Abstract
In paddy field, monitoring soil moisture is required for irrigation scheduling and water resource allocation, management and planning. The current study proposes an Artificial Neural Networks (ANN) model to estimate soil moisture in paddy field with limited meteorological data. Dynamic of ANN model was adopted to estimate soil moisture with the inputs of reference evapotranspiration (ETo) and precipitation. ETo was firstly estimated using the maximum, average and minimum values of air temperature as the inputs of model. The models were performed under different weather conditions between the two paddy cultivation periods. Training process of model was carried out using the observation data in the first period, while validation process was conducted based on the observation data in the second period. Dynamic of ANN model estimated soil moisture with R2 values of 0.80 and 0.73 for training and validation processes, respectively, indicated that tight linear correlations between observed and estimated values of soil moisture were observed. Thus, the ANN model reliably estimates soil moisture with limited meteorological data.
Highlights
Soil moisture represents water availability for the plants and it is required for irrigation scheduling and water resource allocation, management and planning
In the first paddy cultivation period, the meteorological parameters were characterized by low air temperature and high precipitation compared to the second period
The monthly average air temperature changed during in the end of 2010 and 2011, where its value was highest on November 2010 for the first period, and it occurred on December 2011 for the second period
Summary
Soil moisture represents water availability for the plants and it is required for irrigation scheduling and water resource allocation, management and planning. Soil moisture variation affects the pattern of evapotranspiration, runoff and deep percolation in paddy field [1, 2, 3]. Soil moisture level is predominately influenced by water input through precipitation and irrigation. Hydrological data such as crop evapotranspiration, deep percolation, runoff and irrigation are often limited because acquisition of measurements in the field is costly, complicated, and time consuming. Estimation of soil moisture is needed using limited meteorological data
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