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1.
Soil salinization is a worldwide environmental problem with severe economic and social consequences. In this paper, estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR) predictive model was carried out using QuickBird data and soil reflectance spectra. At first, a relationship between the sensitive bands of soil salinity acquired from measured reflectance spectra and the spectral coverage of seven commonly used optical sensors was analyzed. Secondly, the potentiality of QuickBird data in estimating soil salinity by analyzing the correlations between the measured reflectance spectra and reflectance spectra derived from QuickBird data and analyzing the contributions of each band of QuickBird data to soil salinity estimation Finally, a PLSR predictive model of soil salinity was developed using reflectance spectra from QuickBird data and eight spectral indices derived from QuickBird data. The results indicated that the sensitive bands covered several bands of each optical sensor and these sensors can be used for soil salinity estimation. The result of estimation model showed that an accurate prediction of soil salinity can be made based on the PLSR method (R2 = 0.992, RMSE = 0.195). The PLSR model's performance was better than that of the stepwise multiple regression (SMR) method. The results also indicated that using spectral indices such as intensity within spectral bands (Int1, Int2), soil salinity indices (SI1, SI2, SI3), the brightness index (BI), the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) as independent model variables can help to increase the accuracy of soil salinity mapping. The NDVI and RVI can help to reduce the influences of vegetation cover and soil moisture on prediction accuracy. The method developed in this paper can be applied in other arid and semi-arid areas, such as western China. 相似文献
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3.
In certain agricultural fields of Khambhat Taluka in Gujarat State, the salinity has increased considerably rendering the land completely infertile. The occurrence of salinity in this area can be attributed partly to subsurface sea‐water ingress and partly to improper land and water management practices prior to implementation of irrigation. Landsat MSS or TM and IRS IA LISS II data was used to test the feasibility of delineating saline soils by both visual image interpretation and digital analysis. The study of saline soils using multi‐temporal Landsat images of the year 1977, 1983, and 1987, indicated an evident increase in saline areas in past few years. The Soil Brightness Index (SBI) generated from the IRS‐IA data by the application of MSS equivalent coefficients brought out different categories of soil degradation. The supervised classification scheme aided in generating various salinity levels. The analysis of the soil samples of the above area exhibited increasing values of Electrical Conductivity (ECe), and the soluble cations with increasing levels of salinity. 相似文献
4.
N. K. Kalra L. Singh R. Kachhwah D. C. Joshi 《Journal of the Indian Society of Remote Sensing》2010,38(2):279-290
Soil is an integral part of ecosystem nurturing the biological system. Sustainable management of soil resources based on the
consideration of constraints is the key to check land degradation and maintain productivity of biological system. To meet
the objective remote sensing and GIS technology has been employed for identification of soil constraints in resource potential
Bhilwara district. IRS LISS-III FCC images were interpreted for soil constraints using physiography soil approach, verified
through field checking and laboratory analysis. On IRS LISS-III FCC images the salt affected soils of Kotri and Taswaria appeared
in bright white to light grey tone, smooth texture with white mottles. These were also verified during ground truth and soil
analysis for salinity (EC 2.90–3.32 dS m−1) and sodicity (pH 9.50–9.86 and ESP 17.60–19.05). Similarly on the LISS III FCC, constraints due to water erosion near Bir,
Sareri and Vijaypura soil series were apparent in light grey to whitish tone, intercepted by medium grey streaks indicating
streams and exposed sub-soil. The constraints due to shallow depth associated with rock out crops and hilly areas of Balda
and Delwara series appeared in greenish grey tone and coarse texture. There was close relationship between image characteristics,
field observation and analytical data. 相似文献
5.
