scholarly journals Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Xiang Zhang ◽  
Xinming Tang ◽  
Xiaoming Gao ◽  
Hui Zhao
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Root Mean Square Error ◽  
Field Measurements ◽  
Mean Square ◽  
Approximation Model ◽  
Unknown Parameters ◽  
Sar Data ◽  
Agricultural Areas ◽  
Moisture Estimation

The objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. The Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. The solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. This research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. The disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. The optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. The agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm3/cm3 for both methods. The optimized Alpha model shows an obvious improvement for soil moisture retrieval. The results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

An Extension of the Alpha Approximation Method for Soil Moisture Estimation Using Time-Series SAR Data Over Bare Soil Surfaces

2017 ◽  
Vol 14 (8) ◽  
pp. 1328-1332 ◽  
Author(s):  
Lian He ◽  
Qiming Qin ◽  
Rocco Panciera ◽  
Mihai Tanase ◽  
Jeffrey P. Walker ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Approximation Method ◽  
Bare Soil ◽  
Sar Data ◽  
Moisture Estimation

scholarly journals The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Erol Egrioglu ◽  
Cagdas Hakan Aladag ◽  
Cem Kadilar
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Simulation Study ◽  
Long Memory ◽  
Moving Average ◽  
Mean Square ◽  
Sample Sizes ◽  
Advantages And Disadvantages

Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) models are used in the analysis of seasonal long memory-dependent time series. Two methods, which are conditional sum of squares (CSS) and two-staged methods introduced by Hosking (1984), are proposed to estimate the parameters of SARFIMA models. However, no simulation study has been conducted in the literature. Therefore, it is not known how these methods behave under different parameter settings and sample sizes in SARFIMA models. The aim of this study is to show the behavior of these methods by a simulation study. According to results of the simulation, advantages and disadvantages of both methods under different parameter settings and sample sizes are discussed by comparing the root mean square error (RMSE) obtained by the CSS and two-staged methods. As a result of the comparison, it is seen that CSS method produces better results than those obtained from the two-staged method.

Potential of soil moisture estimation using C-band polarimetric SAR data in arid regions

2018 ◽  
Vol 40 (5-6) ◽  
pp. 2138-2150 ◽  
Author(s):  
Liping Yang ◽  
Xiaodong Feng ◽  
Fei Liu ◽  
Jing Liu ◽  
Xiaohui Sun
Keyword(s):  
Soil Moisture ◽  
Arid Regions ◽  
Polarimetric Sar ◽  
Sar Data ◽  
Moisture Estimation

Predicting Daily Confirmed COVID-19 Cases in India

2021 ◽  
pp. 34-51
Author(s):  
Sudip Singh
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Family Welfare ◽  
Mean Square ◽  
Univariate Time Series ◽  
Partial Autocorrelation Function ◽  
Auto Regressive

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

scholarly journals Forecasting hourly emergency department arrival using time series analysis

2020 ◽  
Vol 26 (1) ◽  
pp. 34-43
Author(s):  
Avishek Choudhury ◽  
Estefania Urena
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Mean Square ◽  
Mean Error ◽  
Auto Regressive

Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.

scholarly journals The Potential Use of Multi-Band SAR Data for Soil Moisture Retrieval over Bare Agricultural Areas: Hebei, China

2015 ◽  
Vol 8 (1) ◽  
pp. 7 ◽  
Author(s):  
Xiang Zhang ◽  
Baozhang Chen ◽  
Hongdong Fan ◽  
Jilei Huang ◽  
Hui Zhao
Keyword(s):  
Soil Moisture ◽  
Sar Data ◽  
Agricultural Areas ◽  
Potential Use ◽  
Multi Band

scholarly journals A high-sensitivity multilayer soil moisture monitoring sensor based on a double high-frequency tuning detection circuit

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090782
Author(s):  
Shi Qinglan ◽  
Shi Yujiao ◽  
Liu Xiaochen ◽  
Mei Shuli ◽  
Feng Lei
Keyword(s):  
Soil Moisture ◽  
Internet Of Things ◽  
Root Mean Square Error ◽  
High Frequency ◽  
Detection Method ◽  
Mean Square ◽  
Time Division Multiplexing ◽  
Detection Circuit ◽  
Moisture Detection ◽  
Time Division

The multilayer soil moisture Internet of things sensor is designed to monitor the moisture of multiple soil profiles in real time. Its sensitivity and accuracy are of great concern to improve the performance of sensors. This article introduces the system composition of the end-cloud integrated multilayer soil moisture Internet of things sensor and then focuses on the design of key technologies, such as the moisture detection circuit, the time division multiplexing detection technology, and the deredundancy circuit in the analog–digital integrated design. The performance of the soil moisture detection circuit is directly related to the measurement accuracy of the sensor. A detection method is proposed using a high-frequency double-resonance circuit, which can detect small changes in moisture by changing the circuit detuning voltage. The maximum root mean square error of the calibration is less than 1.35% for five typical soils from different places. Compared with that of an independent detection method, the output consistency of the time division multiplexing detection is significantly improved by using the time division multiplexing detection method, which has a root mean square error of only 0.12%. In order to reduce errors caused by inconsistency in each burial, the gravimetric analysis is used in the sensitivity monitoring test, which shows that small changes in soil moisture can be detected by the circuit.

scholarly journals Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Ayub Mohammadi ◽  
Khalil Valizadeh Kamran ◽  
Sadra Karimzadeh ◽  
Himan Shahabi ◽  
Nadhir Al-Ansari
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Decision Trees ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Susceptibility Mapping ◽  
Mean Square ◽  
Topographic Wetness Index ◽  
Flood Detection ◽  
Flooded Areas

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area.

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