滨海盐渍土可见近红外高光谱特征

土壤光谱是遥感监测的物理基础,盐渍化土壤光谱特征研究对于盐渍化土地的监测有着重要的意义。以黄河三角洲地区的滨海盐渍土为研究对象,通过野外土壤样品采集和高光谱测量,研究在去除水分以及剔除土壤质地影响后,不同盐渍化程度的滨海盐渍土在350~1 100 nm区间的高光谱反射和吸收特征,并且试图构建光谱预测模型。结果表明: 平滑后的光谱曲线能更准确有效地描述光谱的反射特征及吸收峰。不同盐化程度的土壤光谱曲线形态一致,但反射率大小差异较大。连续统去除后,490 nm处轻度盐化土吸收最小,在760~920 nm区间重度盐化土的吸收更强烈。原始光谱不能预测土壤盐渍化信息,但是二阶微分变换能够提高波段敏感性,建立的光谱预测模型能够基本满足预测要求。     

滨海盐渍土可见近红外高光谱特征

土壤光谱是遥感监测的物理基础,盐渍化土壤光谱特征研究对于盐渍化土地的监测有着重要的意义。以黄河三角洲地区的滨海盐渍土为研究对象,通过野外土壤样品采集和高光谱测量,研究在去除水分以及剔除土壤质地影响后,不同盐渍化程度的滨海盐渍土在350～1 100 nm区间的高光谱反射和吸收特征,并且试图构建光谱预测模型。结果表明:平滑后的光谱曲线能更准确有效地描述光谱的反射特征及吸收峰。不同盐化程度的土壤光谱曲线形态一致,但反射率大小差异较大。连续统去除后,490 nm处轻度盐化土吸收最小,在760～920 nm区间重度盐化土的吸收更强烈。原始光谱不能预测土壤盐渍化信息,但是二阶微分变换能够提高波段敏感性,建立的光谱预测模型能够基本满足预测要求。     

GA-PLS方法提取土壤水盐光谱特征的精度分析

光谱定量遥感已成为土壤盐渍化大尺度调查的有效手段之一,但黄河三角洲地区盐渍化土壤的光谱响应特征尚未明确。以黄河三角洲野外测定土壤体积含水率、电导率为例,应用遗传偏最小二乘法(GA-PLS)在小样本集条件下提取盐渍土壤的水分-盐分的光谱响应特征,利用蒙特卡罗方法随机模拟结果表明:在不同土壤水盐含量条件下,GA-PLS方法所提取的光谱特征具有鲁棒性,含水率模型稳定在23个波段变量,即响应特征为365~425,500~515,720~740,755~765与955~965 nm;土壤电导率模型的特征集数目为20个波段变量,特征为370~385,405~425\,500~535,650~660,755~760与1 030~1 050 nm。实验在不同预处理模型下,GA-PLS算法所建立水盐光谱模型较PLSR模型均显示出更高的精度。其中,包络线预处理方法与GA-PLS算法相结合效果最优,其水分光谱模型测试集拟合精度(R²),预测残差平方和(PRSS)与残差预测方差(RPD)分别为0.88,9.36与15.80;土壤光谱模型测试集精度R²,PRSS与RPD分别为0.71,15.68与13.76。     

基于遥感和电磁感应的盐渍土识别分级——以河南封丘为例

以河南封丘TM影像及田间电磁感应调查为基础研究盐渍土的识别分级。通过分析遥感影像的光谱特征,得出几大地类的光谱亮度值和光谱指数差异;同时,利用电磁感应调查结果解译出土壤电导率作为辅助信息,用以辅助盐渍土的提取和分级,最后建立地类识别及盐渍土分级的标准进行决策树分类。研究结果表明,综合运用遥感和电磁感应技术,不仅能够有效的提取水体、城镇等非农用地,而且能够比较准确的识别农用地中的盐渍化土壤,尤其是中轻程度的盐渍化土壤,这对于有效评价该地区土壤盐渍化现状及指导农业生产具有积极意义。     