综合主动和被动微波数据监测土壤水分变化 总被引：12，自引：1，他引：12
微波遥感测量土壤水分的方法主要分主动和被动两种，它们都是基于干燥土壤和水体之间介电常数的巨大差异。估算植被覆盖土壤表面土壤水分必须要考虑地表粗糙度和植被覆盖影响的问题。植被覆盖土壤表面的后向散射包括来自植被的体散射，来自地表的面散射和植被与地表间的交互作用散射项。本研究建立了一个半经验公式模型，用来计算体散射项，综合时间序列的主动和被动微波数据，消除植被覆盖的影响，估算地表土壤水分的变化状况。并应用1997年美国SGP‘97综合实验中的机载800m分辨辐射计ESTAR数据计算表面反射系数，综合Radarsat的SCAN-SAR数据得到体散射项，然后，由NOAA/AVHRR和TM计算得到的NDVI值加权分配50m分辨率的体散射项，最后计算50m分辨率的表面反射系数的变化值，从而得到土壤水分的变化情况，验证数据表明该计算结果与实测值一致。 相似文献
6.
利用光学遥感反演盐度,可以充分利用遥感数据的空间代表性,以及目前高分率遥感数据的高时空精度。本文利用MERIS(Medium Resolution Imaging Spectrometer)300 m数据,以干旱区的博斯腾湖(博湖)为例,探讨了光学遥感数据反演低盐湖泊水体盐度的可行性。结果显示:在开都河入流影响的博湖西南角,存在光学遥感反演盐度利用的黄色物质(CDOM)与盐度的反比关系,但相关性不高,而且在博湖区域不同时间、不同区域CDOM与盐度的关系都不同。博湖盐度低于3 g·L-1,而遥感数据计算盐度的精度约为1.1 psu,因而用光学遥感数据计算博湖盐度的误差太大。博湖本身CDOM与盐度关系的时空异质性以及相关性不高,目前光学遥感反演精度有限,因此,在博湖用光学遥感数据反演整个湖区的盐度有困难。用光学数据反演水体盐度要求盐度足够高,盐度和CDOM存在梯度,并满足CDOM扩散守恒,因此用光学遥感反演低盐湖泊水体盐度较为困难。 相似文献
7.
《Geoscience and Remote Sensing Letters, IEEE》2006,3(4):467-470
The vast and complex oceans that are optically opaque are acoustically transparent, enabling characterization of physical and biological bodies and processes of sea using sound as a premier tool. Lack of direct observations of vertical profiles of velocimeters and/or temperature and salinity, from which sound speed can be calculated, limits specifications and investigation of temporal and spatial variabilities of the three-dimensional structure of the sound speed in the oceans. In this study, the authors demonstrate estimation of sound speed profiles (SSPs) from surface observations using an artificial neural network (ANN) method. Surface observations from a mooring in the central Arabian Sea are used as a proxy to the satellite observations. The ANN-estimated SSPs had a root-mean-square error of 1.16 m/s and a coefficient of determination of 0.98. About 76% (93%) of the estimates lie within plusmn1 m/s (plusmn2 m/s) of the SSPs obtained from in situ temperature and salinity profiles 相似文献
8.
山区土壤含水量对山区植被生长监测、滑坡预测等工作具有重要意义,因此针对山地低矮植被区域,提出了全极化SAR图像的土壤含水量估计方法。为解决山地区域SAR图像几何形变和极化旋转问题,根据入射角、坡度、坡向信息定义了可测区域与不可测区域,并对可测区域后向散射系数进行校正。其次以密西根模型为基础,发展了低矮植被的散射模型。在假定植被和土壤特征不变的情况下,基于此散射模型并结合校正数据建立了山区土壤含水量反演方法。结果表明,模型反演的土壤含水量和实验点实测值基本一致,两个实验点反演值分别为14%和15%,实测值为11.45%和15.80%,能够满足一般应用的需求。 相似文献
9.
