Soil moisture estimation using an artificial neural network: a feasibility study

2004 ◽  
Vol 30 (5) ◽  
pp. 827-839 ◽  
Author(s):  
Hongli Jiang ◽  
William R Cotton
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Feasibility Study ◽  
Artificial Neural ◽  
Moisture Estimation

Soil Moisture Inversion Using AMSR-E Remote Sensing Data: An Artificial Neural Network Approach

2014 ◽  
Vol 501-504 ◽  
pp. 2073-2076 ◽  
Author(s):  
Xing Mei Xie ◽  
Jing Wen Xu ◽  
Jun Fang Zhao ◽  
Shuang Liu ◽  
Peng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Learning Algorithm ◽  
Estimation Accuracy ◽  
Neural Network Approach ◽  
Near Surface ◽  
Artificial Neural ◽  
Moisture Estimation ◽  
Bpnn Model

In this work artificial neural network with a back-propagation learning algorithm (BPNN) is employed to solve soil moisture retrieval for Sichuan Middle Hilly Area in China. Eighteen kinds of BPNN models have been developed using AMSR-E observations to retrieve soil moisture. The results show that the 18.7GHz band has some positive effect on improving soil moisture estimation accuracy while the 36.5GHz may interfere with deriving soil moisture, and vertical brightness temperature has a closer relationship with observed near-surface soil moisture than horizontal TB. The BPNN model driven by vertical and horizontal TB dataset at 6.9GHz and 10.7GHz frequency has the best performance of all the BPNN models withr value of 0.4968 and RMSE 10.2976%. Generally, the BPNN model is more suitable for soil moisture estimation than NASA product for the study area and can provide significant soil moisture information due to its ability of capturing non-linear and complex relationship.

scholarly journals Soil Moisture estimation by ANN using Bistatic Scatterometer data

2014 ◽  
Vol II-8 ◽  
pp. 97-100 ◽  
Author(s):  
D. K. Gupta ◽  
P. Kumar ◽  
V. N. Mishra ◽  
R. Prasad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Incidence Angle ◽  
Scattering Coefficient ◽  
Data Sets ◽  
Computing Technique ◽  
Levenberg Marquardt ◽  
Artificial Neural ◽  
Moisture Estimation

The microwave response of bare soil surfaces is influenced by a variety of parameters such as surface roughness, vegetation density, soil texture and soil moisture. It makes the soil moisture estimation process more complex. In such condition, the estimation of the soil moisture using microwave data with fast and less complex computing technique is a significant area of research today. The artificial neural network (ANN) approach has been found more potential in retrieving soil moisture from microwave sensors as compared to traditional techniques. For this purpose, a back propagation artificial neural network (BPANN) based on Levenberg Marquardt (TRAINLM) algorithm was developed. The measurement of scattering coefficient was carried out over a range of incidence angle from 20° to 70° at 5° steps for both the HH- and VV- polarizations. The BPANN was trained and tested with the experimentally obtained data by using bistatic X-band scatterometer for different values of soil moistures (12%, 16%, 21% and 25%) at 30° incidence angle. The scattering coefficient and soil moisture data were interpolated into 20 data sets and these data sets were divided into training data sets (70%) and testing data sets (30%). The performance of the trained BPANN was evaluated by comparing the observed soil moisture and estimated soil moisture by developed BPANN using a linear regression analysis (least square fitting) and performance factor Adj_R<sup>2</sup>. The values of Adj_R<sup>2</sup> were found 0.93 and 0.94 for HH- and VV- polarization at 30° incidence angle respectively. The estimation of soil moisture by BPANN with Levenberg Marquardt training algorithm was found better at both HH- and VV- polarizations.

Oxygen extraction fraction mapping at 3 Tesla using an artificial neural network: A feasibility study

2017 ◽  
Vol 79 (2) ◽  
pp. 890-899 ◽  
Author(s):  
Sebastian Domsch ◽  
Bettina Mürle ◽  
Sebastian Weingärtner ◽  
Jascha Zapp ◽  
Frederik Wenz ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feasibility Study ◽  
3 Tesla ◽  
Oxygen Extraction Fraction ◽  
Oxygen Extraction ◽  
Extraction Fraction ◽  
Artificial Neural

scholarly journals Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qingyan Meng ◽  
Linlin Zhang ◽  
Qiuxia Xie ◽  
Shun Yao ◽  
Xu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Landsat 8 ◽  
Combined Use ◽  
Artificial Neural ◽  
Agricultural Areas ◽  
Aperture Radar

Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

scholarly journals A model for soil moisture dynamics estimation based on artificial neural network

2019 ◽  
Vol 81 ◽  
pp. 01017
Author(s):  
Wei Wang ◽  
Shuya Wang ◽  
Jianxia Chang ◽  
Dan Bai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Water Diversion ◽  
Soil Moisture Dynamics ◽  
Growth Environment ◽  
Proposed Model ◽  
Solid Foundation ◽  
Artificial Neural ◽  
Moisture Dynamics

Research on soil moisture estimation models can effectively improve the growth environment of crops. In this paper, the author studied the artificial neural network and variation pattern of soil moisture. Then, application of the model for water diversion estimation was explored based on artificial neural network. On this basis, an optimization algorithm was presented to simulate water diversion. Furthermore, a model for remote sensing of soil moisture dynamics was applied to artificial neural network. It has been proven that the research can optimize the application of the proposed model, laying a solid foundation for future study.

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