scholarly journals The Estimation of Time Series Data for Soil Respiration Based on Soil Temperature and Soil Moisture Content Ratio and Its Spatial Variations in a Small Mountainous Catchment: In the Case of Weathered Granite Region in Southern Kyoto Prefecture.

2005 ◽  
Vol 87 (4) ◽  
pp. 331-339 ◽  
Author(s):  
K. Tamai ◽  
Y. Kominami ◽  
T. Miyama ◽  
Y. Goto
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Soil Respiration ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Time Series Data ◽  
Series Data ◽  
Mountainous Catchment ◽  
Content Ratio ◽  
Estimation Of Time

Research and Prediction of Yellow Soil Moisture Content in Guizhou Province Based on ARIMA Model

2013 ◽  
Vol 690-693 ◽  
pp. 3076-3081
Author(s):  
Min Liu ◽  
Zhi-Min He
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Arima Model ◽  
Model Fitting ◽  
Absolute Error ◽  
Guizhou Province ◽  
Content Change ◽  
Change Trend

To predict the change trend of guizhou yellow soil moisture content, we employed the ARIMA model of time series, compared the measured data with the prediction data, and the results show that ARIMA time series model fitting soil moisture content change trend is good, predicted value is very close to the observed value. The maximal absolute error, 0.6% and the maximal relative error, 4.2%).The results have practical application value for drought research and management to provide reference[1].

scholarly journals Soil Moisture Retrieval From Sentinel-1 Time-Series Data Over Croplands of Northeastern Thailand

2021 ◽  
pp. 1-5
Author(s):  
Dong Fan ◽  
Tianjie Zhao ◽  
Xiaoguang Jiang ◽  
Huazhu Xue ◽  
Sitthisak Moukomla ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Time Series Data ◽  
Series Data ◽  
Northeastern Thailand

scholarly journals GEOSPATIAL ANALYSIS OF NEAR-SURFACE SOIL MOISTURE TIME SERIES DATA OVER INDIAN REGION

2016 ◽  
Vol XLI-B7 ◽  
pp. 631-637 ◽  
Author(s):  
P. Berwal ◽  
C. S. Murthy ◽  
P.V. Raju ◽  
M. V. R. Sesha Sai
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Time Series Data ◽  
Surface Soil ◽  
Geospatial Analysis ◽  
Series Data ◽  
Hazard Mapping ◽  
Surface Soil Moisture ◽  
Soil Moisture Data

The present study has developed the time series database surface soil moisture over India, for June, July and August months for the period of 20 years from 1991 to 2010, using data products generated under Climate Change Initiative Programme of European Space Agency. These three months represent the crop sowing period in the prime cropping season in the country and the soil moisture data during this period is highly useful to detect the drought conditions and assess the drought impact. The time series soil moisture data which is in 0.25 degree spatial resolution was analyzed to generate different indicators. Rainfall data of same spatial resolution for the same period, generated by India Meteorological Department was also procured and analyzed. Geospatial analysis of soil moisture and rainfall derived indicators was carried out to study (1) inter annual variability of soil moisture and rainfall, (2) soil moisture deviations from normal during prominent drought years, (3) soil moisture and rainfall correlations and (4) drought exposure based on soil moisture and rainfall variability. The study has successfully demonstrated the potential of these soil moisture time series data sets for generating regional drought surveillance information products, drought hazard mapping, drought exposure analysis and detection of drought sensitive areas in the crop planting period.

scholarly journals Comparison of In Situ Soil Moisture Measurements: An Examination of the Neutron and Dielectric Measurements within the Illinois Climate Network

2016 ◽  
Vol 33 (8) ◽  
pp. 1749-1758 ◽  
Author(s):  
Evan J. Coopersmith ◽  
Michael H. Cosh ◽  
Jennifer M. Jacobs
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Time Series Data ◽  
Series Data ◽  
Data Series ◽  
Neutron Probe ◽  
Dielectric Sensor ◽  
Continuous Record ◽  
Data Record