B. M. Bogachkin Yu. V. Nechayev Ye. A. Rogazhin B. N. Khovanskiy P. N. Yazev 《地理信息系统科学与遥感》2013,50(2):171-184
Soil salinity is one of the most important problems affecting Egyptian soils. It is caused by: (1) a rising water table, or (2) the misuse of the irrigation water. Two Landsat images acquired in 1987 and 1999 were used to detect and monitor soil salinity over the Siwa Oasis, Western Desert, Egypt. DN values of these images were converted to percent reflectance. Inspection of Landsat images revealed that saline soils had an overall higher spectral reflectance in all spectral bands except the two MIR bands. The reflectance curves of saline soils show a strong relationship between the existence of salts in the soil and the difference between bands 4 and 5. A salinity index (SI) was calculated for both images. The majority of pixels in the 1987 image have salinity index values ranging between 0 and 0.2, whereas the values in the 1999 image histogram ranged between 0 and 0.4. These values indicate that soil salinity has increased twofold during the 12 years spanning the imagery. These values show a strong correlation with vegetation index images, in which the 1999 vegetation index image reveals the appearance of surface water lakes formed due to a rising water table. This study presents a model for the identification of soil salinity using remote sensing measurements in conjunction with piezometer readings taken during the time of image acquisition. 相似文献
10.
Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover. 相似文献
11.
Shivani Chauhan Mukta Sharma M.K. Arora N.K. Gupta 《International Journal of Applied Earth Observation and Geoinformation》2010
In the present study, Artificial Neural Network (ANN) has been implemented to derive ratings of categories of causative factors, which are then integrated to produce a landslide susceptibility zonation map in an objective manner. The results have been evaluated with an ANN based black box approach for Landslide Susceptibility Zonation (LSZ) proposed earlier by the authors. Seven causative factors, namely, slope, slope aspect, relative relief, lithology, structural features (e.g., thrusts and faults), landuse landcover, and drainage density, were placed in 42 categories for which ratings were determined. The results indicate that LSZ map based on ratings derived from ANN performs exceedingly better than that produced from the earlier ANN based approach. The landslide density analysis clearly showed that susceptibility zones were in close agreement with actual landslide areas in the field. 相似文献
12.
地表土壤水分含量的时空分布信息是十分重要的,常常作为水文模型、气候模型、生态模型的输入参数,同时,也是干旱预报、农作物估产等工作的重要指标。被动微波遥感是监测土壤含水量最有效的手段之一。相比红外与可见光,它具有波长长,穿透能力强的优势。相比主动微波雷达,被动微波辐射计具有监测面积大、周期短,受粗糙度影响小,对土壤水分更为敏感,算法更为成熟的优势。目前,已研究出许多反演土壤水分的方法.本课题的主要内容是借助AMSR-E土壤水分影像数据、MODIS归一化植被指数(NDVI)影像数据和MODIS分类影像数据,利用ENVI软件进行遥感图像数据处理,运用统计分析方法建立NDVI与土壤水分的经验模型,研究中国西部地区稀疏植被覆盖区土壤水分的反演。 相似文献
13.
V. I. Kharuk K. G. Ranson T. A. Burenina A. A. Onuchin Ye. V. Fedotova 《地理信息系统科学与遥感》2013,50(3):172-179
A Russian-American team of remote sensing researchers investigates the applications of active microwave (synthetic-aperture radar) imagery for estimation of water reserves in snowpack in the Western Sayan Range of East Siberia. Considerable attention is devoted to an explanation of the study's principal finding—that a fundamentally different statistical relationship exists between strength of the reflected radar signal and snow cover depth in open areas versus forested areas with high levels of canopy closure. Translated by Edward Torrey, Alexandria, VA from: Geografiya i prirodnyye resursy, 1999, No. 4, pp. 85-90. 相似文献
14.
含水含盐土壤的微波介电特性分析研究 总被引：13，自引：0，他引：13
用微波网络分析仪测量了实验室制备的各种不同含水量,含盐量的土壤样品的复介电常数,研究了介电常数的实部和虚部与频率、盐度、含水量的关系。研究表明:频率、盐度对土壤介电常数实部的影响很小;对于某一特定土壤,其介电常数的实部由土壤的含水量决定;在较低频率范围内（f<2GHz）,虚部随着频率的增大而迅速下降,高频部分则趋向于一定值,波长较长的波段,如P波段或L波段对土壤含盐程度具有更高的敏感性,含盐量对虚部在较低频范围（f<5GHz）影响很大。同时,采集了内蒙古吉兰泰盐湖区的土壤样品,并测量了其复介电常数,与同步过顶的RADARSAT图像进行了相关分析。分析结果表明雷达图像记录的后向散射强度与含盐土壤复介电常数实部的相关系数为0.23,与虚部的相关系数为0.66,即雷达图像观测的含盐含水士壤的后向散射强度与土壤的含盐量相关性较高。这为利用微波遥感进行土壤盐碱化程度监测,提供了可能和实验依据。 相似文献
15.
