Validation and Comparison of Cropland Leaf Area Index Retrievals from Sentinel-2/MSI Data Using Sl2P Processor and Vegetation Indices Models

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.

Download Full-text

The Utility of Sentinel-2 MSI Data to Estimate Wetland Vegetation Leaf Area Index in Natural and Rehabilitated Wetlands

Geographies ◽

10.3390/geographies1030011 ◽

2021 ◽

Vol 1 (3) ◽

pp. 178-191

Author(s):

Nonjabulo Neliswa Tshabalala ◽

Onisimo Mutanga ◽

Mbulisi Sibanda

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Indices ◽

Wetland Vegetation ◽

Electromagnetic Spectrum ◽

Vegetation Productivity ◽

Area Index ◽

Wetland Ecosystems ◽

Red Edge ◽

Sentinel 2

Wetland ecosystems are being modified and threatened due to anthropogenic activities and climate change, hence the urgent need for wetland restoration. Wetland rehabilitation is important in the reversal of these dire conditions, and this can be pursued through restoring damaged wetland ecosystems and recovering wetland vegetation. Wetland biophysical properties such as leaf area index (LAI) are important indicators of vegetation productivity and stress. Therefore, the study sought to test the potential of Sentinel-2 multispectral instrument (MSI) derived standard bands, traditional vegetation indices and red-edge derived vegetation indices in estimating wetland vegetation LAI across natural and rehabilitated wetlands. Traditional field surveys were carried out for LAI measurement of wetland vegetation using the LAI-2200 Plant Canopy Analyser. Partial Least Squares Regression (PLSR) algorithms were used to compare the estimation strength of models derived from all Sentinel-2 MSI bands, conventional vegetation indices and red-edge derived vegetation indices. Leave-one-out cross-validation (LOOCV) was completed on a selected measured dataset to evaluate the performance and accuracy of the estimation models. The optimal models for estimating wetland vegetation LAI were produced based on red-edge bands centred between the 705–783 nm as well as the 865 nm (Band 8a) of the electromagnetic spectrum. The results showed that vegetation indices derived from red-edge bands performed better at estimating LAI for both wetlands with a root mean square error of prediction (RMSE) of 0.32 m2/m2 and R2 of 0.61 for the natural wetland, and RMSE of 0.51 m2/m2 and R2 of 0.75 for the rehabilitated wetland. The optimal model for predicting LAI across natural and rehabilitated wetlands was attained based on red-edge bands centred at 705 nm (Band 5), 740 nm (Band 6), 783 nm (Band 7) as well as 865 nm (Band 8a) yielding a RMSE of 0.51 m2/m2 and R2 of 0.54. Overall, the results underscore the importance of remotely sensed derived data and vegetation indices in the optimal characterisation of wetland vegetation productivity which can be utilized in the monitoring and management of wetland ecosystems.

Download Full-text

Relationship between Fraction of Photosynthetically Active Radiation and Vegetation Indices, Leaf Area Index of Corn and Soybean

ACTA AGRONOMICA SINICA ◽

10.3724/sp.j.1006.2008.02046 ◽

2009 ◽

Vol 34 (11) ◽

pp. 2046-2052

Author(s):

Fei YANG

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Photosynthetically Active Radiation ◽

Vegetation Indices ◽

Area Index ◽

Active Radiation

Download Full-text

Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices

Agricultural Water Management ◽

10.1016/j.agwat.2021.106906 ◽

2021 ◽

Vol 252 ◽

pp. 106906

Author(s):

Guomin Shao ◽

Wenting Han ◽

Huihui Zhang ◽

Shouyang Liu ◽

Yi Wang ◽

...

Keyword(s):

Random Forest ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Indices ◽

Crop Coefficient ◽

Random Forest Algorithm ◽

Maize Crop ◽

Area Index

Download Full-text

The effect of using different vegetation indices for mangrove leaf area index modelling

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/500/1/012006 ◽

2020 ◽

Vol 500 ◽

pp. 012006

Author(s):

Aldo Restu Agi Prananda ◽

Muhammad Kamal ◽

Denny Wijaya Kusuma

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Indices ◽

Area Index

Download Full-text

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.

Download Full-text