基于环境变量的渭干河-库车河绿洲土壤盐分空间分布

土壤属性的数字制图对精准农业生产和环境保护治理至关重要。为了在大尺度上尽可能精确的监测土壤盐分空间变异性,该文使用普通克里格(ordinary kriging,OK)、地理加权回归(geographically weighted regression,GWR)和随机森林(random forest,RF)方法,结合地形、土壤理化性质和遥感影像数据等16个环境辅助变量,绘制渭干河-库车河绿洲表层土壤盐分分布图。基于决定系数(R^2)、均方根误差(RMSE)和平均绝对误差(MAE)验证模型精度。结果表明:不同方法预测的盐分分布趋势没有显著差异,大体上从研究区的西北向东南部方向增加;结合辅助变量的不同预测方法中,RF方法预测精度最高,R^2为0.74,RMSE和MAE分别为9.07和7.90 mS/cm,说明该模型可以有效地对区域尺度的土壤盐分进行定量估算;RF方法对电导率(electric conductivity,EC)低于2 mS/cm时预测精度最高,RMSE为3.96 mS/cm,很好的削弱了植被覆盖对电导率EC的影响。     

基于电磁感应的典型干旱区土壤盐分空间异质性

为研究干旱区土壤盐分空间异质性,指导农业生产实践,运用大地电导率仪（EM38、EM31）对研究区域进行移动式磁感调查,获取表观电导率（ECa）。同时,通过27个校准点的采样和ECa测量,建立土壤盐分的电磁感应解译模型。干旱区土壤盐分质量分数与EM38、EM31水平模式读数（H38、H31）显示出良好的相关性（R=0.935）,可以利用ECa结合GIS和地统计学知识研究土壤盐分的空间分布。采用两种方法进行研究：一种是先利用解译模型获取磁感调查点的土壤盐分质量分数,然后进行地统计分析研究其空间分布;另一种是先利用地统计分析研究H38和H31的空间分布,然后利用解译模型通过栅格运算计算盐分质量分数,精度检验显示前者预测值与实测值之间的相关性更好（R2, 0.888＞0.873）;标准差较低（std. 0.414＜0.426）,具有更高的预测精度。研究结果表明,基于电磁感应研究干旱区土壤盐分空间异质性是切实可行的,这对于土壤盐渍化的快速诊断,指导农业生产和促进精准农业的发展具有重要的意义。     

Estimating Soil Salinity in the Yellow River Delta,Eastern China——An Integrated Approach Using Spectral and Terrain Indices with the Generalized Additive Model

Soil salinity is one of the most severe environmental problems worldwide. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. The aims of this study were to use remote sensed images and digital elevation model (DEM) to develop quantitative models for estimating soil salinity and to investigate the influence of vegetation on soil salinity estimation. Digital bands of Landsat Thematic Mapper (TM) images, vegetation indices, and terrain indices were selected as predictive variables for the estimation. The generalized additive model (GAM) was used to analyze the quantitative relationship between soil salt content, spectral properties, and terrain indices. Akaike’s information criterion (AIC) was used to select relevant predictive variables for fitted GAMs. A correlation analysis and root mean square error between predicted and observed soil salt contents were used to validate the fitted GAMs. A high ratio of explained deviance suggests that an integrated approach using spectral and terrain indices with GAM was practical and efficient for estimating soil salinity. The performance of the fitted GAMs varied with changes in vegetation cover. Salinity in sparsely vegetated areas was estimated better than in densely vegetated areas. Red, near-infrared, and mid-infrared bands, and the second and third components of the tasseled cap transformation were the most important spectral variables for the estimation. Variable combinations in the fitted GAMs and their contribution varied with changes in vegetation cover. The contribution of terrain indices was smaller than that of spectral indices, possibly due to the low spatial resolution of DEM. This research may provide some beneficial references for regional soil salinity estimation.     

