Jiesheng Huang

Soil salt leaching under different irrigation regimes: HYDRUS-1D modelling and analysis

Field irrigation experiments were conducted in the Hetao Irrigation District of Inner Mongolia, China, to study the effects of irrigation regimes on salt leaching in the soil profile. The data were used to calibrate and validate the HYDRUS-1D model. The results demonstrated that the model can accurately simulate the water and salt dynamics in the soil profile. The HYDRUS-1D model was then used to simulate 15 distinct irrigation scenarios. The results of the simulation indicated that irrigation amount did not have a significant effect on soil water storage but that increases in irrigation amount could accelerate salt leaching. However, when the irrigation amount was larger than 20 cm, the acceleration was not obvious. Compared with irrigating only once, intermittent irrigation had a better effect on increasing soil water storage and salt leaching, but excessive irrigation times and intervals did not improve salt leaching. In addition, we found that the irrigation regime of 20 cm, irrigated twice at 1-d intervals, might significantly increase salt leaching in the plough layer and decrease the risks of deep seepage and groundwater contamination.

Emergence Rate, Yield, and Nitrogen-Use Efficiency of Sunflowers (Helianthus annuus) Vary with Soil Salinity and Amount of Nitrogen Applied

For understanding the effects of soil salinity and nitrogen (N) fertilizer on the emergence rate, yield, and nitrogen-use efficiency (NUE) of sunflowers, complete block design studies were conducted in Hetao Irrigation District, China. Four levels of soil salinity (electrical conductivity [ECe] = 2.44–29.23 dS m−1) and three levels of N fertilization (90–180 kg ha−1) were applied to thirty-six microplots. Soil salinity significantly affected sunflower growth (P < 0.05). High salinity (ECe = 9.03–18.06 dS m−1) reduced emergence rate by 24.5 percent, seed yield by 31.0 percent, hundred-kernel weight by 15.2 percent, and biological yield by 27.4 percent, but it increased the harvest index by 0.9 percent relative to low salinity (ECe = 2.44–4.44 dS m−1). Application of N fertilizer alleviated some of the adverse effects of salinity, especially in highly saline soils. We suggest that moderate (135 kg ha−1) and high (180 kg ha−1) levels of N fertilization could provide the maximum benefit in low- to moderate-salinity and high- or severe-salinity fields, respectively, in Hetao Irrigation District and similar sunflower-growing areas.

Effect of Salinity on Soil Respiration and Nitrogen Dynamics

Abstract 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. Abstrakt Proces BaPS (Ciśnieniowy Proces Separacji) oraz inkubacja pokojowa zostały wykorzystane do określenia wpływu zasolenia gleby na jej oddychanie i transformację azotu. Mierzono szybkości: respiracji gleby, całkowitej nitryfikacji i denitryfikacji, a także stężenie azotu amonowego i azotanowego oraz wartości odpowiednich parametrów gleby. Wyniki wykazały, że respiracja glebowa oraz szybkości nitryfikacji i denitryfikacji były uzależnione od zasolenia gleby. Ponadto stwierdzono, że wpływ poziomu zasolenia gleby na szybkość nitryfikacji i denitryfikacji miał wartość progową (EC1:5 = 1,13 dS/m). Gdy poziom zasolenia gleby był mniejszy od tej wartości progowej, szybkości nitryfikacji i denitryfikacji rosły wraz ze wzrostem zasolenia gleby. Jeżeli zasolenie gleby był większe od progowego, to szybkości te malały. Co więcej, zmiany charakteru zależności szybkości respiracji gleby od jej zasolenia były porównywalne z szybkością nitryfikacji i denitryfikacji, podczas gdy tendencja zmian była odwrotna. Ponadto, transformacja mocznika do amoniaku i azotu azotanowego również zmniejszała się przy wzroście zasolenia gleby, a efekt takiego zmniejszania może być wyrażony funkcją wykładniczą.

Prediction of Soil Moisture Content and Soil Salt Concentration from Hyperspectral Laboratory and Field Data

This research examines the simultaneous retrieval of surface soil moisture and salt concentrations using hyperspectral reflectance data in an arid environment. We conducted laboratory and outdoor field experiments in which we examined three key soil variables: soil moisture, salt and texture (silty loam, clay and silty clay). The soil moisture content models for multiple textures (M_SMC models) were based on selected hyperspectral reflectance data located around 1460, 1900 and 2010 nm and resulted in R2 values higher than 0.933. Meanwhile, the soil salt concentrations were also accurately (R2 > 0.748) modeled (M_SSC models) based on wavebands located at 540, 1740, 2010 and 2350 nm. When the different texture samples were mixed (SL + C + SC models), soil moisture was still accurately retrieved (R2 = 0.937) but the soil salt not as well (R2 = 0.47). After stratifying the samples by retrieved soil moisture levels, the R2 of calibrated M_SSCSMC models for soil salt concentrations improved to 0.951. This two-step method also showed applicability for analyzing soil-salt samples in the field. The M_SSCSMC models resulted in R2 values equal to 0.912 when moisture is lower than 0.15, and R2 values equal to 0.481 when soil moisture is between 0.15 and 0.2.

