Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations

Gao, Si; Zhong, Run; Yan, Kai; Ma, Xuanlong; Chen, Xinkun; Pu, Jiabin; Gao, Sicong; Qi, Jianbo; Yin, Gaofei; Myneni, Ranga B.

doi:10.1016/j.rse.2023.113665

Cited by 41 publications

(8 citation statements)

References 87 publications

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“…That might be the reason why CNN outperforms other models. Extensive studies have revealed that the relationships between LAI and spectral information are intrinsically nonlinear ( Ma et al., 2022 ; Gao et al., 2023 ). In general, the RF regression model demonstrates remarkable robustness in handling high-dimensional data and nonlinear relationships, while PLSR, being a linear regression method, is not adequately equipped to capture these intricate nonlinear relationships between spectral reflectance and LAI.…”

Section: Discussionmentioning

confidence: 99%

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches

Zu,

Yang,

Wang

et al. 2024

Front. Plant Sci.

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Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R2 = 0.83), followed by the RF model (R2 = 0.82), with the PLSR model exhibited the lowest accuracy (R2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.

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Section: Discussionmentioning

confidence: 99%

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches

Zu,

Yang,

Wang

et al. 2024

Front. Plant Sci.

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“…The backup algorithm is derived by establishing an empirical relationship between the main algorithm FAPR and the corresponding NDVI [79]. Empirical methods have few parameters, are simple and efficient to compute, but the vegetation index itself is affected by factors such as soil background, observation angle, atmospheric conditions, and saturation effects, resulting in the high uncertainty of the algorithm [80][81][82][83]. As a result, the backup algorithm has lower inversion accuracy and reliability than the main algorithm, which will lead to greater volatility (higher Std) in the Mixed-QC FPAR time series as well as differences in both FPAR values and trend magnitude between the two QC methods.…”

Section: Understanding the Inconsistency In Different Quality Control...mentioning

confidence: 99%

The Impact of Quality Control Methods on Vegetation Monitoring Using MODIS FPAR Time Series

Yan,

Zhang,

Peng

et al. 2024

Forests

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Monitoring vegetation dynamics (VD) is crucial for environmental protection, climate change research, and understanding carbon and water cycles. Remote sensing is an effective method for large-scale and long-term VD monitoring, but it faces challenges due to changing data uncertainties caused by various factors, including observational conditions. Previous studies have demonstrated the significance of implementing proper quality control (QC) of remote sensing data for accurate vegetation monitoring. However, the impact of different QC methods on VD results (magnitude and trend) has not been thoroughly studied. The fraction of absorbed photosynthetically active radiation (FPAR) characterizes the energy absorption capacity of the vegetation canopy and is widely used in VD monitoring. In this study, we investigated the effect of QC methods on vegetation monitoring using a 20-year MODIS FPAR time series. The results showed several important findings. Firstly, we observed that the Mixed-QC (no QC on the algorithm path) generally produced a lower average FPAR during the growing season compared to Main-QC (only using the main algorithm). Additionally, the Mixed-QC FPAR showed a very consistent interannual trend with the Main-QC FPAR over the period 2002–2021 (p < 0.05). Finally, we found that using only the main algorithm for QC generally reduced the trend magnitude (p < 0.1), particularly in forests. These results reveal differences in FPAR values between the two QC methods. However, the interannual FPAR trends demonstrate greater consistency. In conclusion, this study offers a case study on evaluating the influence of different QC methods on VD monitoring. It suggests that while different QC methods may result in different magnitudes of vegetation dynamics, their impact on the time series trends is limited.

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“…in combination with existing calibration equations for fractional green cover and biomass [18], which have been shown to be highly correlated with winter cover crop biophysical traits prior to index saturation [17][18][19]. Index saturation occurs as red reflectance has little variance in moderate to high biomass plants while NIR reflectance increases in higher biomass plants creating small or no increase in NDVI with increased biomass beyond saturation [67,68].…”

Section: Rgb Photography and Destructive Biomass Samplesmentioning

confidence: 99%

Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits

Thieme,

Prabhakara,

Jennewein

et al. 2024

Sensors

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Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

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Evaluating the saturation effect of vegetation indices in forests using 3D radiative transfer simulations and satellite observations

Cited by 41 publications

References 87 publications

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches

The Impact of Quality Control Methods on Vegetation Monitoring Using MODIS FPAR Time Series

Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits

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