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

Kaplan, Gregoriy; Rozenstein, Offer

doi:10.3390/land10050505

Cited by 20 publications

(9 citation statements)

References 46 publications

(62 reference statements)

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“…In this study, this coefficient of determination was 0.45 for field measurements and 0.35 for PROSAIL simulation, which are within the variation of previous reports. However, we found EVI to produce R 2 = 0.26 and 0.27 with LAI for field measurements and model simulations in our study, respectively, lower than the average value (R 2 = 0.66) of the three species (tomato, cotton and wheat) reported by Kaplan et al [64]. Again, this can be attributed to the larger variation in MTA and partly attributed to soil spectral properties used in the current study.…”

Section: Discussioncontrasting

confidence: 91%

“…Leaf inclination angle had large effects on red NIR-based vegetation indices for LAI or leaf chlorophyll content estimation [65][66][67]. The statistical relationships between LAI and S2 VIs for single species have been assessed in previous studies, and the R 2 for NDVI has been found to differ largely between wheat, maize and alfalfa (R 2 = 0.14-0.72) [68], and between tomato, cotton and wheat (R 2 = 0.05-0.83) [64]. In this study, this coefficient of determination was 0.45 for field measurements and 0.35 for PROSAIL simulation, which are within the variation of previous reports.…”

Section: Discussionmentioning

confidence: 96%

“…The situation may be different if different datasets are studied. Kaplan et al [64] reported that the band with the strongest correlation with LAI was band 8 in a single species analysis for cotton, tomato and wheat. However, the average R 2 between S2 band 4 and the LAI of the three species in [64] was 0.49, which agreed with our study.…”

Section: Discussionmentioning

confidence: 99%

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Estimation of Canopy Structure of Field Crops Using Sentinel-2 Bands with Vegetation Indices and Machine Learning Algorithms

2022

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Leaf angle distribution (LAD), or the leaf mean tilt angle (MTA) capturing its central value, is used to quantify the direction of the leaf surface in a canopy and is one of the most important canopy structuraltraits. Combined with the other important structure parameter, leaf area index (LAI), LAD determines the light interception of a crop canopy. However, unlike LAI, only few studies have addressed the direct retrieval of LAD or MTA from remote sensing data. Recently, it has been shown that the red edge is a key spectral region where the effect of leaf angle on crop spectral reflectance can be separated from that of other structural variables. The Multispectral imager (MSI) onboard the Sentinel-2 (S2) satellite has two specially designed red-edge channels in this spectral region and thus can potentially be used for large-scale mapping of MTA at high spatial and temporal resolutions. Unfortunately, no field data on leaf angles at the scale of S2 pixel are available. Therefore, we simulated 5000 observations of different crops using the PROSAIL canopy reflectance model. Further, we used the MTA and LAI data of six crop species growing in 162 experimental plots in Finland and simulated their reflectance signal in S2 bands by resampling AISA airborne imaging spectroscopy data. Four common machine learning regression algorithms (random forest, support vector machine, multilayer perceptron network and partial least squares regression) were examined for retrieving canopy structure parameters, including leaf angle, from the simulated reflectances. Further, we analyzed the utility of 12 vegetation indices (VIs) well known to be sensitive to canopy structure for canopy structure estimation. Six of the studied indices used information from the visible part of the spectrum and the near infrared (NIR) while another six were selected to also utilize the red edge bands specific to S2. We found that S2 band 6 in the red edge had a strong correlation with MTA (R2 = 0.79 in model simulation and R2 = 0.87 in field measurements) but a low correlation with LAI (R2 = 0.07 in model simulation and R2= 0.06 in field measurements). Of the six red edge-based VIs, four (NDVIRE, CIRE, WDRVIRE and MSRRE) depended less on MTA than the visible NIR-based VIs and thus could be useful for estimating LAI for any LAD. The other two red edge-based VIs, IRECI and S2REP, had stronger correlations with MTA (R2 = 0.67 and 0.52, respectively) than LAI (R2 = 0.24 and 0.19, respectively). Additionally, MTA was accurately estimated (RMSE = 1.1–2.4° in model simulations and RMSE = 2.2–3.9° in field measurements) using the four 10 m spatial resolution bands with the RF, SVM and MLP algorithms, without information in the red edge. These promising results indicate the capability of S2 in accurately mapping the MTA of field crops on a large scale.

