Evapotranspiration partitioning assessment using a machine-learning-based leaf area index and the two-source energy balance model with sUAV information

2021 ◽  
Author(s):  
Rui Gao ◽  
Alfonso F. Torres-Rua ◽  
Ayman Nassar ◽  
Joseph Alfieri ◽  
Mahyar Aboutalebi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Balance ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Energy Balance Model ◽  
Balance Model ◽  
Area Index ◽  
Evapotranspiration Partitioning

Estimation of winter leaf area index and sky view fraction for snow modelling in boreal coniferous forests: consequences on snow mass and energy balance

2012 ◽  
pp. n/a-n/a ◽  
Author(s):  
Sirpa Rasmus ◽  
David Gustafsson ◽  
Harri Koivusalo ◽  
Ari Laurén ◽  
Achim Grelle ◽  
...  
Keyword(s):  
Energy Balance ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Coniferous Forests ◽  
Area Index ◽  
Snow Mass

scholarly journals MULTI-SENSOR APPROACH TO LEAF AREA INDEX ESTIMATION USING STATISTICAL MACHINE LEARNING MODELS: A CASE ON MANGROVE FORESTS

2021 ◽  
Vol V-3-2021 ◽  
pp. 109-115
Author(s):  
K. P. Martinez ◽  
D. F. M. Burgos ◽  
A. C. Blanco ◽  
S. G. Salmo III
Keyword(s):  
Machine Learning ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Machine Learning Algorithms ◽  
Optical Parameters ◽  
Mangrove Forests ◽  
Sar Images ◽  
Area Index ◽  
Optical Images ◽  
Sentinel 2

Abstract. Leaf Area Index (LAI) is a quantity that characterizes canopy foliage content. As leaf surfaces are the primary sites of energy, mass exchange, and fundamental production of terrestrial ecosystem, many important processes are directly proportional to LAI. With this, LAI can be considered as an important parameter of plant growth. Multispectral optical images have been widely utilized for mangrove-related studies, such as LAI estimation. In Sentinel-2, for example, LAI can be estimated using a biophysical processor in SNAP or using various machine learning algorithms. However, multispectral optical images have disadvantages due to its weather-dependence and limited canopy penetration. In this study, a multi-sensor approach was implemented by using free multi-spectral optical images (Sentinel-2 ) and synthetic aperture radar (SAR) images (Sentinel-1) to perform Leaf Area Index (LAI) estimation. The use of SAR images can compensate for the above-mentioned disadvantages and it then can pave the way for regular mapping and assessment of LAI, despite any weather conditions and cloud cover. In this study, generation of LAI models that explores linear, non-linear and decision trees modelling algorithms to incorporate Sentinel-1 derivatives and Sentinel-2 LAI were executed. The Random Forest model have exhibited the most robust model having the lowest RMSE of 0.2845. This result poses a concrete relationship of a biophysical entity derived from optical parameters to RADAR derivatives to which opens the opportunity of integrating both systems to compensate each disadvantages and produce a more efficient quantification of LAI.

scholarly journals Leaf Area Index Estimation Model for UAV Image Hyperspectral Data Based on Wavelength Variable Selection and Machine Learning Methods

2020 ◽  
Author(s):  
Juanjuan Zhang ◽  
Tao Cheng ◽  
Wei Guo ◽  
Xin Xu ◽  
Xinming Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Hyperspectral Data ◽  
Learning Methods ◽  
Successive Projections Algorithm ◽  
Area Index ◽  
Machine Learning Methods ◽  
Successive Projections

Abstract Background In order to accurately estimate leaf area index (LAI) of winter wheat by using unmanned aerial vehicle (UAV) hyperspectral imagery.Methods The UAV hyperspectral imaging data, alternating slice-wise diagonalization (ASD) spectral data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments.The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models.Results Our results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information.The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model.Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. Our results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

scholarly journals Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods

2020 ◽  
Vol 12 (13) ◽  
pp. 2110
Author(s):  
Zhulin Chen ◽  
Kun Jia ◽  
Chenchao Xiao ◽  
Dandan Wei ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Energy Utilization ◽  
Estimation Accuracy ◽  
Soil Conditions ◽  
Area Index ◽  
Estimation Models

Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on evaluating GF-5 data for LAI estimation. Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies, but these abound data also easily result in a dimensionality curse. Therefore, feature selection (FS) is necessary to reduce data redundancy to achieve more reliable estimations. Currently, machine learning (ML) algorithms have been widely used for FS. Moreover, the same ML algorithm is usually conducted for both FS and regression in LAI estimation. However, no evidence suggests that this is the optimal solution. Therefore, this study focuses on evaluating the capacity of GF-5 spectral reflectance for estimating LAI and the performances of different combination of FS and ML algorithms. Firstly, the PROSAIL model, which coupled leaf optical properties model PROSPECT and the scattering by arbitrarily inclined leaves (SAIL) model, was used to generate simulated GF-5 reflectance data under different vegetation and soil conditions, and then three FS methods, including random forest (RF), K-means clustering (K-means) and mean impact value (MIV), and three ML algorithms, including random forest regression (RFR), back propagation neural network (BPNN) and K-nearest neighbor (KNN) were used to develop nine LAI estimation models. The FS process was conducted twice using different strategies: Firstly, three FS methods were conducted to search the lowest dimension number, which maintained the estimation accuracy of all bands. Then, the sequential backward selection (SBS) method was used to eliminate the bands having minimal impact on LAI estimation accuracy. Finally, three best estimation models were selected and evaluated using reference LAI. The results showed that although the RF_RFR model (RF used for feature selection and RFR used for regression) achieved reliable LAI estimates (coefficient of determination (R2) = 0.828, root mean square error (RMSE) = 0.839), the poor performance (R2 = 0.763, RMSE = 0.987) of the MIV_BPNN model (MIV used for feature selection and BPNN used for regression) suggested using feature selection and regression conducted by the same ML algorithm could not always ensure an optimal estimation. Moreover, RF selection preserved the most informative bands for LAI estimation so that each ML regression method could achieve satisfactory estimation results. Finally, the results indicated that the RF_KNN model (RF used as feature selection and KNN used for regression) with seven GF-5 spectral band reflectance achieved the better estimation results than others when validated by simulated data (R2 = 0.834, RMSE = 0.824) and actual reference LAI (R2 = 0.659, RMSE = 0.697).

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