Elimination of the soil moisture effect on the spectra for reflectance prediction of soil salinity using external parameter orthogonalization method

Abstract. Soil salinization is a common desertification process, especially in arid lands. Hyperspectral remote sensing of salinized soil is favored for its advantages of being efficient and inexpensive. However, soil moisture often jointly has a great influence on the soil reflectance spectra under field conditions. It is a challenge to establish a model to eliminate the effect of soil moisture and quantitatively estimate the salinity contents of slightly and moderately salt-affected soil. A controlled laboratory experiment was conducted by way of continuously monitoring changes of soil moisture and salt content, which was mainly focused on the slightly and moderately salt-affected soil. We investigated the external parameter orthogonalization (EPO) method to remove the effect of soil moisture (4 to 36% in weight base) by preprocessing soil spectral reflectance and establishing the partial least squares regression after EPO preprocessing model (EPO-PLS) to predict soil salt content. Through comparing PLS with EPO-PLS model, R2 and ratio of prediction to deviation rose from 0.604 and 1.063, respectively, to 0.874 and 2.865 for validation data. Root mean square error and bias were, respectively, reduced from 1.163 and 0.141  g/100  g to 0.718 and 0.044  g/100  g. The performance of the model after EPO algorithm preprocessing was improved significantly.

Hyperspectral reflectance models for soil salt content by filtering methods and waveband selection

Abstract 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.

Effects of water, salt and nitrogen stress on sunflower (Helianthus annuus L.) at different growth stages

Experiments in soil columns were conducted to evaluate the single and interactive effects of water, salt and nitrogen stress at different sunflower (Helianthus annuus L.) growth stages in Hetao Irrigation District, China. The study factors included soil salinity (S0: ECe=2.5-3.6 dS m -1; S1: ECe=9.6-10.7 dS m -1), soil moisture (W0: 35 %-55% of field water capacity; W1: 75%-100% of field water capacity), and nitrogen application rates (N0: 0 kg N ha-1; N1: 135 kg N ha -1). The results indicated that the S1 treatments increased the duration of the seedling stages by 23.91% but decreased the duration of maturity by 33.09% on average compared with the S0 treatments. Similarly, water deficit significantly retarded anthesis and prolonged the total growth period. The comprehensive stress assessment index (CSAI) was obtained using principal component analysis (PCA) and membership function analysis (MFA). The CSAIs in different treatments showed that soil salinity was the main limiting factor for sunflower vegetative growth from seeding to bud (SS1), whereas water stress dominated the development from bud to flowering (SS2) and flowering to maturity (SS3). Although statistically non-significant, nitrogen stress was intensified after bud initiation and the CSAI in W1S0N0 treatment was 40.68% lower than W1S1N1 treatment in SS3. Moreover, the interactive effects of the three factors were complicated. Our experiments suggested that adequate water supply after bud initiation and the reasonable nitrogen application rate (135 kg N ha-1) can alleviate adverse effects on sunflower reproductive growth under different saline conditions.

Effects of different irrigation strategies on soil water, salt, and nitrate nitrogen transport

Abstract 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.

Design of a new TDR probe to measure water content and electrical conductivity in highly saline soils

PurposeThe inappropriate irrigation is accelerating the soil salinization in western irrigation districts of China. The amelioration of salinized land must be based on large amount of water content and salinity data. Plastic coated TDR has been designed to measure water content accurately in highly saline soil, but the soil bulk electrical conductivity cannot be measured due to the coated materials. In order to measure the volumetric water content and bulk electrical conductivity in highly saline soils at the same time, a parallel three-wire TDR probe with central rod coated which was used to measure water content and a triangle three-wire TDR probe which was used to measure electrical conductivity were integrated in one probe with four rods and one slide switch.Materials and methodsThe influence of angle in triangle three-wire TDR probe and the non-working rod on water content or electrical conductivity measurement were fully discussed through HFSS simulation and NaCl solution test. In the soil column experiment, four levels of salinity, 0.2, 0.4, 0.6, and 1.0% were set, the soil water content decreased from 30% in mass through the evaporation and measured by TDR and electronic balance. Then the probe was calibrated by model of Topp and Evett with these data.Results and discussionThe results show that probe has the largest EPA (polarization degree index) in angle from 97° to 138°; the non-working rod will enhance the EPA during this angle range and the four-wire probe with angle of 120° is optimal; the PVC is a better insulated material which can improve the effective salinity scope; the Evett model could improve the water content measurement greatly especially in soil with higher salinity.ConclusionsThis new four-wire insulated probe can be applied as a beneficial use to monitor the moisture and electrical conductivity in highly saline soils.

Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data

Abstract. This study explored three techniques for estimating the soil salt content from Landsat data. First, the 127 items of in situ measured hyperspectral reflectance data were collected and resampled to the spectral resolution of the reflectance bands of Landsat 5 and Landsat 8, respectively. Second, 12 soil salt indices (SSI) summarized from previous literature were determined based on the simulated Landsat bands. Third, 127 measurement groups with Landsat bands and SSI were randomly divided into training (102) and testing subgroups (25). Three techniques including partial least square regression (PLSR), support vector machine (SVM), and deep learning (DL) were used to establish a soil salinity model using SSI and the simulated Landsat bands as independent variables (IV), respectively. Results indicated that PLSR with SSI performed best for both simulated Landsat 5 and Landsat 8 data. Compared with PLSR, SVM underestimated soil salt content, whereas DL obtained centralized simulations and failed to capture the lower and upper observations. We recommend the PLSR model with SSI as IV to estimate soil salt content because it can identify >66% moderate-to-high-saline soils, which indicates its great potential for soil salt monitoring in arid or semiarid regions.