scholarly journals Analyzing moisture-heat coupling in a wheat-soil system using data-driven vector autoregression model

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7101 ◽  
Author(s):  
Xiaohang Feng ◽  
Xia Zhang ◽  
Zhenqi Feng ◽  
Yichang Wei
Keyword(s):  
Soil Moisture ◽  
Response Time ◽  
Soil Temperature ◽  
Vector Autoregression ◽  
Time Lag ◽  
Central China ◽  
Data Driven ◽  
Response Intensity ◽  
The Past ◽  
Using Data

Soil temperature and moisture have a close relationship, the accurate controlling of which is important for crop growth. Mechanistic models built by previous studies need exhaustive parameters and seldom consider time stochasticity and lagging effect. To circumvent these problems, this study designed a data-driven stochastic model analyzing soil moisture-heat coupling. Firstly, three vector autoregression models are built using hourly data on soil moisture and temperature at the depth of 10, 30, and 90 cm. Secondly, from impulse response functions, the time lag and intensity of two variables’ response to one unit of positive shock can be obtained, which describe the time length and strength at which temperature and moisture affect each other, indicating the degree of coupling. Thirdly, Granger causality tests unfold whether one variable’s past value helps predict the other’s future value. Analyzing data obtained from Shangqiu Experiment Station in Central China, we obtained three conclusions. Firstly, moisture’s response time lag is 25, 50, and 120 h, while temperature’s response time lag is 50, 120, and 120 h at 10, 30, and 90 cm. Secondly, temperature’s response intensity is 0.2004, 0.0163, and 0.0035 °C for 1% variation in moisture, and moisture’s response intensity is 0.0638%, 0.0163%, and 0.0050% for 1 °C variation in temperature at 10, 30, and 90 cm. Thirdly, the past value of soil moisture helps predict soil temperature at 10, 30, and 90 cm. Besides, the past value of soil temperature helps predict soil moisture at 10 and 30 cm, but not at 90 cm. We verified this model by using data from a different year and linking it to soil plant atmospheric continuum model.

scholarly journals Root-zone soil moisture estimation using data-driven methods

2014 ◽  
Vol 50 (4) ◽  
pp. 2946-2962 ◽  
Author(s):  
Kurt C. Kornelsen ◽  
Paulin Coulibaly
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Data Driven ◽  
Using Data ◽  
Moisture Estimation

Soil Temperature Influencing Evaporation Estimation Using Data-Driven Tools

2017 ◽  
pp. 307-316
Author(s):  
Narhari D. Chaudhari ◽  
Neha N. Chaudhari ◽  
Ankush R. Pendhari ◽  
Milind M. Thombre
Keyword(s):  
Soil Temperature ◽  
Data Driven ◽  
Using Data

scholarly journals Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths

2020 ◽  
Vol 12 (16) ◽  
pp. 2614
Author(s):  
Christoph Herbert ◽  
Miriam Pablos ◽  
Mercè Vall-llossera ◽  
Adriano Camps ◽  
José Martínez-Fernández
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Response Time ◽  
Temporal Variability ◽  
Characteristic Time ◽  
Time Lag ◽  
Near Surface ◽  
Time Length ◽  
Spatio Temporal

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land use was analyzed by means of high-resolution images of Normalized Difference Vegetation Index (NDVI) from Sentinel-2 to provide information about the level of spatial representativeness of SMOS observations to each in-situ station. Results of the spatio-temporal analysis of the study were then evaluated to understand seasonally and spatially changing patterns in time lag. The time lag evolution describes a variable characteristic time length by considering the relevant processes which link SMOS and in-situ SM observation, which is an important step to accurately infer subsurface SM from satellite time series. At a further stage, the approach needs to be applied to different SM networks to understand the seasonal, climate- and site-specific characteristic behaviour of time lag and to decide, whether general conclusions can be drawn.

scholarly journals Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content

2009 ◽  
Vol 11 (3-4) ◽  
pp. 237-251 ◽  
Author(s):  
Amin Elshorbagy ◽  
Ibrahim El-Baroudy
Keyword(s):  
Soil Moisture ◽  
Polynomial Regression ◽  
Time Lag ◽  
Soil Layer ◽  
Mining Industry ◽  
Data Driven ◽  
Spatial And Temporal Variability ◽  
Atmospheric Conditions ◽  
Complex Behaviour ◽  
Near Surface

Soil moisture has a crucial role in both the global energy and hydrological cycles; it affects different ecosystem processes. Spatial and temporal variability of soil moisture add to its complex behaviour, which undermines the reliability of most current measurement methods. In this paper, two promising evolutionary data-driven techniques, namely (i) Evolutionary Polynomial Regression and (ii) Genetic Programming, are challenged with modelling the soil moisture response to the near surface atmospheric conditions. The utility of the proposed models is demonstrated through the prediction of the soil moisture response of three experimental soil covers, used for the restoration of watersheds that were disturbed by the mining industry. The results showed that the storage effect of the soil moisture response is the major challenging factor; it can be quantified using cumulative inputs better than time-lag inputs, which can be attributed to the effect of the soil layer moisture-holding capacity. This effect increases with the increase in the soil layer thickness. Three different modelling tools are tested to investigate the tool effect in data-driven modelling. Despite the promising results with regard to the prediction accuracy, the study demonstrates the need for adopting multiple data-driven modelling techniques and tools (modelling environments) to obtain reliable predictions.

scholarly journals Using data‐driven methods to explore the predictability of surface soil moisture with FLUXNET site data

2019 ◽  
Vol 33 (23) ◽  
pp. 2978-2996
Author(s):  
Jinjing Pan ◽  
Wei Shangguan ◽  
Lu Li ◽  
Hua Yuan ◽  
Shupeng Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Data Driven ◽  
Surface Soil Moisture ◽  
Using Data
Sign in / Sign up

Export Citation Format

Share Document