Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index

Korhonen, Lauri; Hadi,; Packalén, Petteri; Rautiainen, Miina

doi:10.1016/j.rse.2017.03.021

Cited by 308 publications

(153 citation statements)

References 64 publications

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“…Based on the spectral bands and vegetation indices extracted from Sentinel-2 data, this study has shown that B5 (Red-Edge 1) was the most important variable when estimating the FSV using both the machine learning methods and MLR method, which had been confirmed in recent studies concerning forest prediction [102] and tree species classification [103]. In the gross primary productivity field (GPP), Lin et al found that the red-edge band was useful for estimating the GPP, and noted that the red-edge reflectance was sensitive to the leaf chlorophyll content [104].…”

Section: Discussionsupporting

confidence: 63%

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Yang

et al. 2020

Remote Sensing

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The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R 2 = 0.91, RMSE = 35.13 m 3 ha −1 , n = 321) and in the test phase (i.e., R 2 = 0.58, RMSE = 65.03 m 3 ha −1 , and n = 138). This model was followed by the SVR model (R 2 = 0.54, RMSE = 65.60 m 3 ha −1 , n = 321 in training; R 2 = 0.54, RMSE = 66.00 m 3 ha −1 , n = 138 in testing), which was slightly better than the MLR model (R 2 = 0.38, RMSE = 75.74 m 3 ha −1 , and n = 321 in training; R 2 = 0.49, RMSE = 70.22 m 3 ha −1 , and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 10 8 m 3 ) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 10 8 m 3 ). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.The forest stock volume (FSV, m 3 ha −1 ) is the sum of the stem volumes of all the living trees per unit area, and is one of key forest variables for forest resources management and assessments on local, region and country scales [1]. FSVs have a strong relationship with the aboveground biomass (AGB) and carbon stocks [2]. To understand the spatial distribution of carbon in forests and to derive predictions for monitoring carbon stock trends, the FSV must be quantified [3]. Traditionally, the FSV is estimated by sampling several plots, which involves substantial manpower, materials, and financial resources [4]. With the development of remote sensing technology, particularly the Landsat series since 1972, satellite imagery has played an important role in forest inventory. Various studies have been performed to estimate the forest variables ...

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Section: Discussionsupporting

confidence: 63%

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Yang

et al. 2020

Remote Sensing

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“…Among them, the “red edge”, which reflects the change of reflectivity from the low value band of chlorophyll red absorption to the high value band of canopy scattering, is the most obvious feature of the green vegetation spectral curve 8 . This change is due to the scattering of leaves and canopy 7 . Chlorophyll forms strong absorption peaks in blue and red bands, absorption valleys in the green band and very little absorption in the near-infrared band.…”

Section: Methodsmentioning

confidence: 99%

“…This reflects the composition and structure of an object and remote sensing technology could achieve the goal of detecting the features and properties of the objects 5 . The application of remote sensing technology to the rapid monitoring of chlorophyll content in apple trees is of great significance for guiding the scientific management of apple trees 6,7 .…”

Section: Introductionmentioning

confidence: 99%

Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging

Cheng

Zhu

Wei

et al. 2018

Sci Rep

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The remote sensing technology provides a new means for the determination of chlorophyll content in apple trees that includes a rapid analysis, low cost and large monitoring area. The Back-Propagation Neural Network (BPNN) and the Supported Vector Machine Regression (SVMR) methods were both frequently used method to construct estimation model based on remote sensing imaging. The aim of this study was to find out which estimation model of apple tree canopy chlorophyll content based on the vegetation indices constructed with visible, red edge and near-infrared bands of the sensor of Sentinel-2 was more accurate and stabler. The results were as follows: The calibration set coefficient of determination (R2) value of 0.729 and validation set R2 value of 0.667 of the model using the SVMR method based on the vegetation indices (NDVIgreen + NDVIred + NDVIre) were higher than those of the model using the BPNN method by 8.2% and 11.0%, respectively. The calibration set root mean square error (RMSE) of 0.159 and validation set RMSE of 0.178 of the model using the SVMR method based on the vegetation indices (NDVIgreen + NDVIred + NDVIre) were lower than those of the model using the BPNN method by 5.9% and 3.8%, respectively.

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“…The main possible reason for this improvement in the performances with the Sentinel-2A dataset is the availability of red-edge spectral bands. The efficacy of red-edge spectral bands of different sensors are analysed in vegetation related earlier studies (Eitel et al 2011;Korhonen et al 2017;Shoko and Mutanga 2017;Ustuner et al 2014) and proven to be very efficient for vegetation monitoring and classification. The improvements in the average classification performances by introducing the NDIs as features in the classifier model are insignificant for both the satellite datasets but in case of 50% of trials (i.e.…”

Section: Comparison Of Classification Performancesmentioning

confidence: 99%

“…Different studies had evaluated the performances of Landsat-8 and Sentinel-2 data based on diversified applications viz. detection of greenhouse (Novelli et al 2016), estimation of forest canopy cover and Leaf Area Index (LAI) (Korhonen et al 2017), detection of C3 and C4 grass species (Shoko and Mutanga 2017), land use land cover mapping (Forkuor et al 2018), characterization of reflectance and Normalized Difference Vegetation Index (NDVI) (Zhang et al 2018) etc. and in most of the applications it was evident that Sentinel-2 was performing better because of its improved spatial and spectral capabilities.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Landsat-8 and Sentinel-2 Data for Classification of Rabi Crops Over Karnataka, India

Paul

Kumar

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Classification of crops is very important to study different growth stages and forecast yield. Remote sensing data plays a significant role in crop identification and condition assessment over a large spatial scale. Importance of Normalized Difference Indices (NDIs) along with surface reflectances of remotely sensed spectral bands have been evaluated for classification of eight types of Rabi crops utilizing the Landsat-8 and Sentinel-2 datasets and performances of both the satellites are compared. Landsat-8 and Sentinel-2A images are acquired for the location of crops and seven and nine spectral bands are utilized respectively for the classification. Experiments are carried out considering the different combinations of surface reflectances of spectral bands and optimal NDIs as features in support vector machine classifier. Optimal NDIs are selected from the set of 7 C 2 and 9 C 2 NDIs of Landsat-8 and Sentinel-2A datasets respectively using the partial informational correlation measure, a nonparametric feature selection approach. Few important vegetation indices (e.g. enhanced vegetation index) are also experimented in combination with the surface reflectances and NDIs to perform the crop classification. It has been observed that combination of surface reflectances and optimal NDIs can classify the crops more efficiently. The average overall accuracy of 80.96% and 88.16% are achieved using the Landsat-8 and Sentinel-2A datasets respectively. It has been observed that all the crop classes except Paddy and Cotton achieve producer accuracy and user accuracy of more than 75% and 85% respectively. This technique can be implemented for crop identification with adequate accessibility of crop information.

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Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index

Cited by 308 publications

References 64 publications

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Estimating apple tree canopy chlorophyll content based on Sentinel-2A remote sensing imaging

Comparison of Landsat-8 and Sentinel-2 Data for Classification of Rabi Crops Over Karnataka, India

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