基于神经网络的土地荒漠化信息提取方法研究 总被引：14，自引：0，他引：14
土地荒漠化是当今全球面临的重大环境问题之一,它的发生、发展及其逆转是气候、环境和人类社会经济活动综合作用的结果.区域荒漠化信息的提取技术研究是荒漠化研究进一步深入的关键,根据土地荒漠化的遥感探测机理,应用神经网络技术,利用了TM卫星遥感数据中的可见光、热红外和植被指数(NDVI)数据,建立了相应的BP神经网络的土地荒漠化信息的自动提取模型.实验应用表明,基于人工神经网络方法提取土地荒漠化发生的地点和范围等信息,其精度可达到84%.因此,应用人工神经网络方法提取土地荒漠化信息是切实可行的,并具有可推广价值. 相似文献
16.
In this study, the NIR-red spectral space of Landsat-8 images, which is manifested by a triangle shape, is deployed for developing two new Soil Moisture (SM) indices. First, ten parameters consisting of six distances and four angles were extracted using the position of a random pixel in this triangle. Then, some correlation assessments were made to derive those parameters that were useful for SM estimation, which were five parameters. To build a soil moisture index, all combinations of these five parameters, which were in total 31 different regression equations, were considered, and the best model was named the Triangle Soil Moisture Index (TSMI). The TSMI consists of three parameters. It showed a RMSE of 0.08 and correlation coefficient (R) of 0.67. Since the TSMI does not consider vegetation interface in SM estimation, the Modified TSMI (MTSMI), which takes into account the fraction of soil cover in each pixel, beside those parameters which were used in the TSMI, was developed (MTSMI: RMSE = 0.07, R = 0.74). The results of the TSMI and MTSMI were compared with each other, and with another soil moisture index (SMMRS introduced by Zhan et al. (2007)). It was concluded that the TSMI and MTSMI provide similar results for bare soil or sparsely vegetated surfaces. However, the MTSMI demonstrated a much better performance in densely vegetated surfaces. The accuracy of both the TSMI and MTSMI were significantly higher than the SMMRS. Moreover, the TSMI and MTSMI were validated by comparison with field measured SM data at five different depths. The results showed that satellite estimated SM by these two indices was more correlated with in situ data at 5 cm soil depth compared to other depths. Also, to show the high applicability of the proposed approach for SM estimation, we selected another set of field SM data collected in Australia. The results proved the effectiveness of the method in different study areas. 相似文献
17.
传统光学遥感技术手段在森林覆盖区难以准确获取林下地形,原因在于其只能测量森林冠层顶部高程。微波信号能够穿透森林冠层并记录森林垂直结构信息,为解决林下地形测绘难题带来了契机,如何准确获取林下地形已成为微波遥感领域的研究热点。首先介绍了面向林下地形测绘的合成孔径雷达（synthetic aperture radar,SAR）干涉测高原理及数据获取手段。然后对利用SAR进行林下地形测绘的方法进行了分类,主要包括基于合成孔径雷达干涉测量（interferometric synthetic aperture radar,InSAR）、极化合成孔径雷达干涉测量（polarimetric InSAR,PolInSAR）及基于多基线InSAR/PolInSAR数据的层析SAR（tomographic SAR,TomoSAR）技术的林下地形测绘方法,并介绍了上述3种方法的应用进展。最后在此基础上,从数据获取、误差改正及散射模型构建3个角度分析了林下地形测绘所面临的问题。 相似文献
18.
