Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach

Xu, Mingzhu; Liu, Ronggao; Chen, Jing M.; Liu, Yang; Shang, Rong; Ju, Weimin; Wu, Chaoyang; Huang, Wenjiang

doi:10.1016/j.rse.2019.01.039

Cited by 108 publications

(59 citation statements)

References 48 publications

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“…The strong sensitivity of MTCI to Cab indicates great potential of Sentinel-derived red-edge VIs for Cab estimation. However, in RS cases, the additional sensitivity of these VIs to other influential parameters (e.g., leaf structure parameter, N, and LAI) should be appropriately modeled [5]. Figure 3 shows the sensitivity of MODIS-derived PRI and OLI-derived TCTG and scatter plots between predicted and true values for the two VIs.…”

Section: Resultsmentioning

confidence: 99%

“…Different combinations of leaf constituents, canopy structure, soil reflectivity, and sun-sensor geometry are given by a pre-defined look-up table (LUT) as Table 1. In general, Table 1 is adapted from previously used LUTs [3,5,6,10], but small Cab values (0-10 ug/cm 2 ) are excluded in order to avoid potential biased sensitivity resulting from unrealistic scenarios. Because the size of the LUT grows dramatically with increasing LUT variables, we use the full random sampling method provided by ARTMO GUI, which samples the LUT uniformly to generate a random subset with each variable ranging within given boundaries.…”

Section: Methodsmentioning

confidence: 99%

“…For the first class of application, i.e., retrieving certain bio-physical parameters (e.g., leaf chlorophyll a+b content, Cab, leaf water content, Cw, and leaf area index, LAI), the additional sensitivity of the used VI to confounding factors beyond the parameter of interest can induce uncertainties to the empirically established regression model. For example, the sensitivity of Cab-sensitive VIs to LAI should be suppressed when they are used to estimate Cab [5]. Recently, a comprehensive global sensitivity analysis (GSA) for Cab-, LAI-and Cw-sensitive VIs has been made on the basis of radiative transfer model (RTM) simulations to evaluate the robustness of VIs with respect to corresponding sensitive parameters [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

Wang

Dong

et al. 2019

Remote Sensing

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Remote sensing (RS) provides operational monitoring of terrestrial vegetation. For optical RS, vegetation information is generally derived from surface reflectance (ρ). More generally, vegetation indices (VIs) are built on the basis of ρ as proxies for vegetation traits. At canopy level, ρ can be affected by a variety of factors, including leaf constituents, canopy structure, background reflectivity, and sun-sensor geometry. Consequently, VIs are mixtures of different information. In this study, a global sensitivity analysis (GSA) is made for several commonly used satellite-derived VIs in order to better understand the application of these VIs at large scales. The sensitivities of VIs to different parameters are analyzed on the basis of PROSPECT-SAIL (PROSAIL) radiative transfer model simulations, which apply for homogeneous canopies, and random forest (RF) learning. Specifically, combined factors such as canopy chlorophyll content (CCC) and canopy water content (CWC) are introduced in the RF-based GSA. We find that for most VIs, the leaf area index is the most influential factor, while the broad-band sensor-derived enhanced VI (EVI) exhibits a strong sensitivity to CCC, and the universal normalized VI (UNVI) is sensitive to CWC. The potential and uncertainty for the application of all the considered VIs are analyzed according to the GSA results. The results can help to improve the use of VIs in different contexts, and the RF-based GSA method can be further applied in more sophisticated situations.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

Wang

Dong

et al. 2019

Remote Sensing

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“…This correlation is mined to retrieve corn canopy LAI and chlorophyll content jointly in this study. For describing this correlation, Xu et al [51] built up the matrices of VI pairs. For their selected VI pairs, one is sensitive to canopy LAI, and the other is sensitive to chlorophyll content.…”