南疆果园滴灌条件下土壤盐分的空间异质性分析——以农十四师224团典型地块为例

土壤盐渍化是制约干旱半干旱地区农业生产的主要障碍因子之一,在新疆,尤其是南疆,盐渍化更为严重。系统认识和掌握盐渍土的空间变异特征对于治理改良盐渍土和土壤盐渍动态变化研究具有重要意义。基于区域变量理论,在GIS和地统计学技术支持下,通过地统计学的半变异函数和Kriging空间插值,以南疆和田地区农十四师224团为研究区,定量分析了试验区内不同层次土壤盐分的空间变异特征。结果表明:140~180 cm土层土壤盐分含量半方差函数的理论分布模型属于指数模型,其它各层属于球状模型;各土层含盐量在一定的区域范围内具有空间结构特征,都呈中等强度空间自相关性;由kriging插值可知,各土层含盐量的空间分布主要表现为条带状伴有斑块状分布。总体来说,试验区土壤盐渍化与次生盐渍化的防治是近期农业管理中所必须考虑的重要因素。     

温带沙漠土壤空间分布的尺度依赖性研究

Spatial variation is a ubiquitous feature of natural ecosystems, especially in arid regions, and is often present at various scales in these regions. To determine the scale dependence of the heterogeneity of soil chemical properties and the dominant scales （factors） for soil heterogeneity in arid regions, the spatial variability of soil resources was investigated in the Gurbantunggut Desert of Central Asia at the scales of 10-3, 10-2, 10-1, 100, 101, 102, 103 and 104 m （from individual plant to population or community to ecosystem）. Soil chemical properties including pH, electrical conductivity （EC）, organic carbon, total nitrogen, available nitrogen, total phosphorus, and available phosphorus were considered in the investigation. At a scale of 10-1 m, which represented the scale of individual plant, significant enrichment of soil resources occurred under shrub canopy and ＂fertile islands＂ formed in the desert ecosystem. Soil EC exhibited the largest heterogeneity at this scale, indicating that individual plants exerted a great influence on soil salinity/alkalinity. Soil nutrients exhibited the greatest heterogeneity at a scale of 102 m, which represented the scale of sand dune/interdune lowlands （between communities）. The main important factors contributing to soil spatial heterogeneity in the Gurbantunggut Desert were individual plants and different topographic characteristics, namely, the appearance of vegetation, especially shrubs or small trees, and existing sand dunes. Soil salinity/alkalinity and soil nutrient status behaved differently in spatial heterogeneity, with an inverse distribution between them at the individual scale.     

压砂地土壤盐分空间变异规律

[目的]研究不同种植年限压砂地及裸地不同土层土壤盐分的空间变异特征,为西北干旱地区土壤盐渍化改良和利用提供理论依据。[方法]以甘肃省景泰县压砂地及裸地不同土层土壤盐分数据为基础,运用经典统计学和地统计学相结合的方法,分析土壤盐分统计特征值,半方差模拟模型及其拟合参数和等值线分布图。[结果]压砂地土壤盐分均值明显低于裸地,且裸地老砂地新砂地中砂地,裸地与压砂地各层土壤盐分的均值差异较大,且土层Ⅳ(30—50cm)土层Ⅲ(20—30cm)土层Ⅱ(10—20cm)土层Ⅰ(0—10cm);裸地Ⅱ,Ⅲ,Ⅳ层,中砂地各层土壤盐分属于弱变异性,其他各地型各土层土壤盐分都属于中等变异性。裸地Ⅰ,Ⅲ层,新砂地第Ⅲ层,中砂地第Ⅰ层,老砂地Ⅰ,Ⅱ,Ⅲ层土壤盐分半方差函数最佳理论模型为指数模型,裸地第Ⅱ层为线性模型,其他各地型各土层均为球状模型。裸地第Ⅱ层土壤盐分具有弱空间相关性,其余各土层土壤盐分均具有强相关性。试验区土壤盐分北部高于南部,且各土层土壤盐分在一定范围内存在空间上的变异性和相关性。[结论]土壤表层压砂能有效减小土壤盐分表聚,抑制土壤次生盐渍化,不同种植年限压砂地不同土层土壤盐分在一定范围内存在空间上的变异性和相关性。     

土壤盐分和含水量的空间变异性及其CoKriging估值——以黄河三角洲地区典型地块为例

以黄河三角洲地区典型地块为研究区，运用经典统计学和地统计学相结合的方法研究了不同深度土层盐分和含水量的空间变异特征。结果表明：研究区土壤盐分和含水量普遍较高，土壤表层积盐作用明显；各层土壤盐分均属于中等变异强度，土壤含水量除表层为中等变异强度外，其余各层为弱变异强度；受结构性因素和随机性因素的共同作用，各土层盐分和含水量均具有中等的空间相关性。对Kriging插值结果分析表明，研究区各土层盐分和含水量在一定的范围内均存在着空间上的相关性和变异性，表层土壤盐分和含水量的空间分布主要受微地形和气候条件的影响，地下水性质是主导深层土壤盐分及含水量空间分布的主要因素。分别采用表层土壤盐分及相应土层含水量对深层土壤盐分进行CoKriging估值，均能提高估值精度，采用表层土壤盐分对深层土壤盐分进行CoKriging估值的精度要明显高于采用土壤含水量，其估计方差减少百分数最大达到167．36％。     