高光谱植被遥感数据光谱特征分析

利用植被的光谱数据,探讨了植被冠层的光谱反射特征和诊断性光谱吸收特征。根据植被光谱特征和连续统去除法(CR),介绍了识别植被种类和预测植被冠层营养元素等生化组分含量的可能性。运用一阶微分反射比(FDR)和从连续统去除的光谱吸收特征中获得的波段深度(BD)、连续统去除后微分反射比(CRDR)、波段深度比(BDR)和归一化波段深度指数(NBDI)等变量,利用逐步线性回归模型并基于光谱吸收特征的变量来选择波长,并通过相关分析来预测植被冠层生化组分。     

松嫩平原大安古河道强度盐渍土种稻脱盐试验

松嫩平原中、西部地区土壤盐渍化非常严重,盐渍土改良对于土地资源利用和生态环境改善具有重大意义。在大安古河道苏打草甸盐化土、碱化盐土和草甸碱土复区选择试验田,通过种植水稻,借助周期性的灌水、排水过程,溶洗土壤中的盐分,达到改良盐渍土的目的。经过连续4年的试验,土壤平均含盐量由试验前的0.45%降至第4个试验年的0.15%。从第1试验年到第4试验年,水稻产量由绝收逐渐增至4250 kg hm-2。试验结果表明,充分利用地表水资源,通过种稻改良强度盐渍土是可行的,同时也可为松嫩平原中、西部更大范围利用强度盐渍土壤资源、恢复生态环境提供科学指导。     

基于特征空间的黄河三角洲垦利县土壤盐分遥感提取

土壤盐渍化是实现土地资源可持续利用所面临的重要挑战,在我国滨海的黄河三角洲区域遥感定量反演适宜方法可为区域盐渍化监测与防治提供技术方法参考。研究以Landsat 8 OLI数据和野外实测数据为基础,提取关键地表特征参量,定量化探讨土壤盐分与地表生物物理参数之间的规律及关系,建立黄河三角洲土壤盐分最优反演模型。结果表明:Albedo-MSAVI、SI-Albedo、SI-NDVI反演精度分别为83.4%、88.8%和80.6%。分析认为SI-Albedo模型最适用于滨海地区盐渍化程度反演,对滨海地区土壤盐分的预测能力较强;Albedo-MSAVI、SI-NDVI模型对内陆干旱、半干旱地区的盐渍化信息提取具有一定的参考意义。基于精度最高的SI-Albedo所反演的结果来看,垦利县盐渍化程度自东向西总体呈高低高走向,与该区域盐分积聚的成因机理相符。     

滏阳河两岸农田土壤Fe、Zn、Se元素光谱响应研究

为了探索遥感技术快速定量化监测土壤元素含量的可行性，本通过对滏阳河两岸农田51个土壤表层样品的室内光谱反射率及其Fe、Zn、Se含量关系的研究，探索了反射光谱快速预测土壤元素含量的技术途径。结果发现预测Fe的最佳光谱间隔为16nm。Zn和Se的为8nm，这说明在使用经验方法预测没有光谱特征的成分时，光谱分辨率不是一个必要条件；土壤中的Fe、Zn、Se元素与土壤的反射光谱存在较好的相关性，各元素含量与土壤平均反射率负复相关系数(R^2)均可达到0．49以上，而与相应TM各波段的平均光谱反射率也都具有较好的负相关关系，与TM7波段的复相关系数最大，Fe、Zn为0．58，Se元素为0．550本研究结果为今后利用高光谱遥感技术定量监测土壤Fe、Zn、Se元素含量提供了一种新的方法和技术途径，对土地质量变化的快速定量监测具有重要的科学意义和应用前景。     