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Section: Discussioncontrasting

confidence: 91%

Section: Discussionmentioning

confidence: 96%

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Estimation of Canopy Structure of Field Crops Using Sentinel-2 Bands with Vegetation Indices and Machine Learning Algorithms

2022

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“…The LAI data for potato, corn and cotton were interpolated from 6, 12, and 9 data points, respectively, which were graphically generated based on previous studies (Bufon et al, 2011;Liu et al, 2002;Monteiro et al, 2006;Villa et al, 2017). The interpolated LAI data were then adjusted (rescaled) according to local studies (Gitelson et al, 2003;Kaplan & Rozenstein, 2021;Nguy-Robertson et al, 2014). All of the irrigation, LAI, and meteorological data that we used are available in the Data Set S1 in Supporting Information S1 and in Weissman et al (2022).…”

Section: Modeling Setupmentioning

confidence: 99%

Probability Distribution for Water Fluxes in a Heterogeneous Unsaturated Zone Using an Ensemble of 1D Simulations

Weissman

Dahan

Bel

2022

Water Resources Research

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“…Four main classes of retrieval techniques from optical remotely sensed images have been used for LAI estimations over the years (Verrelst et al, 2019). Parametric regression methods rely on the explicit relationships between LAI and vegetation indices (VIs) (Ali et al, 2015;Brogi et al, 2020;Kaplan & Rozenstein, 2021;Xing et al, 2020). Linear and nonlinear nonparametric regression methods, e.g., Gaussian Process Regression (Rivera-Caicedo et al, 2017), artificial neural networks (Chen et al, 2020), random forests (Abdelbaki et al, 2021), and support vector machines (Tuia et al, 2011;Zhang et al, 2021), do not assume any explicit relationship between LAI and spectral reflectance.…”

Section: Introductionmentioning

confidence: 99%

LAI estimation through remotely sensed NDVI following hail defoliation in maize (Zea mays L.) using Sentinel-2 and UAV imagery

et al. 2023

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Extreme events such as hailstorms are a cause for concern in agriculture, leading to both economic and food supply losses. Traditional damage estimation techniques have recently been called into question since damages have rarely been quantified at the large-field scale. Damage-estimation methods used by field inspectors are complex and sometimes subjective and hardly account for damage spatial variability. In this work, a normalized difference vegetation index (NDVI)-based parametric method was applied using both unmanned aerial vehicles (UAV) and Sentinel-2 sensors to estimate the leaf area index (LAI) of maize (Zea mays L.) resulting from simulated hail damage. These methods were then compared to the LAI values generated from the Sentinel-2 Biophysical Processor. A two-year experiment (2020–2021) was conducted during the maize cropping season, with hail events simulated during a range of maize developmental stages (the 8th-leaf, flowering, milky and dough stages) using a 0–40% defoliation gradient of damage intensities performed with the aid of specifically designed prototype machines. The results showed that both sensors were able to accurately estimate LAI in a nonstandard damaged canopy while requiring only the calibration of the extinction coefficient $$k(\vartheta )$$ k ( ϑ ) in the case of parametric estimations. In this case, the calibration was performed using 2020 data, providing $$k(\vartheta )$$ k ( ϑ ) values of 0.59 for Sentinel-2 and 0.37 for the UAV sensor. The validation was performed on 2021 data, and showed that the UAV sensor had the best accuracy (R2 of 0.86, root-mean-square error (RMSE) of 0.71). The $$k(\vartheta )$$ k ( ϑ ) value proved to be sensor-specific, accounting for the NDVI retrieval differences likely caused by the different spatial operational scales of the two sensors. NDVI proved effective in parametrically estimating maize LAI under damaged canopy conditions at different defoliation degrees. The parametric method matched the Sentinel-2 biophysical process-generated LAI well, leading to less underestimations and more accurate LAI retrieval.

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

Cited by 20 publications

References 46 publications

Estimation of Canopy Structure of Field Crops Using Sentinel-2 Bands with Vegetation Indices and Machine Learning Algorithms

Estimation of Canopy Structure of Field Crops Using Sentinel-2 Bands with Vegetation Indices and Machine Learning Algorithms

Probability Distribution for Water Fluxes in a Heterogeneous Unsaturated Zone Using an Ensemble of 1D Simulations

LAI estimation through remotely sensed NDVI following hail defoliation in maize (Zea mays L.) using Sentinel-2 and UAV imagery

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