A Generalised Family of Estimator for Estimating Unknown Variance Using Two Auxiliary Variables

Viplav Kumar Singh; Rajesh Singh

doi:10.15415/mjis.2015.32017

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

Remote Sensing ◽

10.3390/rs11141683 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1683 ◽

Cited By ~ 6

Author(s):

Yangchengsi Zhang ◽

Long Guo ◽

Yiyun Chen ◽

Tiezhu Shi ◽

Mei Luo ◽

...

Keyword(s):

Time Series ◽

Organic Carbon ◽

Time Series Data ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Series Data ◽

Landsat 8 ◽

Auxiliary Variables ◽

Jianghan Plain ◽

Ndvi Time Series

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

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Deep learning for denoising

Geophysics ◽

10.1190/geo2018-0668.1 ◽

2019 ◽

Vol 84 (6) ◽

pp. V333-V350 ◽

Cited By ~ 15

Author(s):

Siwei Yu ◽

Jianwei Ma ◽

Wenlong Wang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Graphics Processing Unit ◽

Random Noise ◽

Stochastic Gradient Descent ◽

Processing Unit ◽

Noise Attenuation ◽

Optimal Parameters ◽

Training Set ◽

Unknown Variance

Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set in which the inputs are the raw data sets and the corresponding outputs are the desired clean data. After the completion of training, the deep-learning (DL) method achieves adaptive denoising with no requirements of (1) accurate modelings of the signal and noise or (2) optimal parameters tuning. We call this intelligent denoising. We have used a convolutional neural network (CNN) as the basic tool for DL. In random and linear noise attenuation, the training set is generated with artificially added noise. In the multiple attenuation step, the training set is generated with the acoustic wave equation. The stochastic gradient descent is used to solve the optimal parameters for the CNN. The runtime of DL on a graphics processing unit for denoising has the same order as the [Formula: see text]-[Formula: see text] deconvolution method. Synthetic and field results indicate the potential applications of DL in automatic attenuation of random noise (with unknown variance), linear noise, and multiples.

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HIGHER-ORDER CURVATURE TERMS IN BORN–INFELD TYPE BRANE THEORIES

International Journal of Modern Physics D ◽

10.1142/s0218271811018615 ◽

2011 ◽

Vol 20 (01) ◽

pp. 59-75 ◽

Cited By ~ 4

Author(s):

EFRAIN ROJAS

Keyword(s):

Ricci Tensor ◽

General Structure ◽

Extrinsic Curvature ◽

Higher Order ◽

Square Root ◽

Auxiliary Variables ◽

Field Equations ◽

Alternative Description ◽

Brane Models ◽

Combined Matrix

The field equations associated to Born–Infeld type brane theories are studied by using auxiliary variables. This approach hinges on the fact, that the expressions defining the physical and geometrical quantities describing the worldvolume are varied independently. The general structure of the Born–Infeld type theories for branes contains the square root of a determinant of a combined matrix between the induced metric on the worldvolume swept out by the brane and a symmetric/antisymmetric tensor depending on gauge, matter or extrinsic curvature terms taking place on the worldvolume. The higher-order curvature terms appearing in the determinant form come to play in competition with other effective brane models. Additionally, we suggest a Born–Infeld–Einstein type action for branes where the higher-order curvature content is provided by the worldvolume Ricci tensor. This action provides an alternative description of the dynamics of braneworld scenarios.

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