典型煤系岩石的可见-近红外光谱特征研究

为研究基于可见-近红外光谱技术的煤岩识别方法,从山西、山东4个煤矿收集了页岩、砂岩、灰岩三大类11种典型煤系岩石,测定了其可见-近红外波段(400～2 450nm)的反射光谱,分析了其矿物、元素组成对反射光谱特征的影响,获得了碳质物质含量对煤系页岩反射光谱曲线特征参数的影响规律。研究结果表明:①绝大多数煤系岩石的反射光谱曲线在可见光波段(400～780nm)和短波近红外波段(780～1 100nm)呈现出随波长增加的多重吸收谷。在长波近红外波段(1 100～2 450nm),明显的吸收谷主要集中在1 400,1 900,2 200,2 350nm波长,页岩、灰岩吸收谷的波长相对固定,而不同砂岩吸收谷的波长呈现出多种变化。②除碳质物质含量较高的碳质页岩外,同一煤矿各类煤系岩石与煤的可见-近红外波段反射光谱吸收特征差异明显。③当煤系页岩中碳质物质含量增大时,可见-近红外波段反射光谱曲线的光谱斜率和各明显吸收谷深度均呈先快速减小后趋于平缓的特点。     

东北黑土区土壤铬含量高光谱反演研究

以东北黑土区某农场为研究区,在光谱积分定义的基础上,提出一种新的光谱特征参数——反向光谱吸收积分,建立偏最小二乘回归模型对土壤中铬元素含量进行反演研究。与传统的光谱特征参量,包括微分变换、倒数变换、对数变换等11种光谱变换以及吸收面积建立的土壤铬含量高光谱反演模型进行对比分析,结果表明:在光谱变换特征中,平方根一阶微分模型能够较好地定量预测铬含量;吸收面积模型稳定性略差,只能对样本进行初略估计;针对反向光谱吸收积分模型,其建模样本的调整决定系数为0.73,均方根误差为2.63 mg/kg;验证样本的调整决定系数为0.77,均方根误差为2.36 mg/kg,相对偏差为3.21,表明此模型具有极好的预测能力。因此,反向光谱吸收积分能够明显改善铬含量反演模型的精度和稳定性,为土壤铬污染监测提供了新的思路。     

Comparing Sentinel-2 MSI and Landsat 8 OLI in soil salinity detection: a case study of agricultural lands in coastal North Carolina

This study presents the first comparison of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) in identifying soil salinity using soil physiochemical, spectral, statistical, and image analysis techniques. By the end of the century, intermediate sea level rise scenarios project approximately 1.3 meters of sea level rise along the coast of the southeastern United States. One of the most vulnerable areas is Hyde County, North Carolina, where 1140 km² of agricultural lands are being salinized, endangering 4,200 people and $40 million USD of property. To determine the best multispectral sensor to map the extent of salinization, this study compared the feasibility of OLI and MSI to estimate electrical conductivity (EC). The EC of field samples were correlated with handheld spectrometer spectra resampled into multispectral sensor bands. Using an iterative ordinary least squares regression, it was found that EC was sensitive to OLI bands 2 (452 nm – 512 nm) and 4 (636 nm – 673 nm) and MSI bands 2 (457.5 nm – 522.5 nm) and 4 (650 nm – 680 nm). Respectively, the R²_Adj and Root Mean Square Error (RMSE) of 0.04–0.54 and 1.15 for OLI, and 0.05–0.67 and 1.17 for MSI, suggests that the two sensors have similar salinity modelling skill. The extracted saline soils make up approximately 1,703 hectares for OLI and 118 hectares for MSI, indicating overestimation from the OLI image due to its coarser spatial resolution. Additionally, field samples indicate that nearby vegetated land is saline, indicating an underestimation of total impacted land. As sea levels rise, accurately monitoring soil salinization will be critical to protecting coastal agricultural lands. MSI’s spatial and temporal resolution makes it superior to OLI for salinity tracking though they have roughly equivalent spectral resolutions. This study demonstrates that visible spectral bands are sensitive to soil salinity with the Blue and Red spectral ranges producing the highest model accuracy; however, the low accuracies for both sensors indicate the need of narrowband sensors. The HyspIRI to be launched in the early 2020s by NASA may provide ideal data source in soil salinity studies.     

