Examining the impacts of urbanization on surface radiation using Landsat imagery

Metropolitan Beijing is facing many environmental problems such as haze and urban heat island due to the rapid urbanization. Surface shortwave, longwave, and net radiations are key components of the surface-atmosphere radiation budget. Since megacities are affected by the thermal radiation of complex landscape structures and atmospheric environments, quantitative and spatially explicit retrieval from remotely sensed data remains a challenge. We collected the surface radiation fluxes from seven fixed sites representing different land-use types to calibrate the local parameters for remotely sensed retrieval of net radiation. We proposed a remote sensing–based surface radiation retrieval method by embedding the underlying land covers and integrating the observational data. The improved method is feasible to accurately retrieve surface radiation and delineate spatial characteristics in metropolitan areas. The accuracy evaluation indicated that the difference between remotely sensed and in situ observed net radiation ranged within 0~± 40 W· m^?2. The root mean squared error of the estimated net surface radiation was 32.71 W· m^?2. The strongly spatial heterogeneity of surface radiation components in metropolitan Beijing was closely related to land-cover patterns from urban area to outskirts. We also found that the surface net radiation had a decreasing trend from 1984 to 2014, and the net radiation in the urban area was lower than that in the outskirts. According to the surface radiation budgets, urbanization resulted in the cooling effect in net radiation flux in the daytime, which was stemmed from low atmospheric transmittances from massive aerosol concentration and high surface albedo from light building materials.     

Upscaling plot-scale soil respiration in winter wheat and summer maize rotation croplands in Julu County,North China

Soil respiration (R_s) data from 45 plots were used to estimate the spatial patterns of R_s during the peak growing seasons of winter wheat and summer maize in Julu County, North China, by combining satellite remote sensing data, field-measured data, and a support vector regression (SVR) model. The observed R_s values were well reproduced by the model at the plot scale, with a root-mean-square error (RMSE) of 0.31 μmol CO₂ m⁻² s⁻¹ and a coefficient of determination (R²) of 0.73. No significant difference was detected between the prediction accuracy of the SVR model for winter wheat and summer maize. With forcing from satellite remote sensing data and gridded soil property data, we used the SVR model to predict the spatial distributions of R_s during the peak growing seasons of winter wheat and summer maize rotation croplands in Julu County. The SVR model captured the spatial variations of R_s at the county scale. The satellite-derived enhanced vegetation index was found to be the most important input used to predict R_s. Removal of this variable caused an RMSE increase from 0.31 μmol CO₂ m⁻² s⁻¹ to 0.42 μmol CO₂ m⁻² s⁻¹. Soil properties such as soil organic carbon (SOC) content and soil bulk density (SBD) were the second most important factors. Their removal led to an RMSE increase from 0.31 μmol CO₂ m⁻² s⁻¹ to 0.37 μmol CO₂ m⁻² s⁻¹. The SVR model performed better than multiple regression in predicting spatial variations of R_s in winter wheat and summer maize rotation croplands, as shown by the comparison of the R² and RMSE values of the two algorithms. The spatial patterns of R_s are better captured using the SVR model than performing multiple regression, particularly for the relatively high and relatively low R_s values at the center and northeast study areas. Therefore, SVR shows promise for predicting spatial variations of R_s values on the basis of remotely sensed data and gridded soil property data at the county scale.     

面向农作物监测的遥感信息处理技术研究

以区域性主要农作物种类识别、长势分析与产量估算及农业种植结构现状监测为主要研究对象,开展适于农业管理部门业务化运行的卫星遥感信息处理的关键技术研究。从分析主要作物类型识别的遥感物理依据入手,提出了卫星遥感数据处理及专题信息提取的基本技术框架、主要农作物类型及种植面积信息的提取方法以及主要粮食作物长势分析和产量估算模型,并对结果进行了简要的精度分析。     

Thermal infrared imaging of the temporal variability in stomatal conductance for fruit trees

Repeated measurements using thermal infrared remote sensing were used to characterize the change in canopy temperature over time and factors that influenced this change on ‘Conference’ pear trees (Pyrus communis L.). Three different types of sensors were used, a leaf porometer to measure leaf stomatal conductance, a thermal infrared camera to measure the canopy temperature and a meteorological sensor to measure weather variables. Stomatal conductance of water stressed pear was significantly lower than in the control group 9 days after stress began. This decrease in stomatal conductance reduced transpiration, reducing evaporative cooling that increased canopy temperature. Using thermal infrared imaging with wavelengths between 7.5 and13 μm, the first significant difference was measured 18 days after stress began. A second order derivative described the average rate of change of the difference between the stress treatment and control group. The average rate of change for stomatal conductance was 0.06 (mmol m⁻² s⁻¹) and for canopy temperature was −0.04 (°C) with respect to days. Thermal infrared remote sensing and data analysis presented in this study demonstrated that the differences in canopy temperatures between the water stress and control treatment due to stomata regulation can be validated.     