典型绿洲区土壤盐分的空间变异特征

针对绿洲区存在的土壤盐化制约干旱区农业可持续发展问题,应用面域土壤信息调研采集,结合地统计学与GIS技术对新疆典型绿洲区土壤盐分空间变异特征进行研究。研究结果表明:0～30cm耕层土壤盐分呈现强变异性,30cm以下土层呈现中等变异。各土层土壤盐分均呈现中等空间自相关性,空间相关距离在24～28km范围内。各等级盐化土在不同深度土层的分布方位基本一致。各土层非盐化土面积均占主导地位,都占研究区总面积的69%以上;其次为轻度盐化土面积,占研究区总面积的19%～30%;各土层中度盐化土面积都在3.4%以下;重度盐化土仅在0～30cm土层有极少量分布。随着土层深度的增加,非盐化土和中度盐化土的面积逐渐减小,轻度盐化土面积逐渐增大。本研究对于指导干旱区绿洲农业生产、保障区域土壤资源可持续合理利用具有重要的理论价值和明确的实际应用前景。     

银川平原土壤盐分及盐渍土的空间分布格局

系统认识和掌握盐渍土的空间分布特征,是治理改良盐渍土的基础。在总面积约6 184.9 km2的银川平原引黄灌区布设101个采样点,分层（0～180 cm）测定了土壤全盐量,应用地统计学方法结合GIS技术对其空间分布特征进行了研究。结果表明,银川平原各层土壤盐分的分布类型均比较复杂,呈高度的偏态分布。土壤盐分的变异属于中等变异强度。0～120 cm土层盐分的空间相关距离一般在20～28 km;而深层（＞120 cm）土壤的空间相关距离较大,约34 km。银川平原表层土壤属于非盐化土、轻度、中度、重度盐渍土和盐土的土地面积分别为0、1 508.8、3 614.9、982.6和78.6 km2,总体属于中度和轻度盐化土类型,且呈现一定的盐分表聚趋势。重度盐渍土和盐土主要分布在银北的石嘴山市、平罗县、惠农县一带和银南的部分地区。总体来说,银川平原的土壤盐渍化现象依然严重,应加强研究与治理改良。     

环境敏感变量优选及机器学习算法预测绿洲土壤盐分

基于机器学习预测干旱区(如新疆)土壤盐分的研究目前较少涉及且敏感变量的筛选还需深入探讨。该研究比较5种机器学习算法(套索算法,The Least Absolute Shrinkage and Selection Operator-LASSO;多元自适应回归样条函数,Multiple Adaptive Regression Splines-MARS;分类与回归树,Classification and Regression Trees-CART;随机森林,Random Forest-RF;随机梯度增进算法,Stochastic Gradient Treeboost-SGT)在3个不同地理区域(奇台绿洲,渭-库绿洲和于田绿洲)的性能表现;参与的变量被分为6组:波段,植被相关变量集,土壤相关变量集,数字高程模型(digital elevation model,DEM)衍生变量集,全变量组,优选变量组(全变量组经过算法筛选后的变量集合)。通过算法筛选,以示不同研究区的盐度敏感变量。同时借助以上述6组结果评判算法的性能。结果表明:综合分析6个变量组的R2和RMSE,预测精度排名如下:优选变量组植被指数变量组土壤相关变量组波段DEM衍生变量组。由于结果不稳定,全变量组未参与排名。在所有变量中,植被指数(EEVI,ENDVI,EVI2,CSRI,GDVI)和土壤盐度指数(SIT,SI2和SAIO)与土壤盐度相关性高于其他变量。综合评价以上5种算法,Lasso和MARS的预测结果出现极端异常值,但其预测结果能基本呈现土壤盐分空间分布格局。CART的结果能清晰分辨灌区和非灌区土壤盐分的分布态势,但二者内部并无太多变化且稳定性较差。RF和SGT的结果显示,二者在3个绿洲的土壤盐分值域范围和土壤盐分空间分布格局相似,纹理信息相对其他3个算法更为丰富。更为重要的是,算法在各个地区的结果都较为稳定。二者相比,SGT验证精度相对最高,其次为RF。     