Analysis of a small agricultural watershed using remote sensing techniques

Salinization of land and sweet water is an increasing problem worldwide. In the Carpathian Basin, particularly in arid and semi‐arid regions, irrigation is a contributing factor to the secondary salinization problems, one of the major problems affecting soils in Hungary. Conventional broadband sensors such as SPOT, Landsat MSS, and Landsat ETM+ are not suitable for mapping soil properties, because their bandwidth of 100–200 mm cannot resolve diagnostic spectral features of terrestrial materials. Analytical techniques, developed for analysis of broadband spectral data, are incapable of taking advantage of the full range of information present in hyperspectral remote sensing imagery. In our pilot project in Tedej farm in the Great Plain Region, Hungary, the DAIS sensor was used to assess salinity risk, covering the spectral range from the visible to the thermal infrared wavelengths at 5 m spatial resolution, and other major indicators of soil salinization (NDVI, SAVI, canopy cover) were quantified with advanced remote sensing techniques using the TETRACAM ADC agricultural multispectral camera which offers red/green and NIR imaging at megapixel resolution. As a result, prominent absorption bands around 1450 nm and 1950 nm wavelength in most soil spectra are attributed to water and hydroxyl ions. Occasional weaker absorption bands caused by water also occur at 970, 1200, and 1700 nm. Absorption features near the 400 nm wavelength for all samples are also noticeable. Absorption bands at 1800 and 2300 nm are attributed to gypsum, while strong absorption features near 2350 nm are assigned to calcite (CaCo₃). Saline soils exhibited significantly higher reflectance values all throughout the 325–2500 nm wavelengths of the spectrum. Soils with a high amount of soluble salts gave a higher average reflectance than soils with a low salt content. In the project, an ADC camera‐based real‐time integrated system was developed to take advantage of more specialized spectral information and to provide even more accurate and useful data directly from the field. The results revealed that the NDVI and SAVI index and the canopy cover mapping taken with multispectral cameras can be useful as an indirect marker and help for detecting salinization. However, we did not find a strong correlation between NDVI and soil salinity. This is probably because the detection and assessment of lower levels of salinity are difficult, mainly owing to the nature of the remotely sensed images; with such images, it is not possible to obtain information on the third dimension of the 3‐D soil body. Also, the impact of salinity on electromagnetic properties needs to be explored further to understand how it can be derived indirectly from remotely sensed information. With the rapid validation of remotely sensed hyperspectral data, the decision in the future, with the best trade‐off between irrigation and sustainable land use made by agricultural specialists in this region, can be more environmentally sound and more accurate using the results from the pilot.     

海岸带高光谱遥感与近海高光谱成像仪(HICO)

应海岸带监测需求,高光谱成像仪开始在海岸带监测中发挥重要作用。搭载于国际空间站上的HICO(Hyperspectral Imager for the Coastal Ocean)是第一颗针对近岸海洋遥感的高光谱成像仪,其波谱范围为360～1 080 nm,光谱分辨率为5 nm。介绍了HICO数据的基本情况,并与在轨星载高光谱成像仪EO-1 Hyperion和HJ-1A HSI基本参数做了对比。同时针对高浑浊水体,以黄河三角洲近岸3种典型地物为例,结合FLAASH大气校正模型,提取了辐亮度和地表反射率,初步对比分析了HICO和HSI的光谱性能。结果表明HICO能更好地反映近岸地物的光谱特征。     

Generating soil electrical conductivity maps at regional level by integrating measurements on the ground and remote sensing data

In arid and semi‐arid areas, salinization of soil and water resources is one of the major threats to irrigated agriculture. For management purposes, quantifying both the extent and distribution of salinization is important, but accurate data with sufficient spatial resolution are often not available. Commonly used techniques such as soil sampling and geophysical methods are time‐consuming and yield only point data. A method is described in which multispectral remote sensing images can be used to regionalize point data measured on the field. Field data consist of measurements of electrical conductivity and are obtained by the combination of geophysical methods and the analysis of field soil samples. Uncalibrated salinity maps were calculated with spectral correlation mapping using image‐based reference spectra of saline areas. As an alternative indicator for soil salinity, the NDVI was used. The method was verified in the Yanqi Basin, northwestern China. Correlations between field data and the uncalibrated salinity maps were found over non‐irrigated sites for all images. Good correlations (R ² up to 0.85) resulted for images collected during the winter months. The high correlation coefficients allow the uncalibrated salinity maps to be scaled to electrical conductivity maps.     