动态综合层次判别分析方法──以水土流失强度综合分析为例

随着遥感技术的发展,可获取的遥感信息越来越丰富,各种分类算法也层出不穷,然而在分类精度上却始终无法突破遥感数据本身的局限,而且不同的方法在判识的结果上往往有很大的不同。本文认为不同遥感信息、不同的判别分析方法,对不同的对象在不同的地表组合条件下其判别的能力是不同的,只有综合地运用这些不同的方法才可能在现有遥感资料的能力下提高分类精度。本文提出一种动态综合层次判别分析算法可将多种并行的分类算法综合在一起,取长补短,提高最后分类精度。通过在浙江省新昌县某地进行水土流失强度分析试验,取得了良好的效果。     

卫星遥感数据的像元地面反射率反演计算

本文探讨了一种在卫星遥感数据地面辐射改正的基础上,利用少量离散分布的观测数据逐点的计算研究区域卫星像片像元地面反射率的方法。该项研究在遥感应用基础和山地辐射状况的研究中有重要意义。     

The U.S. Geological Survey Land Remote Sensing Program

The U.S. Geological Survey has been a provider of remotely sensed information for decades. As the availability and use of satellite data has grown, USGS has placed increasing emphasis on expanding the knowledge about the science of remote sensing and on making remotely sensed data more accessible. USGS encourages widespread availability and distribution of these data and through its programs, encourages and enables a variety of research activities and the development of useful applications of the data. The science of remote sensing has great potential for assisting in the monitoring and assessment of the impacts of natural disasters, management and analysis of environmental, biological, energy, and mineral investigations, and supporting informed public policy decisions. By establishing the Land Remote Sensing Program (LRS) as a major unit of the USGS Geography Program, USGS has taken the next step to further increase support for the accessibility, understanding, and use of remotely sensed data. This article describes the LRS Program, its mission and objectives, and how the program has been structured to accomplish its goals.     

An improved SVM model for relevance feedback in remote sensing image retrieval

With the rapid development of satellite remote sensing technology and an ever-increasing number of Earth observation satellites being launched, the global volume of remotely sensed imagery has been growing exponentially. Processing the variety of remotely sensed data has increasingly been complex and difficult. It is also hard to efficiently and intelligently retrieve what users need from a massive database of images. This paper introduces an improved support vector machine (SVM) model, which optimizes the model parameters and selects the feature subset based on the particle swarm optimization (PSO) method and genetic algorithm (GA) for remote sensing image retrieval. The results from an image retrieval experiment show that our method outperforms traditional methods such as GRID, PSO, and GA in terms of consistency and stability.     

Remotely sensed biomass over steep slopes: An evaluation among successional stands of the Atlantic Forest,Brazil

Remotely sensed images have been widely used to model biomass and carbon content on large spatial scales. Nevertheless, modeling biomass using remotely sensed data from steep slopes is still poorly understood. We investigated how topographical features affect biomass estimation using remotely sensed data and how such estimates can be used in the characterization of successional stands in the Atlantic Rainforest in southeastern Brazil. We estimated forest biomass using a modeling approach that included the use of both satellite data (LANDSAT) and topographic features derived from a digital elevation model (TOPODATA). Biomass estimations exhibited low error predictions (Adj. R² = 0.67 and RMSE = 35 Mg/ha) when combining satellite data with a secondary geomorphometric variable, the illumination factor, which is based on hill shading patterns. This improved biomass prediction helped us to determine carbon stock in different forest successional stands. Our results provide an important source of modeling information about large-scale biomass in remaining forests over steep slopes.     