盐渍化灌区土壤水盐时空变异特征分析及地下水埋深对盐分的影响

针对盐渍化灌区土壤盐渍化问题,以河套灌区下游乌拉特灌域为研究区,通过野外实测与室内试验分析结合,采用统计学方法地质统计学原理分析表层土壤(0-20,20-40 cm)及深层土壤(40-100 cm)含水率与盐分(EC值)时空分布和变异规律,以及探求地下水埋深对土壤盐分的影响。结果表明:(1)除6月0-20 cm(9.779%)外,表层土壤含水率变异系数均在12.384%~19.667%,属于中等变异性,深层土壤含水率变异系数较小,在3.513%~9.757%,属于弱变异性;表层土壤盐分(EC值)变异系数在100.845%~129.279%,属于强变异性,深层土壤盐分变异系数均在83.685%~98.853%,属于中等变异性;随着土壤深度的增加,含水率和盐分的变异性都相对减弱。(2)不同时期土壤含水率和盐分在一定范围内具有空间结构特征,均可用高斯模型模拟,各层土壤含水率空间相关度在0.038%~20.408%,各层土壤盐分空间相关度在0.043%~8.374%,均小于25%,说明具有强烈的空间相关性,可以认为主要是受结构性因素的影响,其自相关引起的空间变异性较强。(3)试验区土壤盐分主要集中在北侧盐荒地,由于蒸发强烈,包气带毛细水上升,把深层土壤以及地下水中的可溶性盐类带到土壤表层,致使盐分升高,属于典型的盐分表聚型土壤,需及时防治与治理,同时土壤盐分受地下水埋深的影响较大,随着地下水埋深减小而增大,荒地地下水埋深与土壤盐分满足线性关系,耕地地下水埋深与土壤盐分满足指数关系。荒地0-20 cm土壤盐分含量随地下水埋深变化趋势较大,20-40,40-100 cm土壤盐分含量随地下水埋深变化趋势较小,耕地地下水埋深在1~1.6 m时,土壤盐分含量随着地下水埋深变化趋势较大,当地下水埋深大于1.6 m时,土壤盐分含量随着地下水埋深变化趋势较小。研究结果为河套灌区下游盐渍化土壤的防治与改良提供了重要的理论基础和参考依据。     

Spatial variability of early season nitrogen availability indicators in corn

Abstract

The pre‐sidedress nitrate test (PSNT), a soil test used to refine sidedress fertilizer nitrogen (N) recommendations in corn (Zea mays L.), has shown great potential to reduce the excessive use of N fertilizer in corn production systems in the Northeast. The research reported in this paper evaluated the application of the PSNT to site‐specific management by investigating the spatial variability of several early season soil and plant indicators of N availability in a uniformly managed 16‐ha no‐till corn production field. The spatial variation in PSNT nitrate concentrations, total organic carbon (C) and N in the soil, and corn biomass yield and N content at the fifth leaf development stage were characterized from 189 samples taken during the 1994 growing season on a triangular grid with a 30 m minimum separation distance. There were no significant correlations found between any of the variables studied. All the variables studied were normally distributed with the exception of total organic C and biomass yield, both of which approximated a log‐normal distribution. Geostatistical analysis revealed significant spatial variation and spherical semivariogram models were developed for each variable. Results suggest that the average minimum grid size used in this study (30 m) may not have been adequate for an accurate estimation of nugget semivariance in total soil C and N and corn tissue N concentration. Plant indicators exhibited landscape features influencing biomass production and N uptake that were not revealed by the soil variables studied. These results illustrate the potential utility of integrating soil and plant productivity indicators to identify and address limitations to crop production using site‐specific management.     

