◽  
J.P. Francisco ◽  
O.N.A. Santos ◽  
I.P.S. Andrade
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Canopy ◽  
Area Index

scholarly journals Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

2020 ◽  
Vol 12 (11) ◽  
pp. 1843 ◽  
Author(s):  
Andrew Revill ◽  
Anna Florence ◽  
Alasdair MacArthur ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Two Stage ◽  
Area Index ◽  
Field Surveys ◽  
Red Edge ◽  
Calibration Approach ◽  
Sentinel 2

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.

Performance of the LAI-2000 plant canopy analyzer in estimating leaf area index of some Scots pine stands

1994 ◽  
Vol 14 (7-8-9) ◽  
pp. 981-995 ◽  
Author(s):  
P. Stenberg ◽  
S. Linder ◽  
H. Smolander ◽  
J. Flower-Ellis
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Scots Pine ◽  
Plant Canopy ◽  
Area Index ◽  
Pine Stands ◽  
Scots Pine Stands

scholarly journals Leaf area index of ground covers in a subtropical watershed

2003 ◽  
Vol 60 (3) ◽  
pp. 425-431 ◽  
Author(s):  
Alexandre Cândido Xavier ◽  
Carlos Alberto Vettorazzi
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Sugar Cane ◽  
Riparian Forest ◽  
Initial Step ◽  
Plant Canopy ◽  
Structural Variable ◽  
Forest Patches ◽  
Area Index ◽  
Plant Characteristics

Leaf Area Index (LAI), an important structural variable descriptive of vegetation, is directly related to evapotranspiration and productivity. The objective of this work was to measure and analyze monthly LAI of different ground covers in a subtropical watershed. A field campaign to collect monthly LAI data was carried out during the year 2001, with a LAI-2000 (plant canopy analyzer) device, in sugarcane, pasture, corn, eucalypt, and riparian forest patches. Riparian forest presented a maximum LAI of 4.90; LAI values decreased as precipitation decreased, as it is a characteristic of this type of semideciduous vegetation. LAI for sugar cane presented the greatest variability throughout the year, related to plant characteristics and crop management in the study area. Results represent an initial step for the understanding of LAI dynamics in the study area and areas under similar conditions.

scholarly journals Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1027 ◽  
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Michal Jenicek
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Snow Depth ◽  
Control Point ◽  
Winter Season ◽  
Snow Accumulation ◽  
Plant Canopy ◽  
Coverage Area ◽  
Area Index ◽  
Uav Images

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

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