共查询到20条相似文献,搜索用时 562 毫秒
1.
2.
通过对长期培肥试验田不同处理土壤的比重、容重、孔隙度、团聚体等物理性质的系统分析,企图揭示在长期培肥过程中土壤物理性质演变规律。研究结果表明:未进行培肥作用的土壤(对照)小区仍维持着原来土壤有机质含量水平,该试验小区土壤所具有的是最基础的物理性质,其它不同培肥处理的土壤比重和容重均随有机质积累而明显减小,孔隙度随土壤有机质含量增加而升高。不同培肥措施都明显地促进土壤大团聚体的形成,特别是增加了>0.25mm水稳性团聚体的含量,降低了团聚体破坏率,逐渐地改善了土壤物理性质。在常年休闲过程中土壤结构性能明显地被破坏。 相似文献
3.
通过对沈阳市郊玉米连作土壤的有机质、结构情况以及二者相互关系研究表明,土壤中有机质大致范围在16.31g kg-1~23.72g kg-1,腐殖质大致范围在15.17g kg-1~21.49g kg-1,非腐殖质大致范围在1.14g kg-1~2.59g kg-1;在腐殖质各组分中,胡敏素的含量最高,约占腐殖质总量的58.93%~66.34%,胡敏酸约占16.26%~22.87%,富里酸约占16.82%~18.20%瑚富比约在0.95~1.32之间;干筛处理后>0.25mm的土壤团聚体占到80%以上,其中以>2mm团聚体为主,湿筛处理后>0.25mm的土壤团聚体仅占20%左右,其中0.25~0.5mm团聚体为主,水稳性系数在17.33%~35.67%.对有机质及其各组分与土壤各级团聚体做的相关分析结果表明,有机质与0.25~5mm粒径组的团聚体呈显著相关,与>5mm团聚体不相关,且胡敏酸主要影响1~5mm大团聚体的形成. 相似文献
4.
土壤结构性是影响土壤肥力的一个重要因素。本文运用分形模型对不同植被类型土壤水稳性团聚体进行了研究,探讨了分形维数与土壤肥力的关系。结果表明:土壤团聚体粒径分布的分形维数与水稳性团聚体含量之间存在呈显著线性回归关系,林地和不同开垦年限农地土壤的物理性质随土壤团聚体的分形维数变化而变化,即分形维数越小,>0.25mm水稳性团聚体含量越高,土壤容重越小,总孔度大,毛管孔隙和通气孔隙占总孔隙的比例大,自然含水量和田间持水量相应较高;有机质、全N、碱解N与分形维数呈极显著的负相关,分形模型为土壤肥力研究提供新方法。上述结果在一定程度表明,植被遭到破坏、不合理的人为开垦,是土壤退化和生态环境恶化的主要原因。 相似文献
5.
利用沉降离心法将老虎山黄土-古土壤的酸不溶相分离成<2μm、2~45μm和>45μm三个粒级组分,并分别测定了这些组分和全岩的磁化率。结果表明,无论是全岩,还是三个不同粒级组分,它们的磁化率都可以单独作为长江中下游地区古气候演变的参考指标。其中,<2μm粒级的质量磁化率明显大于全岩和2~45μm、>45μm两粒级,而且其分辨率也明显高于2~45μm和>45μm这两个粒级组分。本实验结果还显示,虽然<2μum的质量磁化率最大,但对全岩质量磁化率的贡献率与2~45μm粒级相当,>45μm粒级的贡献率较低。 相似文献
6.
基于土壤高光谱反射特征可以实现土壤全氮(TN)含量与碳氮比(C∶N)等土壤属性的快速、无损测定,但其估测模型受土壤颗粒粒径水平与光谱指数(预处理)等因素影响。通过研磨准备2、0.25和0.15 mm共3个水平颗粒粒径的土样,分析了原始(RAW)及多次散射校正MSC(Multiple Scattering Correction)、一阶微分FD(First Derivative)、连续统去除CR(Continuum Removal)等预处理的土壤反射光谱与TN含量、碳氮比变化之间的关系,发现土壤研磨可以提高反射光谱对TN含量变化的响应,而FD、CR与MSC等光谱预处理能够明显缩小不同颗粒粒径水平土样的光谱反射-TN含量、碳氮比相关性差异。结果表明:0.25 mm颗粒粒径土样的FD预处理光谱在2 250 nm和2 280 nm处分别与TN含量、碳氮比变化存在最大相关,但最大相关单波段线性回归模型的TN含量、碳氮比估测精度不如全波段光谱PLSR模型。其中,0.25 mm土样RAW光谱全波段PLSR模型估测TN含量的表现最佳(RPD=3.49,R2=0.92,RMSEP=0.1 g/kg);而碳氮比的估测结果并不十分理想,其最优估测模型(0.25 mm土样FD预处理的全波段PLSR模型)的RPD仅为1.21,可能与土样的碳氮比变化范围较小有关,在以后的研究中可以尝试采集更多的样本数量或土壤类型,使训练样本具有较大的变量范围,以取得较好的估测效果。 相似文献
7.