地表蒸散定量遥感的研究进展

简要介绍了遥感监测地表蒸散和能量平衡研究的科学意义和应用价值，回顾了国内外的研究历史和现状，分析了目前该领域存在的一些问题和难点，并对今后的工作重点和研究方向提出了建议。     

舟山群岛-宁波深水港群遥感综合调查

利用TM/ETM⁺和SPOT开展港口现状、海岸类型和仓储场地的调查;利用ERS-2SAR调查探测锋面和内波等海洋环境要素;利用TM/ETM⁺和NOAA探测调查区的悬浮泥沙分布情况。最后,结合常规调查数据,对舟山群岛—宁波深水港群港口资源进行了综合评价。     

MODIS LAI产品真实性检验与偏差分析

当前对MODIS LAI产品的真实性检验工作中,更多的是关注遥感产品在数值与趋势上与地表真值的一致性程度,很少工作能够全面分析遥感LAI产品偏差来源以及不同来源的偏差对全局偏差的贡献率。本文在对MODIS LAI产品进行真实性检验基础之上,进一步分析了MODIS LAI产品偏差来源。将遥感产品真实性检验偏差来源分解为反演模型,反射率数据和冠层聚集效应3个方面,并定量分析各个偏差源对真实性检验结果的影响。以河北省怀来玉米为研究对象,结合实测LAI数据和Landsat 8 OLI(Operational Land Imager)数据建立NDVI LAI半经验模型,得到LAI参考数据,据此对MODIS LAI产品进行真实性检验及偏差分析。研究表明,该区域MODIS LAI产品存在明显的低估现象,参考数据和MODIS LAI数据均值分别为3.53 m2/m2和2.33 m2/m2,MODIS产品低估为34.14%。在各个偏差因素中,反射率数据的差异对结果影响最大,即MODIS地表反射率数据与Landsat 8 OLI地表反射率数据的差异造成的偏差占总偏差的57.50%;聚集效应的影响次之,占总偏差的28.33%;模型差异对结果的影响最小,占总偏差的14.17%。本研究对遥感产品真实性检验及其不确定性分析具有一定的借鉴意义。     

Remote sensing of nutrients in a subtropical African reservoir: testing utility of Landsat 8

Remote sensing is useful for water quality assessments but current remote sensing applications favour parameters that are easy to detect such as chlorophyll-a. An assessment of the utility of Landsat 8 for detecting nutrients was conducted in Mazvikadei reservoir in Zimbabwe. The main objective was to determine whether nutrients often overlooked by remote sensing and yet are the main determinants of water quality can be remotely sensed. Sampling targeted ammonia, nitrates and reactive phosphorus from May to October 2015. In situ nutrient concentrations were regressed against reflectance derived from Landsat 8 imagery. Strong negative relationships were found between ammonia and the near-infrared band in July (R² = 0.80, p < 0.05) as well as between nitrates and the blue band (R² = 0.67, p < 0.05) in June. Overall, the results suggest that the cool dry season is the optimum time to use Landsat 8 for monitoring nutrients in tropical lakes.     

Optimization of spectral indices and long-term separability analysis for classification of cereal crops using multi-spectral RapidEye imagery

Crop monitoring using remotely sensed image data provides valuable input for a large variety of applications in environmental and agricultural research. However, method development for discrimination between spectrally highly similar crop species remains a challenge in remote sensing. Calculation of vegetation indices is a frequently applied option to amplify the most distinctive parts of a spectrum. Since no vegetation index exist, that is universally best-performing, a method is presented that finds an index that is optimized for the classification of a specific satellite data set to separate two cereal crop types. The η² (eta-squared) measure of association – presented as novel spectral separability indicator – was used for the evaluation of the numerous tested indices. The approach is first applied on a RapidEye satellite image for the separation of winter wheat and winter barley in a Central German test site. The determined optimized index allows a more accurate classification (97%) than several well-established vegetation indices like NDVI and EVI (<87%). Furthermore, the approach was applied on a RapidEye multi-spectral image time series covering the years 2010–2014. The optimized index for the spectral separation of winter barley and winter wheat for each acquisition date was calculated and its ability to distinct the two classes was assessed. The results indicate that the calculated optimized indices perform better than the standard indices for most seasonal parts of the time series. The red edge spectral region proved to be of high significance for crop classification. Additionally, a time frame of best spectral separability of wheat and barley could be detected in early to mid-summer.     