土壤水分不仅在地表水、能量以及碳循环中发挥着非常重要的作用,其时空变化也是影响和反映气候变化的关键因子。虽然被动微波遥感技术是目前监测大尺度范围土壤水分变化最为成熟的技术手段,但是其土壤水分产品空间分辨率往往较低（几十千米不等）,不能满足区域和局地尺度的应用需求。鉴于这一问题,空间降尺度逐渐成为了提高被动微波土壤水分遥感产品空间分辨率的主要方式,也是当前遥感研究领域的热点之一。本文总结与分析了近20多年来国内外被动微波土壤水分遥感产品空间降尺度研究进展,系统归纳了经验性、半经验性和基于物理机理的3大类降尺度方法,并就各方法特征进行了详细说明,概述了各方法的优势和缺点。归纳而言,虽然被动微波土壤水分遥感产品空间降尺度方法众多,但可靠的高分辨率降尺度土壤水分产品仍较少,这与被动微波土壤水分遥感产品、降尺度关系模型方法以及降尺度辅助因子等有着直接的关联。未来相关研究应重点结合多源遥感数据建立适用性强、精度高的降尺度关系模型,进而实现时空无缝的高时空分辨率降尺度土壤水分产品的生产,为推动土壤水分遥感产品在农林业管理、自然灾害监测、水文过程分析等区域应用中提供支持。 相似文献
19.
森林高度是反映森林资源数量和质量的重要参数,极化干涉合成孔径雷达PolInSAR （Polarimetric Synthetic Aperture Radar Interferometry）技术在森林高度反演中极具潜力。由于森林散射特征受波长影响明显,由此引起的散射机理差异使得基于PolInSAR技术反演的森林高度结果具有很大的不确定性。为了定量化该不确定性的影响,本文以模拟森林场景为例,对PolInSAR技术森林高度反演中常用的4种方法——极化相位中心高度估测法、复相干相位中心差分法、复相干幅度反演法以及相干幅度、相位联合反演法,以及它们在常用的4个微波波段P、L、C和X中的森林高度估测结果进行了分析;明确了匀质森林场景中,算法、波段选择引起的森林高度估测结果的不确定性。研究结果表明：在森林场景基本一致的情况下,估测算法的选择直接影响森林高度估测结果,其中复相干幅度反演法在4个波段的估测结果中精度均最高,但各估测点的估测结果离散度及不确定度较大。波长对4类估测方法估测结果的影响差异明显：复相干幅度反演法的反演结果几乎不受波长的影响,而相干幅度、相位联合反演法受波长影响明显,在P和L波段反演结果中精度较高,在C和X波段反演结果中精度降低明显。此外,以传统的交叉极化（HV）相位代表冠层散射相位中心,水平同极化与垂直同极化的相位差（HH-VV）代表地表散射相位中心,采用复相干相位中心差分法进行森林高度估测会出现严重低估现象。估测结果不确定度具有波长和算法选择依赖性,在C和X波段采用复相干相位中心差分法估测结果不确定度最低,在P和L波段采用极化相位中心高度估测法估测结果不确定度最低,而复相干幅度反演法估测结果则在多个波段中的不确定度均最高。 相似文献
20.
Pramila Raina Mahesh Kumar Mohar Singh 《Journal of the Indian Society of Remote Sensing》2009,37(4):647-657
In the present study, efforts have been made to identify and map areas affected by various soil degradation processes in Hanumangarh
district of western Rajasthan. Soil degradation processes were identified by using IRS-1B satellite image of the year 1998,
SOI toposheets, ground truth verification and soil studies. The kind, extent and degree of soil degradation have been mapped
in an area of 9703 km2. The study reveals that the soil degradation problems were mainly due to wind erosion/deposition and water-logging, followed
by salinity/alkalinity, water erosion and wind and water erosion combinedly. Nearly 38.7% area is subjected to slight and
moderate degradation, which can easily be combated by adopting the suggested techniques and 17.1% area is free of hazard.
Soil degradation processes have resulted in the loss of organic carbon, available Phosphorus and Potassium. Soil degradation
due to water logging/salinization has also shown a significant increase in electrical conductivity and available potassium
content of soil. 相似文献