长江三角洲地区滩涂土壤盐分空间分布特征研究

盐碱地盐分空间分布精准识别及形成机理解析对盐碱地科学利用具有重要意义。针对传统点状采样监测方法工作量大、代表性差和检测费用高等问题,本研究采用电磁感应仪精准调查技术对上海崇明某滩涂农场进行土壤盐分空间分布评估,全面快速获得盐分空间分布信息,解析土壤盐分形成机理,进而分类分区分级指导农业生产和科学精准改良。结果表明：土壤表观电导率(EC_a)和土壤盐分含量呈显著线性正相关关系(R²>0.9,P<0.05),土壤表观电导率的变化可以用来表征土壤盐分含量的变化;通过克里金插值研究区内土壤盐分的空间变异分布状况发现,研究区土壤盐分空间分布不均匀,有近80%区域存在轻中度盐碱化问题;通过定点取样土壤盐碱剖面评价,发现研究区土壤盐分具有较强的表聚性和变异强度,且变异系数随土壤深度增加不断减小;将表观电导率解译出的土壤盐分和土壤pH、有机质含量及主要组成离子含量进行相关性分析,剖析出该区域盐渍化类型以硫酸盐型为主,海水浸渍侧渗和地下水返盐影响是该区域盐渍化形成的主要原因。本研究实现了对土壤盐碱化快速、大范围、科学且动态的监测,对于全面提升盐碱地农...     

Changes in variance and correlation of soil properties with scale and location: analysis using an adapted maximal overlap discrete wavelet transform

The magnitude of variation in soil properties can change from place to place, and this lack of stationarity can preclude conventional geostatistical and spectral analysis. In contrast, wavelets and their scaling functions, which take non‐zero values only over short intervals and are therefore local, enable us to handle such variation. Wavelets can be used to analyse scale‐dependence and spatial changes in the correlation of two variables where the linear model of coregionalization is inadmissible. We have adapted wavelet methods to analyse soil properties with non‐stationary variation and covariation in fairly small sets of data, such as we can expect in soil survey, and we have applied them to measurements of pH and the contents of clay and calcium carbonate on a 3‐km transect in Central England. Places on the transect where significant changes in the variance of the soil properties occur were identified. The scale‐dependence of the correlations of soil properties was investigated by calculating wavelet correlations for each spatial scale. We identified where the covariance of the properties appeared to change and then computed the wavelet correlations on each side of the change point and compared them. The correlation of topsoil and subsoil clay content was found to be uniform along the transect at one important scale, although there were significant changes in the variance. In contrast, carbonate content and pH of the topsoil were correlated only in parts of the transect.     

Characterization of Slightly and Moderately Saline and Sodic Soils in Irrigated Agricultural Land using Simulated Data of Advanced Land Imaging (EO‐1) Sensor

Around the world, especially in semi‐arid regions, millions of hectares of irrigated agricultural land are abandoned each year because of the adverse effects of irrigation, mainly secondary salinity and sodicity. Accurate information about the extent, magnitude, and spatial distribution of salinity and sodicity will help create sustainable development of agricultural resources. In Morocco, south of the Mediterranean region, the growth of the vegetation and potential yield are limited by the joint influence of high temperatures and water deficit. Consequently, the overuse of surface and groundwater, coupled with agricultural intensification, generates secondary soils salinity and sodicity. This research focuses on the potential and limits of the advance land imaging (EO‐1 ALI) sensor spectral bands for the discrimination of slight and moderate soil salinity and sodicity in the Tadla's irrigated agricultural perimeter, Morocco. To detect affected soils, empirical relationships (second‐order regression analysis) were calculated between the electrical conductivity (EC) and different spectral salinity indices. To achieve our goal, spectroradiometric measurements (350 to 2500 nm), field observation, and laboratory analysis (EC of a solution extracted from a water‐saturated soil), and soil reaction (pH) were used. The spectroradiometric data were acquired using the ASD (analytical spectral device) above 28 bare soil samples with various degrees of soil salinity and sodicity, as well as unaffected soils. All of the spectroradiometric data were resampled and convolved in the solar‐reflective spectral bands of EO‐1 ALI sensor. The results show that the SWIR region is a good indicator of and is more sensitive to different degrees of slight and moderate soil salinity and sodicity. In general, relatively high salinity soils show higher spectral signatures than do sodic soils and unaffected soils. Also, strongly sodic soils present higher spectral responses than moderately sodic soils. However, in spite of the improvement of EO‐1 ALI spectral bands by comparison to Landsat‐ETM+, this research shows the weakness of multispectral systems for the discrimination of slight and moderate soil salinity and sodicity. Although remote sensing offers good potential for mapping strongly saline soils (dry surface crust), slight and moderately saline and sodic soils are not easily identified, because the optical properties of the soil surfaces (color, brightness, roughness, etc.) could mask the salinity and sodicity effects. Consequently, their spatial distribution will probably be underestimated. According to the laboratory results, the proposed Soils Salinity and Sodicity Indices (SSSI) using EO‐1 ALI 9 and 10 spectral bands offers the most significant correlation (52.91%) with the ground reference (EC). They could help to predict different spatial distribution classes of slight and moderate saline and sodic soils using EO‐1 ALI imagery data.     

