Exploring the utility of Sentinel-2 for estimating maize chlorophyll content and leaf area index across different growth stages

2021 ◽  
pp. 1-13
Author(s):  
Sabelo Madonsela ◽  
Moses Azong Cho ◽  
Laven Naidoo ◽  
Russell Main ◽  
Nobuhle Majozi
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Growth Stages ◽  
Area Index ◽  
Different Growth Stages ◽  
Sentinel 2

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.

Leaf area durations and grain yields of two tropical rice varieties

1968 ◽  
Vol 8 (34) ◽  
pp. 587
Author(s):  
PC Owen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Photosynthetic Efficiency ◽  
Temperate Climate ◽  
Growth Stages ◽  
Rice Varieties ◽  
Area Index ◽  
Grain Yields ◽  
Different Growth Stages

A series of differing leaf area index regimes during the growth of two tropical rice varieties was produced by partial defoliation at different growth stages. In addition, part of the crop was completely defoliated after panicle emergence. Comparison of the effects of the range of leaf area durations (D) thus produced showed that these rice varieties differed from temperate climate cereals. Grain yields were least associated with D after panicle emergence, but were most influenced by D before emergence. This effect is mainly via an influence upon the number of spikelets formed per panicle. Grain : leaf ratio, a measure of photosynthetic efficiency, was considerably lower than values reported for wheat.

scholarly journals Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages

2021 ◽  
pp. 1-23
Author(s):  
Naichen Xing ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
Yingying Dong ◽  
Weiping Kong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Area Index ◽  
Wheat Leaf ◽  
Different Growth Stages

scholarly journals Phosphorus and Zinc Interaction Influence Leaf Area Index in Fine vs. Coarse Rice (Oryza sativa L) Genotypes in Northwest Pakistan

2016 ◽  
Vol 2 ◽  
Author(s):  
Amanullah , ◽  
Inamullah , ◽  
Zahir Shah ◽  
Shad Khan Khalil
Keyword(s):  
Oryza Sativa ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth And Yield ◽  
Growth Stages ◽  
Area Index ◽  
Physiological Maturity ◽  
Rice Genotypes ◽  
The Impact ◽  
Different Growth Stages

Leaf area index (LAI) is a measure of leafiness per unit ground area and denotes the extent of photosynthetic machinery is an important growth and yield-determining factor because it is a major determinant of light interception and transpiration. Phosphorus (P) and zinc (Zn) and rate are the most important factors affecting leaf area index (LAI) of rice(<em>Oryza sativa</em> L.). A field experiment was conducted to assess the impact of phosphorus (0, 40, 80, 120 kg P ha<sup>-1</sup>) and zinc levels (0, 5, 10, 15 kg Zn ha<sup>-1</sup>) on LAI of rice (<em>Oryza sativa</em> L.) genotypes [fine (Basmati-385) and coarse (Fakhr-e-Malakand &amp; Pukhraj)]. The experiment was conducted on farmer field at Batkhela, Malakadnd in Northwest Pakistan during summer 2011 and 2012. When combined over the two years, the data revealed that the highest LAI at three different growth stages (tillering, heading and physiological maturity) was obtained with application of the highest P level (120 kg ha<sup>-1</sup>) being at par with 80 kg P ha<sup>-1</sup>, while the lowest LAI was recorded when P was not applied. Similarly, the highest LAI was obtained with application of the two higher Zn levels (10 and 15 kg Zn ha<sup>-1</sup>), while the lowest LAI was recorded when Zn was not applied. In case of rice genotypes, the highest LAI was obtained from Pukhraj than other two genotypes at all growth stages. The other two rice genotypes (Fakher-e-Malakand and Basmati-385) produced statistically similar LAI at different growth stages. The higher LAI of Pukhraj was attributed to its long and wider leaves that resulted in higher mean single leaf area, leaf area per tiller, per hill and per square meter. The LAI was highest at heading stages than at early (tillering) and later (physiological maturity) growth stages. The increase in LAI was attributed to the increase in tillers number and leaf area hill<sup>-1</sup>. The increase in LAI showed positive impact on crop growth rate, dry matter accumulationand yield. Application of 120 kg P + 10 kg Zn ha<sup>-1</sup> to rice genotype Pukhraj was more beneficial in terms of higher LAI and productivity in the study area.

