A facility of BaPS (Barometric Process Separation) and indoor incubation experiments were used to determine the effect of soil salinity on soil respiration and nitrogen transformation. The rates of soil respiration, gross nitrification, denitrification, ammonium and nitrate nitrogen concentrations and relevant soil parameters were measured. Results showed that soil respiration and nitrification and denitrification rates were all affected by soil salinity. Furthermore, the effect of soil salinity level on nitrification and denitrification rates had a threshold value (EC1:5 = 1.13 dS/m). When soil salinity level was smaller to this threshold value, the rates of nitrification and denitrification increased with soil salinity while they were reduced when soil salinity level was larger than the threshold value. Moreover, the changing law of soil respiration rate with soil salinity was similar with the nitrification and denitrification rates while the variation tendency was opposite. In addition, the transformation form urea to ammonium and nitrate nitrogen was also reduced with the increase of soil salinity and the reduced effect could be expressed by exponential functions.
Intermittent irrigation has attracted much attention as a water-saving technology in arid and semi-arid regions. For understanding the effect of intermittent irrigation on water and solute storage varied from irrigation amount per time (IRA), irrigation application frequency (IRAF), irrigation intervals (IRI) and even soil texture (ST), intermittent irrigation experiment was carried out in 33 micro-plots in Inner Mongolia, China. The experiment results were used for the calibration and validation of HYDRUS-1D software. Then 3 ST (silty clay loam, silty loam, and silty clay), 5 IRA (2, 4, 6, 8, and 10 cm), 4 IRAF (2, 3, 4, and 5 times) and 4 IRI (1, 2, 3, and 4 days) were combined and total 240 scenarios were simulated by HYDRUS-1D. Analysis of variance (ANVOA) of simulated results indicated that ST, IRA, and IRAF had significant effect on salt and nitrate nitrogen (NO3−-N) storage of 0-40 cm depth soil in intermittent irrigation while only ST affected soil water storage obviously. Furthermore, salt leaching percentage (SLP) and water use efficiency (WUE) of 0-40 cm depth were calculated and statistical prediction models for SLP were established based on the ANOVA using multiple regression analysis in each soil texture. Then constraint conditions of soil water storage (around field capacity), salt storage (smaller than 168 mg·cm−2), WUE (as large as possible) in 0-40 cm depth and total irrigation water amount (less than 25 cm) were proposed to find out the optimal intermittent irrigation strategies. Before sowing, the optimal irrigation strategy for silty clay loam soil was 6 cm IRA, 3 times IRAF, and 2 days IRI respectively. For silty loam and silty clay soils, IRA, IRAF, and IRI were 8 cm, 3 times, and 2 days respectively.
For improving the understanding of interactions between hyperspectral reflectance and soil salinity, in situ hyperspectral inversion of soil salt content at a depth of 0-10 cm was conducted in Hetao Irrigation District, Inner Mongolia, China. Six filtering methods were used to preprocess soil reflectance data, and waveband selection combined by VIP (variable importance in projection) and b-coefficients (regression coefficients of model) was also applied to simplify model. Then statistical methods of partial least square regression (PLS) and orthogonal projection to latent structures (OPLS) were processed to establish the inversion models. Our findings indicate that the selected sensitive wavebands for the 6 filtering methods are different, among which the multiplicative signal correction (MSC) and standard normal variate methods (SNV) have some similar sensitive wavebands with unfiltered data. Derivatives (DF1 and DF2) could characterize sensitive wavebands along the scale of VNIR (350-1100 nm), especially the second derivative (DF2). The sensitive wavebands for continuum-removed reflectance method (CR) have protruded many narrow absorption features. For orthogonal signal correction method (OSC), the selected wavebands are centralized in the range of 565-1013 nm. The calibration and evaluation processes have demonstrated the second order derivate filtering method (DF2) combined with waveband selection is superior to other processes, for it has high R2 (larger than 0.7) both in PLS and OPLS models for calibration and evaluation, by choosing only 156 wavebands from the whole 700 wavebands. Meanwhile, OPLS method was considered to be more suitable for the analyzing than PLS in most of our situations.
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.