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Spatial Scale Analysis of Soil Water Content in Agricultural Soils of the Nitra River Catchment (Slovakia)

Tárník Andrej, Igaz Dušan

Journal of Ecological Engineering

2020

Vol. 21, nr 1

112--119

Determining the soil water content (SWC) in a soil profile is very important task for agriculture and also for a wider ecological context. The spatial and temporal variability of SWC is a elementary issue for agricultural practice, irrigation management, or landscape management globally. Various methods are used for obtaining the SWC data. Every method has some advantages and also disadvantages. Many of them are focused only on one dimension but modern precise agriculture needs the information about SWC in spatial scale. This study is focused on the spatial scale analysis of SWC in the Nitra river catchment for years 2013 and 2014. The HYDRUS 1D hydrological model and GIS tools were used for the creation maps of SWC. Combination of the measured and simulated data was used for the creation of the unique spatial maps of soil moisture in 0–30 and 30–60 cm soil horizons. Validation of our method shows trustworthy results. Soil water storage and fulfillment of maximum soil water storage were analysed with using the created maps.

Estimating root zone moisture from surface soil using limited data

Zeng W. Z., Lei G. Q., Zhang H. Y., Hong M. H., Xu C., Wu J. W., Huang J. S.

Ecological Chemistry and Engineering. S

2017

Vol. 24, nr 4

501--516

For estimation of root-zone moisture content from EO-1/Hyperion imagery, surface soil moisture was first predicted by hyperspectral reflectance data using partial least square regression (PLSR) analysis. The textures of more than 300 soil samples extracted from a 900 m × 900 m field site located within the Hetao Irrigation District in China were used to parameterize the HYDRUS-1D numerical model. The study area was spatially discretized into 18,000 compartments (30 m × 30 m × 0.02 m), and Monte Carlo simulations were applied to generate 2000 different soil-particle size distributions for each compartment. Soil hydraulic properties for each realization were determined by application of artificial neural network analysis and used to parameterize HYDRUS-1D to simulate averaged soil-moisture contents within the root zone (0-40 cm) and surface (approximately 0-4 cm). Then the link between surface moisture and root zone was established by use of linear regression analysis, resulting in R and RMSE of 0.38 and 0.03, respectively. Kriging and co-kriging with observed surface moisture, and co-kriging with surface moisture obtained from Hyperion imagery were also used to estimate root-zone moisture. Results indicated that PLSR is a powerful tool for soil moisture estimation from hyperspectral data. Furthermore, co-kriging with observed surface moisture had the highest R (0.41) and linear regression model, and HYDRUS Monte Carlo simulations had a lowest RMSE (0.03) among the four methods. In regions that have similar climatic and soil conditions to our study area, a linear regression model with HYDRUS Monte Carlo simulations is a practical method for root-zone moisture estimation before sowing and it can be easily coupled with remote sensing technology.