土壤有机物质对土壤介电常数的影响

土壤介电常数是开展微波土壤水分和冻融状态的监测的基础,也是植被和积雪的下垫面边界条件,然而目前已有的介电常数研究都没有对高有机质含量的土壤开展系统观测。本文将土壤中的自然有机物质分为腐殖质和植物性残留物两类。采用控制变量实验方法,通过测量5种不同有机质含量的东北黑土和加入不同比例毛白杨碎屑的扁都口草甸土,研究了腐殖质和植物性残留物的对土壤介电常数的影响。结果表明,腐殖质会降低干燥土壤的容重,从而发挥间接作用,使介电常数降低;而对于相同容重下观测的潮湿土壤,腐殖质含量较多的土壤介电常数更大。与Dobson模型的比较显示,在29℃室温下,腐殖质对25%重量含水量潮湿土壤实部的影响在±2左右,虚部能达到1。与腐殖质相比,植物性残留物对风干土壤和潮湿土壤的影响都十分明显。植物性残留物能有效地疏松土壤并代入植物组分的介电特征。当重量含水量为30%时,含毛白杨含量为20%的混合土壤比纯扁都口土壤在实部平均减小3—7左右,虚部减小 1—3左右。因此,根据实验观测以及和模型的比较,土壤中的有机物质会改变土壤介电性质,对微波遥感造成影响。     

山区地形效应对微波辐射特征与土壤水分反演的影响—以青藏高原地区为例

采用星载微波辐射计AMSR-E的低频C波段(6.925GHz),改进了山区微波辐射传输方程,以中国青藏高原地区为例,研究山区可能产生的多种地形效应对微波辐射特征以及土壤水分反演的影响。结果表明,地形效应使得垂直极化亮温最多衰减达到16K,水平极化的亮温最大增强了18K,土壤水分在地形的影响下将被高估超过最大允许误差4%。最后,利用地形效应模拟模型计算的山区地表有效发射率,为山区土壤水分的反演提出了可行的地形校正方法。     

基于电磁感应的干旱区土壤盐渍化定量遥感研究

以南疆典型干旱区Landsat 7 ETM+遥感图像为数据源,利用决策树分类法提取农业用地,并对农业用地进行移动式电磁感应调查(简称磁感调查)和光谱特征提取,同时分析磁感数据和图像光谱特征与土壤盐分含量的相关性,从而建立土壤盐分的定量反演模型。研究结果表明:土地利用类型决策树的分类精度达到93.75%,Kappa系数达0.915 4;经多元逐步回归分析,磁感调查获得的土壤盐分含量与差值植被指数(DVI)、ETM+图像第二波段像元值(B2)以及比值植被指数(RVI)间具有显著相关性,由此建立的遥感反演模型可用于土壤盐分含量的定量反演。经89个样点检验,基于磁感调查的土壤盐分遥感反演精度虽低于基于磁感调查的地统计空间分析的精度,但遥感定量反演值与磁感调查实测值仍具有良好的相关性,而且精度较高,因此利用本文方法进行土壤盐渍化大面积监测是快速有效的途径。     

基于GIS的土壤质量评价研究

介绍了基于GIS土壤质量等级评价的实现过程,探讨了土壤等级评价因子的选择和因子权重的确定方法。在分析了几种常用方法在土壤质量评价中的应用特点并指出几种方法的局限性之后,提出了顾及因子稳定性的多元线性回归分析法,并以南方某地为例进行了实例分析。     

Computerized database on salt affected soils in western and central India using GIS

Salt affected soils occupy significant areas in western and central India manifested by the arid and semiarid climate, sandy/clayey soil texture, absence of natural drainage, and inadequate infrastructure and irrigation development. These soils are productive following reclamation and appropriate management. The National Remote Sensing Agency, Hyderabad (India) published state-wise maps of salt affected soils in India on 1:250,000 scale using a legend that includes physiography, soil characteristics, and the aerial extent of the mapping units. In the analogue form, voluminous data contained in such maps were difficult to handle by users of varied backgrounds. An attempt was made to prepare a computerized database of salt affected soils for easy access, retrieval, and manipulation of spatial and attribute data useful for management of salt affected soils. The salt affected soils maps were prepared, for Rajasthan, Gujarat, Madhya Pradesh, and Maharashtra states, overlaying digitized layers of SAS polygons and the Survey of India basemap using the ILWIS (Integrated Land and Water Information System) software. GIS was used to prepare a composite (master) database of western and central India that showed the extent and distribution of salt affected soils. A relational database was prepared combining the digitized polygons with soil characteristics such as nature and degree of salinity (presence of higher concentration of neutral salts and neutral soil reaction), sodicity (presence of higher concentration of basic salts and alkaline reaction) and ground coverage. The regional and zonal databases of salt affected soils were prepared at a suitable scale overlaying agro-climatic regions agro-climatic zones. Spatial relation of salt affected soils with physiography, climate, geology, and agro-eco-sub-regions were evaluated employing map calculations in GIS. Saline soils were prevalent in Gujarat, and Rajasthan while sodic soils were dominant in Maharashtra and Madhya Pradesh. These were distributed primarily in the arid (B) plain of Rajasthan, alluvial (A) and coastal (D) plains of Gujarat, and peninsular plain (F) of Maharashtra and Madhya Pradesh. It occupied 2,596,942 ha (78%) in the western (Rajasthan and Gujarat) and 733,608 ha (22%) in the central (Madhya Pradesh and Maharashtra) regions. The SAS occupied 3.3 million ha in the western and central region constituting 50% of the total salt affected soils in India. The saline and sodic soils occupied 2,069,285 ha (62%) and 1,261,266 ha (38%), respectively.     