Similarity measures for spectral discrimination of salt‐affected soils

This paper illustrates a pilot study designed to examine the spectral response of soils due to salt variations. The aim of the study includes determining whether salt‐affected soils can be discriminated based on their spectral characteristics, by establishing a relationship between soil properties and soil spectra and by testing if variations in the spectra of salt‐affected soil samples are statistically significant. To answer the research questions, a laboratory experiment was designed to simulate salt transport to a column of soil in order to provide direct measurements of soil spectra and soil properties when salt concentration in a soil sample changes. The measured spectra were examined by the application of spectral matching techniques to quantify the variations and ascertain a relationship that supports the spectral identification of saline soils. The Ward's grouping method was conducted as an exploratory tool to statistically create homogeneous classes among data, which were obtained from the application of the spectral matching techniques to salt affected soil spectra. A nonparametric statistical test (Mann–Whitney U‐test) was used to determine whether the differences between the classes are statistically significant. The results of spectral matching techniques showed differences in absorption strength, absolute reflectance and spectral angle in the near and shortwave infrared regions. The results also showed significant correlations between soil electrical conductivity (EC) and spectral similarity measures, indicating that similarity between the samples' spectra decreases as the salt concentration in the soil increases. The generated clusters indicate two classes at the highest level, which were subdivided at the next level and further subdivided into multiple subclasses as the dissimilarity decreased. The spectral data were grouped into classes and were used to test the null hypothesis by applying the Mann–Whitney U‐test. The results indicate a significance level of α<0.02 between salinity classes and α<0.05 per waveband, meaning variations between the classes are higher than within each class.     

CBERS影像数据在土壤盐渍化评价中的应用——以焉耆盆地为例

采用CBERS遥感影像数据,对焉耆盆地不同类型典型地物进行光谱分析。根据不同地物在不同波段的光谱特征曲线,分别选取第一、第三波段与BI指数合成并进行监督分类。结合土样分析结果,评价了焉耆盆地内土壤盐渍化程度。结果表明,盐渍土主要分布于开都河沿岸和博斯腾湖北岸地区,其中30%为轻度盐渍地,9.1%为中度盐渍地,5.5%为重度盐渍地。     

基于高光谱的土壤重金属铜的反演研究

为探讨高光谱遥感反演红壤重金属铜含量的可行性,研究采集了34个红壤性土壤样品,通过对350~2 500 nm波段范围光谱曲线进行测试和分析,建立了不同的土壤光谱变量与重金属铜含量多元回归关系模型,分析了土壤重金属铜与土壤化学组分以及土壤特征光谱的关系。结果表明,土壤重金属铜含量与土壤全铁和镁含量显著相关,而与土壤有机质的相关性不显著,表明红壤性土壤粘土矿物对土壤铜含量影响较大;与重金属铜含量相关性较好的波段在830 nm、1 000 nm和2 250 nm附近,且一阶微分模型精度(79%)高于反射率模型(66.26%)和倒数对数模型(67%)的精度。因此,一阶微分高光谱反演模型具有较好的快速估算土壤中重金属铜含量的潜力。     

Remote Sensing Extraction of Soil Salinity in Yellow River Delta Kenli County based on Feature Space

Soil salinization is an important challenge to achieve sustainable use of land resources. The appropriate method for remote sensing quantitative inversion in the coastal Yellow River Delta region of China can provide technical reference for regional salinization monitoring and prevention. Utilizing Landsat 8 OLI image and field measured data, we extracted key surface characteristic parameters, quantitatively discussed the law and relationship between soil salinity and surface biophysical parameters and established a soil salinity inversion model. The results show that the inversion precisions of Albedo-MSAVI, SI-Albedo and SI-NDVI feature space are 83.4%, 88.8% and 80.6% respectively. The analysis shows the SI-Albedo model is suitable for the inversion of salinization level in Binhai areas. For Albedo-MSAVI and SI-NDVI models, they have certain reference significance for salinization information extraction in inland arid and semi-arid areas. Based on the inversion of the SI-Albedo feature space with the highest accuracy, the level of salinization in Kenli County is generally high-low-high trends from the east to the west, which is consistent with the formation mechanism of salt accumulation in this area.