scholarly journals Estimating Forest Leaf Area Index and Canopy Chlorophyll Content with Sentinel-2: An Evaluation of Two Hybrid Retrieval Algorithms

2019 ◽  
Vol 11 (15) ◽  
pp. 1752 ◽  
Author(s):  
Luke A. Brown ◽  
Booker O. Ogutu ◽  
Jadunandan Dash
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Retrieval Algorithm ◽  
Forest Site ◽  
Area Index ◽  
Retrieval Algorithms ◽  
Deciduous Broadleaf Forest ◽  
Two Hybrid ◽  
Sentinel 2

Estimates of biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) are a fundamental requirement for effectively monitoring and managing forest environments. With its red-edge bands and high spatial resolution, the Multispectral Instrument (MSI) on board the Sentinel-2 missions is particularly well-suited to LAI and CCC retrieval. Using field data collected throughout the growing season at a deciduous broadleaf forest site in Southern England, we evaluated the performance of two hybrid retrieval algorithms for estimating LAI and CCC from MSI data: the Scattering by Arbitrarily Inclined Leaves (SAIL)-based L2B retrieval algorithm made available to users in the Sentinel Application Platform (SNAP), and an alternative retrieval algorithm optimised for forest environments, trained using the Invertible Forest Reflectance Model (INFORM). Moderate performance was associated with the SNAP L2B retrieval algorithm for both LAI (r2 = 0.54, RMSE = 1.55, NRMSE = 43%) and CCC (r2 = 0.52, RMSE = 0.79 g m−2, NRMSE = 45%), while improvements were obtained using the INFORM-based retrieval algorithm, particularly in the case of LAI (r2 = 0.79, RMSE = 0.47, NRMSE = 13%), but also in the case of CCC (r2 = 0.69, RMSE = 0.52 g m−2, NRMSE = 29%). Forward modelling experiments confirmed INFORM was better able to reproduce observed MSI spectra than SAIL. Based on our results, for forest-related applications using MSI data, we recommend users seek retrieval algorithms optimised for forest environments.

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 A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

2018 ◽  
Vol 10 (5) ◽  
pp. 763 ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco García-Haro ◽  
Lorenzo Busetto ◽  
Luigi Ranghetti ◽  
Beatriz Martínez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Polar System ◽  
Critical Comparison ◽  
Landsat 7 ◽  
Sentinel 2

Crop Yield Modelling Applying Leaf Area Index Estimated from Sentinel-2 and Proba-V Data at JECAM site in Poland

2018 ◽  
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Maciej Bartold ◽  
Radoslaw Gurdak ◽  
Martyna Gatkowska ◽  
Wojciech Kiryla ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Area Index ◽  
Sentinel 2

Using Hemispherical Photography to Determine Leaf Area Index of Landscape Trees in Urban Green Space of Dongguan, South China

2014 ◽  
Vol 955-959 ◽  
pp. 4034-4038
Author(s):  
Luo Jian Mo ◽  
Wen Bin Li ◽  
Yong Chang Ye ◽  
Yong Wen Zhou ◽  
Song Song Liu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Green Space ◽  
Urban Green Space ◽  
Growth Stages ◽  
Hemispherical Photography ◽  
Area Index ◽  
Urban Green ◽  
Large Trees ◽  
Landscape Trees

Transect sampling method was used to measure structural attributes of landscape trees in urban green space of three city parks and one residential greenbelt in Dongguan. Leaf area index (LAI) of the landscape trees in each urban green space was determined using hemispherical photography. Average DBH (diameter at the breast height) and CW(crown width) in Wenhua Square were the largest due to the presence of heritage large trees, while the landscape trees were species diverse in Renmin Park. A comparison of LAI in the green space gave a result in descending order: Renmin Park > Wenhua Square > Jinhuwan greenbelt > Yuanmei Park. The case of Renmin Park indicated that when a green space consisted of various structural attributes, landscape trees in different growth stages tended to have large LAI. Findings of our study suggested that a diversity of trees with potentially different LAI should be selected when planning urban green space.

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