Deforestation and fragmentation of seasonal tropical forests in the southern Yucatán,Mexico (1990–2006)

The southern Yucatán (SY) has been recognized as a hotspot of biodiversity with great risk of deforestation. Land change analysis, based on classified Landsat TM and ETM?+?satellite imagery (1990, 2000 and 2006), was used to estimate the annual deforestation rates of 141 land management units of the SY, and spatial patterns of forest fragmentation around and within the Calakmul Biosphere Reserve (CBR), which comprises approximately one-third of the region. Results indicate a decrease in annual deforestation rates over 1990–2006, from 0.15% year^?1 to 0.06% year^?1, but with significant sub-regional variations in the quantity and rate of forest loss. Despite a decline in deforestation during this period, there was considerable fragmentation both inside and outside the CBR. While population pressures and the expansion of pasture have caused deforestation across the region, agricultural intensification, diversified income strategies and reserve conservation may have contributed to reduced forest loss during the study period.     

Monthly soil erosion monitoring based on remotely sensed biophysical parameters: a case study in Strymonas river basin towards a functional pan-European service

Abstract

Currently, many soil erosion studies at local, regional, national or continental scale use models based on the USLE-family approaches. Applications of these models pay little attention to seasonal changes, despite evidence in the literature which suggests that erosion risk may change rapidly according to intra-annual rainfall figures and vegetation phenology. This paper emphasises the aspect of seasonality in soil erosion mapping by using month-step rainfall erosivity data and biophysical time series data derived from remote-sensing. The latter, together with other existing pan-European geo-databases sets the basis for a functional pan-European service for soil erosion monitoring at a scale of 1:500,000. This potential service has led to the establishment of a new modelling approach (called the G2 model) based on the inheritance of USLE-family models. The G2 model proposes innovative techniques for the estimation of vegetation and protection factors. The model has been applied in a 14,500 km² study area in SE Europe covering a major part of the basin of the cross-border river, Strymonas. Model results were verified with erosion and sedimentation figures from previous research. The study confirmed that monthly erosion mapping would identify the critical months and would allow erosion figures to be linked to specific land uses.     

Uncertainty analysis of soil erosion modelling using different resolution of open-source DEMs

The Digital Elevation Model (DEM) is one of the important parameters of soil erosion assessment and notable uncertainties are found in using different resolutions of the DEM. Revised Universal Soil Loss Equation model has been applied to analyze the effect of open-source DEMs with different resolution and accuracy on the uncertainties of soil erosion modelling in a part of the Narmada river basin in Madhya Pradesh in central India. Selected open-source DEMs are GTOPO30 (1 km), SRTM (30 and 90 m), CARTOSAT (30 m) and ASTER (30 m), used for estimating erosion rate. Results with better accuracy are achieved with the high-resolution DEMs (30 m) with higher vertical accuracy than the coarse resolution DEMs with lower accuracy. This study has presented potential uncertainties introduced by the open-source DEMs in soil erosion modelling for better understanding of appropriate selection and acceptable errors for researchers.     

Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces

Surface moisture is important to link land surface temperature (LST) to people’s thermal comfort. In urban areas, the surface roughness from buildings and urban trees impacts wind speed, and consequently surface moisture. To find the role of surface roughness in surface moisture estimation, we developed methods to estimate daily and hourly evapotranspiration (ET) and soil moisture, based on a case study of Indianapolis, Indiana, USA. In order to capture the spatial and temporal variations of LST, hourly and daily LST was produced by downscaling techniques. Given the heterogeneity in urban areas, fractions of vegetation, soil, and impervious surfaces were calculated. To describe the urban morphology, surface roughness parameters were calculated from digital elevation model (DEM), digital surface model (DSM), and Terrestrial Light Detection and Ranging (LiDAR). Two source energy balance (TSEB) model was employed to generate ET, and the temperature vegetation index (TVX) method was used to calculate soil moisture. Stable hourly soil moisture fluctuated from 15% to 20%, and daily soil moisture increased due to precipitation and decreased due to seasonal temperature change. ET over soil, vegetation, and impervious surface in the urban areas yielded different patterns in response to precipitation. The surface roughness from high-rise has bigger influence on ET in central urban areas.     

用ERS风散射计数据估算土壤水分方法的研究

欧空局ERS1/2卫星上的风散射计(WSC),分辨率是50km,4天内能覆盖全球超过80%的范围,并可在多角度下对地物目标进行观测。本文研究利用该散射计数据估算土壤水分的方法。首先,利用基于ERS散射计数据建立的全球C波段雷达后向散射系数数据库,根据传统的几何光学模型(GOM),反演得到与土壤含水量密切相关的法线方向Fresnel反射率,并与两个采样点(安多和那曲)上的实测降雨量及土壤水分相对比,证明了ERS散射计数据与土壤水分的高相关性;第二步,以水云模型为基础,结合AIEM模型,发展了一种简化模型来估算土壤水分绝对值,分别利用气象站实测点数据和同时期的Basist湿度指数(BWI)进行验证,表明反演结果能较好反映土壤水分的空间分布状况。