AbstractThe continuity of soil moisture time series data is crucial for climatic research. Yet, a common problem for continuous data series is the changing of sensors, not only as replacements are necessary, but as technologies evolve. The Illinois Climate Network has one of the longest data records of soil moisture; yet, it has a discontinuity when the primary sensor (neutron probes) was replaced with a dielectric sensor. Applying a simple model coupled with machine learning, the two time series can be merged into one continuous record by training the model on the latter dielectric model and minimizing errors against the former neutron probe dataset. The model is able to be calibrated to an accuracy of 0.050 m3 m−3 and applying this to the earlier series and applying a gain and offset, an RMSE of 0.055 m3 m−3 is possible. As a result of this work, there is now a singular network data record extending back to the 1980s for the state of Illinois.

scholarly journals GEOSPATIAL ANALYSIS OF NEAR-SURFACE SOIL MOISTURE TIME SERIES DATA OVER INDIAN REGION

2016 ◽  
Vol XLI-B7 ◽  
pp. 631-637
Author(s):  
P. Berwal ◽  
C. S. Murthy ◽  
P.V. Raju ◽  
M. V. R. Sesha Sai
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Time Series Data ◽  
Surface Soil ◽  
Geospatial Analysis ◽  
Series Data ◽  
Hazard Mapping ◽  
Surface Soil Moisture ◽  
Soil Moisture Data

The present study has developed the time series database surface soil moisture over India, for June, July and August months for the period of 20 years from 1991 to 2010, using data products generated under Climate Change Initiative Programme of European Space Agency. These three months represent the crop sowing period in the prime cropping season in the country and the soil moisture data during this period is highly useful to detect the drought conditions and assess the drought impact. The time series soil moisture data which is in 0.25 degree spatial resolution was analyzed to generate different indicators. Rainfall data of same spatial resolution for the same period, generated by India Meteorological Department was also procured and analyzed. Geospatial analysis of soil moisture and rainfall derived indicators was carried out to study (1) inter annual variability of soil moisture and rainfall, (2) soil moisture deviations from normal during prominent drought years, (3) soil moisture and rainfall correlations and (4) drought exposure based on soil moisture and rainfall variability. The study has successfully demonstrated the potential of these soil moisture time series data sets for generating regional drought surveillance information products, drought hazard mapping, drought exposure analysis and detection of drought sensitive areas in the crop planting period.

Characteristics on Soil Respiration of Eucalyptus Plantation with Four Years Old in Beihai of Guangxi, Southern China

2014 ◽  
Vol 618 ◽  
pp. 380-387
Author(s):  
Jiang Ming Ma ◽  
Meng Wu ◽  
Ting Ting Zhan ◽  
Feng Tian ◽  
Shi Chu Liang
Keyword(s):  
Soil Moisture ◽  
Soil Respiration ◽  
Moisture Content ◽  
Soil Temperature ◽  
Respiration Rate ◽  
Soil Moisture Content ◽  
Southern China ◽  
Heterotrophic Respiration ◽  
Autotrophic Respiration ◽  
Soil Respiration Rate

This experiment was conducted on the 4 years old Eucalyptus plantation in Beihai of Guangxi, southern China. From January to December 2013, in the spring, summer, autumn and winter, seasonal variation and diurnal variation of the soil respiration and its environmental factors had been observed, respectively. The results showed that: (1) Soil respirations has obvious seasonal characteristics, the soil respiration rate in each seasons showed that: summer> spring > autumn > winter. The heterotrophic respiration rate was higher than the autotrophic respiration rate. The contribution of autotrophic respiration rate in winter was higher than that in other three seasons. (2) Soil respiration has obvious diurnal characteristic, it could be expressed as a single-peak curve. But the maximum value of soil respiration appeared in different times in different seasons. (3) There existed positive correlation index exponential relationships between the soil temperature and the soil respiration rate and its components. Soil temperature changes could explain soil respiration, autotrophic respiration and heterotrophic respiration by 90.2%, 27.5% and 92.8%. Temperature sensitivity showed following order: the heterotrophic respiration rate> the soil respiration rate> the autotrophic respiration rate, in terms of affected by temperature, the heterotrophic respiration was higher than the autotrophic respiration. (4) There were notable positive correlations between soil moisture content and soil respiration rate. Obviously, soil moisture content could promote soil respiration in a certain range.

Sign in / Sign up

Export Citation Format

Share Document