Section: Joint Retrieval Of Corn Canopy Lai and Leaf Chlorophyll Usinmentioning

confidence: 99%

Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images

Su¹,

Sun

Chen

et al. 2019

Remote Sensing

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Continuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of corn canopy LAI and chlorophyll content using filtered reflectances from Sentinel-2 and MODIS data acquired during the corn growing season, which, being generally hot and rainy, results in few cloud-free Sentinel-2 images. In addition, the retrieved time series of LAI and chlorophyll content results were used to monitor the corn growth behavior in the study area. Our results showed that: (1) the joint retrieval of LAI and chlorophyll content using the proposed joint probability distribution method improved the estimation accuracy of both corn canopy LAI and chlorophyll content. Corn canopy LAI and chlorophyll content were retrieved jointly and accurately using the PROSAIL model with fused Kalman filtered (KF) reflectance images. The relation between retrieved and field measured LAI and chlorophyll content of four corn-growing stages had a coefficient of determination (R 2 ) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1-0.2 and 0.0-0.3, respectively. (2) Kalman filtering is a good way to produce continuous high-resolution reflectance images by synthesizing Sentinel-2 and MODIS reflectances. The correlation between fused KF and Sentinel-2 reflectances had an R 2 value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R 2 value of 0.8598 and RMSE of 0.0404. (3) The derived continuous KF reflectances captured the crop behavior well. Our analysis showed that the LAI increased from day of year (DOY) 181 (trefoil stage) to DOY 236 (filling stage), and then increased continuously until harvest, while the chlorophyll content first also increased from DOY 181 to DOY 236, and then remained stable until harvest. These results revealed that the jointly retrieved continuous LAI and chlorophyll content could be used to monitor corn growth conditions.

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“…The spectral variables are the most used variables, such as the normalized difference index (NDVI), the normalized difference index using the green band (NDVIg), the chlorophyll index using the green band (CIg), the enhanced vegetation index (EVI) and the soil adjusted vegetation index (SAVI). These spectral variables were widely used in estimating forest variables such as canopy cover, biomass, and leaf area index (LAI) [20][21][22][23][24]. Moreover, the contextual variables which indicated the pattern of spatial distributions of gray were performed to promote the accuracy in estimating forest parameters [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Comparison of GF2 and SPOT6 Imagery on Canopy Cover Estimating in Northern Subtropics Forest in China

Zhou

Dian

Wang

et al. 2020

Forests

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Canopy cover is an important vegetation attribute used for many environmental applications such as defining management objectives, thinning and ecological modeling. However, the estimation of canopy cover from high spatial resolution imagery is still a difficult task due to limited spectral information and the heterogeneous pixel values of the same canopy. In this paper, we compared the capacity of two high spatial resolution sensors (SPOT6 and GF2) using three ensemble learning models (Adaptive Boosting (AdaBoost), Gradient Boosting (GDBoost), and random forest (RF)), to estimate canopy cover (CC) in a Chinese northern subtropics forest. Canopy cover across 97 plots was measured across 41 needle forest plots, 24 broadleaf forest plots, and 32 mixed forest plots. Results showed that (1) the textural features performed more importantly than spectral variables according to the number of variables in the top ten predictors in estimating canopy cover (CC) in both SPOT6 and GF2. Moreover, the vegetation indices in spectral variables had a lower relative importance value than the band reflectance variables. (2) GF2 imagery outperformed SPOT6 imagery in estimating CC when using the ensemble learning model in our data. On average across the models, the R2 was almost 0.08 higher for GF2 over SPOT6. Likewise, the average RMSE and average MAE were 0.002 and 0.01 lower in GF2 than in SPOT6. (3) The ensemble learning model showed good results in estimating CC, yet the different models performed a little differently in the results. Additionally, the GDBoost model performed the best of all the ensemble learning models with R2 = 0.92, root mean square error (RMSE) = 0.001 and mean absolute error (MAE) = 0.022.

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Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach

Cited by 108 publications

References 48 publications

A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning

Joint Retrieval of Growing Season Corn Canopy LAI and Leaf Chlorophyll Content by Fusing Sentinel-2 and MODIS Images

Comparison of GF2 and SPOT6 Imagery on Canopy Cover Estimating in Northern Subtropics Forest in China

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