比较了青海省东部山区垂直梯度分布的三种旱作农田土壤(黑钙土、栗钙土、灰钙土),在0~60 cm土层的不同粒级土壤风干团聚体和水稳性团聚体含量间的差异,并结合其它土壤质量指标(有机质、粘粒)对不同土壤结构和抗侵蚀能力进行了综合评价。结果表明,>0.25mm风干团聚体、>0.25mm水稳性团聚体含量和土壤有机质含量与土壤类型间有密切关系。均表现为黑钙土>栗钙土>灰钙土。黑钙土和栗钙土的土壤有机质含量与>0.25mm水稳性团聚体间存在显著正相关关系(P<0.05),灰钙土则无明显相关性;三种土壤粘粒含量与>0.25mm风干团聚体和0.25mm水稳性团聚体含量间无明显相关性。各项指标综合比较,三种土壤抗侵蚀能力大小为:黑钙土>栗钙土>灰钙土。 相似文献
8.
9.
研究了枫香×樟树、楠木×尖叶杜英、椆木×海南红豆、格木×海南红豆、火力楠×阴香、枫香×米老排×降香黄檀、樟树×马占相思混交林林地的土壤物理性质与微生物数量及酶活性.各林地的容重、毛管孔隙、非毛管孔隙、自然含水量、毛管持水量的不同引起其保水性和通气性的差异.细菌是土壤微生物总量的主要组成者.各混交林地的细菌、真菌和放线菌的数量差异大.各混交林地的脲酶、过氧化氢酶和纤维素分解酶活性有一定的差异.放线菌与容重呈显著正相关,而与总孔隙呈显著负相关.脲酶与自然含水量、毛管持水量、毛管孔隙呈显著或极显著正相关. 相似文献
10.
针对经典环刀法在土层浅薄,土壤紧实和粗骨土的山地土壤物理性质研究中的局限性,利用IN-SITU原状取土管对澜沧江流域典型山地的土壤容重、毛管孔隙度、饱和水、毛管水、田间持水量、初渗系数、稳渗系数等土壤物理性质进行研究,并同经典环刀法的测定值相比较。方差分析结果表明:IN-SITU原状取土管法和经典环刀法在土壤物理性质的对比研究中无显著差异。差异性分析中,7项土壤物理指标测定值间F方差相等检验的相伴概率Sig.均大于显著性水平α=0.05,方差相等时T检验结果的7项土壤物理指标相伴概率均大于显著性水平α=0.10,两法均值差的95%置信区间均跨0,表明IN-SITU原状取土管完全可用于土壤物理性质的研究,尤其在山地土壤的研究中显示出较环刀更为明显的优越性。 相似文献
11.
为降低SMOS土壤水分反演算法的复杂度、提高土壤水分反演精度,对SMOS土壤水分反演策略进行调整:将多参数反演改为单参数反演以简化观测与模拟亮温的代价函数,以固定步长(0.001 m3/m3)代替不定步长从而避免复杂的矩阵运算,将围绕土壤水分先验值的少量局部搜索调整为全土壤水分区间(0~0.05 m3/m3)的密集全局搜索。利用美国USCRN 44个站点实测土壤水分分别与SMOS官方反演的土壤水分和SMOS调整算法反演的土壤水分进行对比分析。结果表明:与SMOS相比,算法调整后土壤水分的平均绝对偏差MAD、均方根误差RMSE和无偏均方根误差ubRMSE分别降低了0.012、0.018和0.020 m3/m3。 相似文献
12.
《四川地质科技情报》 《遥感技术与应用》1986,1(1):65-73
In order to reduce the complexity of SMOS official soil moisture retrieval algorithm and improve the accuracy of soil moisture retrievals, a new retrieval strategy on SMOS soil moisture retrieval algorithm was developed. In the new retrieval strategy on SMOS soil moisture retrieval algorithm, the fixed step size (0.001 m3/m3) was used to replace the flexible step size obtained by the SMOS matrix operation. The multi-parameter was changed to a single-parameter in the cost function. The data from 44 USCRN sites in the United States were compared with the soil moisture retrieved from SMOS official algorithm as well as the adjustment of SMOS algorithm. The results show that compared with the SMOS official algorithm, the average absolute deviation, root mean square error,and unbiased root mean square error of the adjustment of SMOS algorithm are reduced by 0.012 m3/m3, 0.018 m3/m3,and 0.020 m3/m3,respectively. 相似文献
13.
14.
