A Global Sensitivity Analysis of Soil Parameters Associated With Backscattering Using the Advanced Integral Equation Model

Ma, Can; Li, Xiaoqiong; Wang, Shuguo

doi:10.1109/tgrs.2015.2426194

Cited by 45 publications

(12 citation statements)

References 50 publications

(67 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EFAST is a well-recognized global SA algorithm that is especially widely applied in the sensitivity analysis for nonlinear models [11], such as the Biome-BGC model [50], crop growth model [12], canopy reflectance model [51], microwave remote sensing models [18]. The algorithm utilizes the main sensitivity index (MSI) and total sensitivity index (TSI) to quantify the sensitivity of the model outputs on various model inputs.…”

Section: Extended Fourier Amplitude Sensitivity Test (Efast)mentioning

confidence: 99%

“…By comparing the two cases, it can be seen that the ranges of the parameter significantly influenced their sensitivities and importance order [18]. The range selected in the first experiment was narrower and limiting some important but small parameters, such as α q , so the range was given in the second experiment was suggested for FBEM.…”

Section: Parameters' Si Variation With Parameter Rangementioning

confidence: 99%

“…Under such background, various sensitivity analyses (SAs) were proposed and applied to ascertain the corresponding responses in the output variables when input parameters alter within their valid ranges [11,12]. Indeed, various SA algorithms have been widely expanded to ecological models [13,14], SCOPE model [15], hydrological models [16,17], remote sensing models [18] and crop growth models [12]. Thus, performing an SA is a feasible way to characterize and reduce uncertainties in ecosystem models, and to improve their performance [19,20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation

et al. 2020

Sustainability

View full text Add to dashboard Cite

An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters’ sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO2 to air CO2 ( f C i ) , canopy quantum efficiency of photon conversion ( α q ) , maximum carboxylation rate at 25 ° C ( V m 25 ) were the most sensitive parameters for the GPP. It was also indicated that α q , E V m and Q 10 were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE = 1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as f c i , α q , E V m , V m 25 strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters’ SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.

show abstract

Section: Extended Fourier Amplitude Sensitivity Test (Efast)mentioning

confidence: 99%

Section: Parameters' Si Variation With Parameter Rangementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation

et al. 2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…For example, Vijay estimated soil moisture through the improved WCM [23], and Kumar used Sentinel-1 data to estimate winter wheat crop growth parameters [15]. These studies showed that C-band synthetic aperture radar data have unique advantages in soil moisture estimation, but the backscatter signal is susceptible to the influence of surface vegetation and surface roughness [24]. The interaction between vegetation and radar scattering signals is more complicated, which increases the uncertainty of soil moisture retrieval [25,26].…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau

Yang

Zhao

et al. 2021

Sustainability

View full text Add to dashboard Cite

Soil moisture plays an important role in the land surface model. In this paper, a method of using VV polarization Sentinel-1 SAR and Landsat optical data to retrieve soil moisture data was proposed by combining the water cloud model (WCM) and the deep belief network (DBN). Since the simple combination of training data in the neural network cannot effectively improve the accuracy of the soil moisture inversion results, a WCM physical model was used to eliminate the effect of vegetation cover on the ground backscatter, in order to obtain the bare soil backscatter coefficient. This improved the correlation of ground soil backscatter characteristics with soil moisture. A DBN soil moisture inversion model based on the bare soil backscatter coefficients as the foundation training data combined with radar incidence angle and terrain factors obtained good inversion results. Studies in the Naqu area of the Tibetan Plateau showed that vegetation cover had a significant effect on the soil moisture, and the goodness of fit (R2) between the backscatter coefficient and soil moisture before and after the elimination of vegetation cover was 0.38 and 0.50, respectively. The correlation between the backscatter coefficient and the soil moisture was improved after eliminating the vegetation cover. The inversion results of the DBN soil moisture model were further improved through iterative parameters. The model prediction reached its highest level of accuracy when the restricted Boltzmann machine (RBM) was set to seven layers, the bias and R were 0.007 and 0.88, respectively. Ten-fold cross-validation showed that the DBN soil moisture model performed stably with different data. The prediction was further improved when the bare soil backscatter coefficient was used as the training data. The mean values of the root mean square error (RMSE), the inequality coefficient (TIC), and the mean absolute percent error (MAPE) were 0.023, 0.09, and 11.13, respectively.

show abstract

“…The SAR backscattering coefficient is sensitive to soil dielectric constant, which is directly related to soil moisture content, laying a solid physical foundation for microwave remote sensing retrieval of soil moisture [13]. However, in addition to being controlled by soil dielectric properties, the backscattered signal is strongly influenced by vegetation and surface roughness [14]. The quantification of the respective contributions from vegetation and surface roughness affects the accuracy of soil moisture retrieval.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau

et al. 2021

View full text Add to dashboard Cite

This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and α of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R2 and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m3/m3, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau.

show abstract

A Global Sensitivity Analysis of Soil Parameters Associated With Backscattering Using the Advanced Integral Equation Model

Cited by 45 publications

References 50 publications

Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation

Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation

Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau

Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau

Contact Info

Product

Resources

About