数据驱动的蒸散发遥感反演方法及产品研究进展

蒸散发是水圈、大气圈和生物圈中水分循环和能量交换的纽带。在全球尺度上,蒸散发约占陆地降水总量的60%;作为其能量表达形式,潜热通量约占地表净辐射的80%。随着通量观测技术的发展,全球长期持续的观测数据得以获取和共享,近年来基于数据驱动的蒸散发遥感反演方法取得了较好的研究进展。本文针对数据驱动的蒸散发遥感反演方法和产品,从经验回归、机器学习和数据融合3个方面展开,对现有的研究进展进行了梳理、归纳和总结,并从驱动数据、反演方法、已有产品等方面指出目前仍存在的问题和不足。未来仍需开展数据驱动的高时空分辨率的蒸散发遥感反演方法的研究,有效考虑地表温度和土壤水分等可以指示地表蒸散发短期变化的重要信息,同时加强基于过程驱动的物理模型与数据驱动的模型的结合,使两类模型能互为补充、各自发挥所长,共同推动蒸散发遥感反演研究水平的进步。     

自然光辐射各分量卫星遥感图像的计算机生成原理与方法

本文讨论了利用卫星遥感数据和与之匹配的数字地面模型以及它们和光辐射各分量之间的定量关系，使用计算逐点分解遥感数字图像以生成成像瞬间直、散射辐射光照射下相应的卫星遥感图像的原理、方法。最后，阐述了生成的辐射各分量遥感图像特点和应用前景。     

Prediction of Land Cover Change Using Markov and Cellular Automata Models: Case of Al-Ain,UAE, 1992-2030

The UAE has witnessed rapid urban development and economic growth in recent years. With its ambitious vision to become one of the advanced nations by 2021, planners and policy-makers need to know the most likely direction of future urban development. In this study, remotely sensed imagery coupled with cellular automata models were used to predict land cover in Al Ain, the second largest city in the Emirate of Abu Dhabi. Markov and cellular automata models were used for 1992 and 2006 to predict land cover in 2012. Land Use and Land Cover maps for the study area were derived from 1992, 2006, and 2012 Landsat satellite images (TM, ETM⁺). The models achieved an overall accuracy of approximately 80 %. A Markov model was applied for 2006 and 2012 to predict land cover in 2030. The results conformed to the general trend of the Al Ain Master Plan 2030. This study demonstrates that remote sensing, with the availability of free Landsat data, is a viable technology that could be used to help in the prediction process especially in developing countries, where data availability is a problem.     

Uncertainty analysis of remote sensing of colored dissolved organic matter: Evaluations and comparisons for three rivers in North America

The uncertainties involved in remote sensing inversion of CDOM (Colored Dissolved Organic Matter) were analyzed in estuarine and coastal regions of three North American rivers: Mississippi, Hudson, and Neponset. Water optical and biogeochemical properties, including CDOM absorption and above-surface spectra, were collected in very high resolution. CDOM’s concentrations (a_g(440), absorption coefficient at 440 nm) were inverted from EO-1 Hyperion images, using a quasi-analytical algorithm for CDOM (QAA-CDOM). Uncertainties are classified to five levels, in which the underwater measurement uncertainty (level 1), image preprocessing uncertainty (level 4) and inverse model uncertainty (level 5) were evaluated. Results indicate that at level 1, in situ CDOM measurement is significant with 0.1 in the unit of QSU and 0.01 in the unit of a_g(440) (m⁻¹). At level 4, surface wave is a potential uncertainty source for high-resolution images in estuarine and coastal regions. The remote sensing reflectance of wavy water is about 10 times of the truth. At level 5, the overall uncertainty of QAA-CDOM inversion is 0.006 m⁻¹, with accuracy R² = 0.77, k = 1.1 and RMSE_log = 0.33 m⁻¹. The correlations between uncertainties and other water properties indicate that the large uncertainty in some rivers, such as the Neponset and Atchafalaya, might be caused by high-concentration chlorophyll or sediments. The relationships among the three level uncertainties show that the level 1 uncertainty generally does not propagate into level 4 and 5, but the large uncertainty at level 4 usually introduce large uncertainty at level 5.     

Comparing the Use of Remote Sensing with Traditional Techniques to Detect Nitrogen Stress in Wheat

Abstract

An experiment was designed to compare ground‐based methods of nitrogen (N) stress detection with N stress detection using remote sensing. The study area, located in Minidoka, Idaho, is a 64 ha center pivot with a crop of Penawawa spring white wheat. Nitrogen was varied on four transects approximately 40 m wide and 805 m long. The N application rates chosen for the research were 0%, 40%, 100%, and 130% of normal. Nitrogen deficiency was quantified from tissue sampling, which was used as the response variable. Nitrogen was then measured or estimated at key stages in the wheat growth cycle using visual observation, a chlorophyll meter, and remotely‐sensed data. Visual observation was the normally‐employed method for area farmers. These methods of N stress detection were compared for accuracy, timeliness, usefulness, and cost. Remote sensing was comparable to the chlorophyll meter in accuracy. The chlorophyll meter was the timeliest method for obtaining a quantitative measurement.