Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States

Leaf area index (LAI) is an important biophysical indicator of forest health that is linearly related to productivity, serving as a key criterion for potential nutrient management. A single equation was produced to model surface reflectance values captured from the Sentinel-2 Multispectral Instrument (MSI) with a robust dataset of field observations of loblolly pine (Pinus taeda L.) LAI collected with a LAI-2200C plant canopy analyzer. Support vector machine (SVM)-supervised classification was used to improve the model fit by removing plots saturated with aberrant radiometric signatures that would not be captured in the association between Sentinel-2 and LAI-2200C. The resulting equation, LAI = 0.310SR − 0.098 (where SR = the simple ratio between near-infrared (NIR) and red bands), displayed good performance ( R 2 = 0.81, RMSE = 0.36) at estimating the LAI for loblolly pine within the analyzed region at a 10 m spatial resolution. Our model incorporated a high number of validation plots (n = 292) spanning from southern Virginia to northern Florida across a range of soil textures (sandy to clayey), drainage classes (well drained to very poorly drained), and site characteristics common to pine forest plantations in the southeastern United States. The training dataset included plot-level treatment metrics—silviculture intensity, genetics, and density—on which sensitivity analysis was performed to inform model fit behavior. Plot density, particularly when there were ≤618 trees per hectare, was shown to impact model performance, causing LAI estimates to be overpredicted (to a maximum of X i + 0.16). Silviculture intensity (competition control and fertilization rates) and genetics did not markedly impact the relationship between SR and LAI. Results indicate that Sentinel-2’s improved spatial resolution and temporal revisit interval provide new opportunities for managers to detect within-stand variance and improve accuracy for LAI estimation over current industry standard models.

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Validation of an Integrated Estimation of Loblolly Pine (Pinus taeda L.) Leaf Area Index (LAI) Using Two Indirect Optical Methods in the Southeastern United States

Southern Journal of Applied Forestry ◽

10.1093/sjaf/32.3.101 ◽

2008 ◽

Vol 32 (3) ◽

pp. 101-110 ◽

Cited By ~ 11

Author(s):

John S. Iiames ◽

Russell Congalton ◽

Andrew Pilant ◽

Timothy Lewis

Keyword(s):

United States ◽

Leaf Area Index ◽

Leaf Area ◽

Pinus Taeda ◽

Loblolly Pine ◽

Southeastern United States ◽

Area Ratio ◽

Allometric Equations ◽

Area Index ◽

Whole Tree

Abstract Quality assessment of satellite-derived leaf area index (LAI) products requires appropriate ground measurements for validation. Since the National Aeronautics and Space Administration launch of Terra (1999) and Aqua (2001), 1-km, 8-day composited retrievals of LAI have been produced for six biome classes worldwide. The evergreen needle leaf biome has been examined at numerous validation sites, but the dominant commercial species in the southeastern United States, loblolly pine (Pinus taeda), has not been investigated. The objective of this research was to evaluate an in situ optical LAI estimation technique combining measurements from the Tracing Radiation and Architecture of Canopies (TRAC) optical sensor and digital hemispherical photography (DHP) in the southeastern US P.taeda forests. Stand-level LAI estimated from allometric regression equations developed from whole-tree harvest data were compared to TRAC–DHP optical LAI estimates at a study site located in the North Carolina Sandhills Region. Within-shoot clumping, (i.e., the needle-to-shoot area ratio [γE]) was estimated at 1.21 and fell within the range of previously reported values for coniferous species (1.2–2.1). The woody-to-total area ratio (α = 0.31) was within the range of other published results (0.11–0.34). Overall, the indirect optical TRAC–DHP method of determining LAI was similar to LAI estimates that had been derived from allometric equations from whole-tree harvests. The TRAC–DHP yielded a value 0.14 LAI units below that retrieved from stand-level whole-tree harvest allometric equations. DHP alone yielded the best LAI estimate, a 0.04 LAI unit differential compared with the same allometrically derived LAI.

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Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

Remote Sensing ◽

10.3390/rs10121942 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1942 ◽

Cited By ~ 5

Author(s):

Sosdito Mananze ◽

Isabel Pôças ◽

Mario Cunha

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Indices ◽

Cost Functions ◽

Support Vector ◽

Area Index ◽

Maize Leaf ◽

Multispectral Data ◽

Simple Ratio ◽

Sentinel 2

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

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Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2021.02.020 ◽

2021 ◽

Vol 175 ◽

pp. 71-87

Author(s):

Luke A. Brown ◽

Richard Fernandes ◽

Najib Djamai ◽

Courtney Meier ◽

Nadine Gobron ◽

...

Keyword(s):

United States ◽

Leaf Area Index ◽

Leaf Area ◽

The United States ◽

Area Index ◽

Level 2 ◽

Sentinel 2

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

Land ◽

10.3390/land10050505 ◽

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.

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Leaf Area Index (LAI) of Loblolly Pine and Emergent Vegetation Following a Harvest

Transactions of the ASABE ◽

10.13031/2013.40664 ◽

2011 ◽

Vol 54 (6) ◽

pp. 2057-2066 ◽

Cited By ~ 5

Author(s):

D. A. Sampson ◽

D. M. Amatya ◽

C. D. Blanton Lawson ◽

R. W. Skaggs

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Loblolly Pine ◽

Emergent Vegetation ◽

Area Index

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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

Remote Sensing ◽

10.3390/rs10050763 ◽

2018 ◽

Vol 10 (5) ◽

pp. 763 ◽

Cited By ~ 20

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

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

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8519120 ◽

2018 ◽

Cited By ~ 1

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

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Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing

Remote Sensing ◽

10.3390/rs13163263 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3263

Author(s):

Zhijie Liu ◽

Pengju Guo ◽

Heng Liu ◽

Pan Fan ◽

Pengzong Zeng ◽

...

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Shaanxi Province ◽

Gradient Boosting ◽

Support Vector ◽

Apple Orchards ◽

Apple Trees ◽

Area Index ◽

Spatial Efficiency

The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.

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Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

10.21203/rs.3.rs-1169494/v1 ◽

2021 ◽

Author(s):

Shuang Wu ◽

Lei Deng ◽

Lijie Guo ◽

Yanjie Wu

Keyword(s):

High Resolution ◽

Data Fusion ◽

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Prediction Accuracy ◽

Sensor Data ◽

Support Vector ◽

Area Index ◽

Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

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