中国新疆绿洲地区多时空尺度上土壤盐分变化特征及其相关驱动因素研究

Located in the inland arid area of central Asia, salt-affected farmlands take up one third of the total irrigated land area in Xinjiang of Northwest China. Spatio-temporal variability of soil salinity and the underlying mechanism are fundamental problems challenging the sustainability of oasis agriculture in China. In this study, the data of total dissolved solids(TDS) measured for soil samples collected from 27 representative study areas in the oasis areas of Xinjiang were analyzed and the coefficient of variation(CV) and stratification ratio(SR) of TDS were used to describe the lateral and vertical soil salinity variations, respectively. Weekly, monthly,and annual changes in soil salinity were also summarized. Results showed that the top(0–20 cm) soil salinity was highly variable(CV 75%) for most studied areas. Lateral variation of soil salinity was significantly correlated with the sampling interval; as a result, a maximum sampling interval of 0.9 m was found for reducing evaluation uncertainty. The top 0–20 cm soil salt accounted for about25.2% of the total salt in the 0–100 cm soil profile. The stratification ratio values(the ratio of TDS at the 20–40 cm depth to that at the 0–20 cm depth) were mostly smaller than 1 and on average 0.92, illustrating that the top 0–20 cm soil contained slightly more salt and a considerable amount of salt still existed in subsurface and deep horizons. Irrigation reduced top soil salinity by 0.52 g kg-1, or14.6%, within the first week. On average, the relative range of soil salinity, calculated to indicate monthly changes in soil salinity, was58.2% from May to September. A 27-year experiment indicated that cultivation increased soil salinity by 44.4% at a rate of 0.14 g kg-1year-1. At small spatio-temporal scales, soil salinity variation was mainly affected by anthropogenic factors, such as irrigation and land use. However, natural factors, including groundwater, topography, and climate conditions, mainly influenced soil salinity variation at large spatio-temporal scales. This study displayed the highly variable nature of soil salinity in space and time. Those driving factors identified in this study could provide guidelines for developing sustainable agriculture in the oasis areas and combating salinization in arid regions of China.     

基于环境变量的中国土壤有机碳空间分布特征

研究中国土壤有机碳(Soil Organic Carbon,SOC)的空间分布特征对SOC储量估算以及农业生产管理具有重要意义。以全国第二次土壤普查2473个土壤典型剖面的表层(A层)SOC含量为研究对象,探寻地形、气候和植被等环境因素对SOC空间异质性分布的影响;以普通克里格法为对照,利用地理加权回归、地理加权回归克里格、多元线性回归和回归克里格模型建立SOC空间预测模型;并分别绘制了中国SOC的空间分布预测图。结果表明:(1)SOC含量与年均降水量、年均温、归一化植被指数、高程以及地形粗糙指数呈极显著相关关系;(2)平均绝对估计误差、均方根误差、平均相对误差和皮尔逊相关系数等模型验证指标表明地理加权回归的预测精度优于其他模型,可以更好地绘制SOC在大尺度上的空间分布特征;(3)较高SOC含量主要分布在研究区东北部、西南部以及东南部,而西北部SOC含量普遍偏低。本文以期从大尺度上探讨土壤属性与环境变量之间的相关关系,为全国土壤属性的空间制图提供一定的解决方案和思路。     

基于电磁感应仪的土壤盐渍化剖面特征解译研究

为了快速、精准解译区域尺度土壤盐分特征,有必要建立土壤剖面盐分信息精确解译模型。以新疆农灌区不同土壤质地的盐渍化土壤为研究对象,利用电磁感应式大地电导率仪EM38获取土壤表观电导率,构建了基于EM38的两种土壤质地剖面分层盐分解译模型,并对盐分解译回归模型进行了精度检验。结果表明:两种质地的土壤剖面盐分含量变异均较大,中壤土各层电导率变异系数在58%~98%,呈现中等变异强度,而砂土表层(0~40 cm)变异系数达到100%以上,属于强变异强度,深层土壤变异系数介于76%~88%属于中度变异强度。两种质地土壤电导率与磁感表观电导率EMh、EMv间呈显著的相关关系,水平和垂直测量模式都能够对不同深度土盐层分进行预测,且以EMh+EMv为自变量的二元回归解译模型具有较高的精度,相关系数R达到0.94以上。中壤土EM38的盐分预测在不同深度土层的验证结果决定系数达到0.59以上,砂土质地土壤盐分预测验证R2达到0.36以上,预测精度与土壤剖面盐分变异性呈现显著负相关,其相关系数为-0.86,中壤土质地的解译效果优于砂土质地。分析EM38在预测不同土壤质地盐分精度上的差异性,构建了电磁感应式土壤剖面盐分含量的预测模型。研究结果引入土壤质地变量,可为大面积土壤盐渍化的快速精确测定提供理论依据。     

