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1

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

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2017

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Vol. 24, nr 4

501--516

EN

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.

2

Nitrogen Transportation and Transformation Under Different Soil Water and Salinity Conditions

Zeng W. Z., Ma T., Huang J. S., Wu J. W.

Ecological Chemistry and Engineering. S

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2016

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Vol. 23, nr 4

677--693

EN

Soil nitrogen transportation and transformation are important processes for crop growth and environmental protection, and they are influenced by various environmental factors and human interventions. This study aims to determine the effects of irrigation and soil salinity levels on nitrogen transportation and transformation using two types of experiments: column and incubation. The HYDRUS-1D model and an empirical model were used to simulate the nitrogen transportation and transformation processes. HYDRUS-1D performed well in the simulation of nitrogen transportation and transformation under irrigated conditions (R2 as high as 0.944 and 0.763 for ammonium and nitrate-nitrogen simulations, respectively). In addition, the empirical model was able to attain accurate estimations for ammonium (R2 = 0.512-0.977) and nitrate-nitrogen (R2 = 0.410-0.679) without irrigation. The modelling results indicated that saline soil reduced the rate of urea hydrolysis to ammonium, promoted the longitudinal dispersity of nitrogen and enhanced the adsorption of ammonium-nitrogen. Furthermore, the effects of soil salinity on the nitrification rate were not obviously comparable to the effects of the amount of irrigation water. Without irrigation, the hydrolysis rate of urea to ammonium decreased exponentially with the soil salinity (R2 = 0.787), although the nitrification coefficient varied with salinity. However, the denitrification coefficient increased linearly with salinity (R2 = 0.499).

3

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

Zeng W. Z., Huang J. S., Xu C., Ma T., Wu J. W.

Ecological Chemistry and Engineering. S

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2016

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Vol. 23, nr 1

117--130

EN

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.

4

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

Xu C., Zeng W. Z., Wu J. W., Huang J. S.

Ecological Chemistry and Engineering. S

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2015

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Vol. 22, nr 4

589--609

EN

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.

PL

Nawadnianie przerywane zwraca uwagę jako technologia oszczędnego użycia wody w regionach suchych i półpustynnych. Dla zrozumienia wpływu parametrów nawadniania przerywanego, takich jak czas (IRA), częstotliwości stosowania nawadniania (IRAF), odstępy czasu nawadniania (IRI), a także struktury gleby (ST) na magazynowanie wody i substancji rozpuszczonych, przeprowadzono eksperyment przerywanego nawadniania na 33 mikropoletkach w Mongolii Wewnętrznej, w Chinach. Wyniki doświadczeń użyto do kalibracji i walidacji oprogramowania HYDRUS-1D. Następnie 3 ST (mulisty piasek gliniasty, muliste iły i gliny pylaste), 5 IRA (2, 4, 6, 8 i 10 cm), 4 IRAF (2, 3, 4 i 5 razy) i 4 IRI (1, 2, 3 i 4 dni) połączono ogółem w 240 scenariuszy symulowanych przez HYDRUS-1D. Analiza wariancji (ANVOA) symulowanych wyników wykazała, że ST, IRA i IRAF miały znaczący wpływ na sól i azot azotanowy (NO3–-N), składowane na głębokości 0-40 cm gleby w nawadnianiu przerywanym, podczas gdy ST wpływał tylko na magazynowanie wody w glebie. Ponadto, procentowe ługowanie soli (SLP) i efektywność wykorzystania wody (WUE) zostały obliczone dla głębokości 0-40 cm i statystyczne modele predykcyjne dla SLP zostały ustalone na podstawie analizy wariancji i za pomocą analizy regresji wielokrotnej w każdej strukturze gleby. Aby określić optymalną strategię sporadycznego nawadniania, zaproponowano ograniczenie warunków magazynowania wody w glebie (około pojemności polowej), magazynowania soli (mniejsze niż 168 mg · cm–2), WUE (jak największa) w 0-40 cm głębokości i całkowitej ilości wody do nawadniania (mniej niż 25 cm). Przed siewem optymalna strategia nawadniania gleb mulistych gliniastych zakładała odpowiednio 6 cm IRA, 3 razy IRAF i 2 dni IRI. Dla gliny pylastej i ilastych gleb gliniastych założono IRA, IRAF i IRI odpowiednio 8 cm, 3 razy i 2 dni.

5

Effect of Salinity on Soil Respiration and Nitrogen Dynamics

Zeng W. Z., Xu C., Wu J. W., Huang J. S., Ma T.

Ecological Chemistry and Engineering. S

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2013

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Vol. 20, nr 3

519--530

EN

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.

PL

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.