为了探讨土壤侵蚀与温度、降雨量、植被指数、地形指数、水热指数和环境质量综合指数的关系,本文对重庆市不同环境条件下的土壤侵蚀进行了分析.在定义土壤侵蚀综合指数的基础上,将年平均温度、大于0°积温,大于10°积温,年降雨量,干燥度、湿润度,地形综合指数、植被指数、热量指数、水分指数,水热指数,和环境质量综合指数等分级数据统一成格网大小为100
m*100 m的栅格(grid)数据.然后,利用GIS的叠加统计分析功能,将这些数据与100
m*100 m格网大小的土壤侵蚀栅格(grid)数据进行叠加统计分析,从而揭示出了土壤侵蚀与这些环境因子之间的关系. 相似文献
15.
16.
17.
土壤机械组成是最基本的土壤参数之一。探讨了应用激光粒度仪分析土壤机械组成的实验条件、检测结果与精度,并与传统的吸管法加以比较。对9个土壤样品分别用激光粒度分析和吸管法作对比测试实验,结果显示:两种方法原理的不同产生了在颗粒含量上激光粒度分析得到的粘粒含量显著低于吸管法,但是两种方法在质地分类上的结果是基本一致的。总之,运用激光粒度分析土壤机械组成是可行的。 相似文献
18.
讨论了土壤磷素形态和有机磷有效性研究进展,分析了传统土壤磷素分级方法局限性,综述了土壤磷素分级方法的研究进展,特别详细地描述了Hedley法和Guppy法。 相似文献
19.
Rocco Panciera Jeffrey P. Walker Edward J. Kim Jean-Pierre Wigneron 《Remote sensing of environment》2009,113(2):435-3354
Soil moisture will be mapped globally by the European Soil Moisture and Ocean Salinity (SMOS) mission to be launched in 2009. The expected soil moisture accuracy will be 4.0 %v/v. The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil-vegetation layer. The model parameters have been calibrated with data acquired by tower mounted radiometer studies in Europe and the United States, with a typical footprint size of approximately 10 m. In this study, aircraft L-band data acquired during the National Airborne Field Experiment (NAFE) intensive campaign held in South-eastern Australia in 2005 are used to perform the first evaluation of the L-MEB model and its proposed parameterization when applied to coarser footprints (62.5 m). The model could be evaluated across large areas including a wide range of land surface conditions, typical of the Australian environment. Soil moisture was retrieved from the aircraft brightness temperatures using L-MEB and ground measured ancillary data (soil temperature, soil texture, vegetation water content and surface roughness) and subsequently evaluated against ground measurements of soil moisture. The retrieval accuracy when using the L-MEB ‘default’ set of model parameters was found to be better than 4.0 %v/v only over grassland covered sites. Over crops the model was found to underestimate soil moisture by up to 32 %v/v. After site specific calibration of the vegetation and roughness parameters, the retrieval accuracy was found to be equal or better than 4.8 %v/v for crops and grasslands at 62.5-m resolution. It is suggested that the proposed value of roughness parameter HR for crops is too low, and that variability of HR with soil moisture must be taken into consideration to obtain accurate retrievals at these scales. The analysis presented here is a crucial step towards validating the application of L-MEB for soil moisture retrieval from satellite observations in an operational context. 相似文献
20.
Monitoring agricultural soil moisture extremes in Canada using passive microwave remote sensing 总被引:1,自引:0,他引:1
Current methods to assess soil moisture extremes rely primarily on point-based in situ meteorological stations which typically provide precipitation and temperature rather than direct measurements of soil moisture. Microwave remote sensing offers the possibility of quantifying surface soil moisture conditions over large spatial extents. Capturing soil moisture anomalies normally requires a long temporal record of data, which most operating satellites do not have. This research examines the use of surface soil moisture from the AMSR-E passive microwave satellite to derive surface soil moisture anomalies by exploiting spatial resolution to compensate for the shorter temporal record of the satellite sensor. Four methods were used to spatially aggregate information to develop a surface soil moisture anomaly (SMA). Two of these methods used soil survey and climatological zones to define regions of homogeneity, based on the Soil Landscapes of Canada (SLC) and the EcoDistrict nested hierarchy. The second two methods (ObShp3 and ObShp5) used zones defined by a data driven segmentation of the satellite soil moisture data. The level of sensitivity of the calculated SMA decreased as the number of pixels used in the spatial aggregation increased, with the average error reducing to less than 5% when more than 15 pixels are used. All methods of spatial aggregation showed somewhat weak but consistent relationship to in situ soil moisture anomalies and meteorological drought indices. The size of the regions used for aggregation was more important than the method used to create the regions. Based on the error and the relationship to the in situ and ancillary data sets, the EcoDistrict or ObShp3 scale appears to provide the lowest error in calculating the SMA baseline. This research demonstrates that the use of spatial aggregation can provide useful information on soil moisture anomalies where satellite records of data are temporally short. 相似文献