渭-库绿洲多尺度景观格局与盐度关系

中国盐渍化土壤面积大,分布广,对区域农业发展构成了严重的威胁。探索土壤景观格局与盐度的关系将有助于盐渍化监测和评估。该研究选择渭-库绿洲GF-1影像为数据源,结合研究区同期38个样点不同剖面土壤盐度数据,对样点缓冲区景观格局与土壤盐度做Pearson相关分析和逐步回归分析,揭示土壤盐度空间分布格局,探讨景观格局与盐度的定量关系。结果表明:1)水平方向上,土壤盐分高值区主要集中分布在绿洲东部荒漠地带和绿洲西部农牧交错区;垂直方向上,渭-库绿洲表层土壤盐渍化现象最为严重,其他各层土壤盐渍化情况相对较轻,盐渍化程度随着深度下降呈降低趋势;2)绿洲区域易受人类活动影响,景观破碎化程度高,而同一区域不同梯度下,随着缓冲距离的增加,区域景观类型增多、均质性降低、多样性增强;3)耕地利用数量指标能较好指示土壤盐度状况,而水体面积、盐渍地面积、其他用地面积、最大斑块指数(largest patch index,LPI)、蔓延度(contagion index,CONTAG)和分维数(perimeter-area fractal dimension,PAFRAC)对盐度影响相对较弱;4)除0~10 cm层外,自10~20 cm至80~100 cm层逐步回归方程的自变量中,耕地面积、水体面积和最大斑块指数LPI为负效应,而盐渍地面积、其他用地面积、CONTAG和PAFRAC为正效应,最优回归方程决定系数为0.537。该研究确定了渭-库绿洲土壤盐度的分异规律,以及不同盐度对绿洲景观格局的影响程度。研究结果可为西北干旱区绿洲土壤盐度预警提供理论依据,同时为干旱区景观格局研究提供一定的参考价值。     

Spatial evaluation of soil salinity using the WET sensor in the irrigated area of the Segura river lowland

The electrical conductivity of the water within the soil pores (EC_p) measured with the WET sensor, appears to be a reliable estimate of soil salinity. A methodology combining the use of the WET sensor along with geostatistics was developed to delimit and evaluate soil salinity within an irrigated area under arid to semiarid Mediterranean climate in SE Spain. A systematic random sampling of 104 points was carried out. The association between EC_p and the saturation‐extract electrical conductivity (EC_se) was assessed by means of correlation analysis. The semivariograms for EC_p were obtained at three different soil depths. Interpolation techniques, such as ordinary kriging and cokriging, were applied to obtain EC_p levels in the unknown places. For each one of the soil depths, a model able to predict EC_se from EC_p was developed by means of ordinary least squares regression analysis. A good correlation (r = 0.818, p < 0.001) between EC_p and EC_se was found. Spherical spatial distribution was the best model to fit to experimental semivariograms of EC_p at 10, 30, and 50 cm soil depths. Nevertheless, cokriging using the EC_p of an adjacent soil depth as an auxiliary variable provided the best results, compared to ordinary kriging. An analytical propagation‐error methodology was found to be useful to ascertain the contribution of the spatial interpolation and ordinary least squares analysis to the uncertainty of the EC_se mapping. This methodology allowed us to identify 98% of the study area as affected by salinity problems within a rooting depth of 50 cm, with the threshold of EC_se value at 2 dS m^–1. However, considering the crops actually grown and 10% potential reduction yield, the soil‐salinity‐affected area decreased to 83%. The use of sensors to measure soil salinity in combination with geostatistics is a cost‐effective way to draw maps of soil salinity at regional scale. This methodology is applicable to other agricultural irrigated areas under risk of salinization.