Robust Satellite Techniques for oil spill detection and monitoring using AVHRR thermal infrared bands

In this article, a new satellite technique for oil spill detection and monitoring is fully presented and discussed. It is based on the general RST (Robust Satellite Techniques) approach applied to Advanced Very High Resolution Radiometer (AVHRR) observations in the thermal infrared region of the electromagnetic spectrum. The proposed approach, which exploits the analysis of multi-temporal satellite records, seems to be able to detect the anomalous signals on the sea due to the oil polluted areas with excellent reliability (0% of false alarms) and good sensitivity in different observational conditions. Its performances have also been evaluated in comparison with another well-known AVHRR approach, analysing the spill event which happened during the Gulf War off the Kuwait and Saudi Arabia coasts in January 1991. The results confirm the reliability of the proposed approach which promises to offer new economically sustainable opportunities for building a near-real-time monitoring system for oil spills on a global scale. Moreover, in order to further assess the exportability of the proposed approach in different observational and environmental conditions, outcomes obtained by applying it to the Seki–Baynunah event affecting the Gulf of Oman in March 1994 are also shown.     

Automated detection of thermal features of active volcanoes by means of infrared AVHRR records

An innovative, Advanced Very High Resolution Radiometer (AVHRR)-based technique for improved automatic detection of volcanic hotspots and thermal anomalies is proposed in this paper. It is mainly based on a multitemporal analysis of historical, long-term satellite records. Such a technique basically rests on the Robust AVHRR Techniques (RAT) approach, which has been already successfully applied to several natural and environmental emergencies (e.g., fires, floods, earthquakes). In this work, the proposed technique has been tested on an extended set of eruptive events of Mt. Etna and Stromboli volcanoes. Results achieved, in terms of reliability (low false alarm rate) as well as of effectiveness (detection sensitivity), are described in detail. Moreover, the potential in low-level thermal anomaly detection, as possible pre-eruptive thermal signs, is also addressed and preliminary results obtained for a couple of events, discussed. The study cases here presented show the benefits of such a technique especially when different observational conditions (time/season of pass, atmospheric moisture content, solar illumination, satellite angles of view, etc.) are considered, making such a method globally applicable.The future prospects, also in terms of possible operational scenarios, coming from the implementation of such an approach on the new generation of satellite sensors (such as SEVIRI aboard Meteosat Second Generation) are also discussed.     

Improving flood monitoring by the Robust AVHRR Technique (RAT) approach: the case of the April 2000 Hungary flood

In the past, satellite remote sensing techniques have been widely used within the flood risk management cycle. In particular, there have been many demonstrations of the operational use of satellite data for detailed monitoring and mapping of floods and for post-flood damage assessment. When frequent situation reports are requested (e.g. in the emergency phase or for early warning purposes) to assist civil protection activities, high temporal resolution satellites (mainly meteorological, with revisiting times from hours to minutes) can play a strategic role. In this paper, a new Advanced Very High Resolution Radiometer (AVHRR) technique for monitoring flooded areas is presented. Its performances are evaluated in comparison with other well-known approaches, analysing the flood event that occurred in Hungary during April 2000 involving the Tisza and Timis Rivers. The preliminary results seem to indicate the benefits of such a new technique, especially when different observational conditions are considered. In fact, compared with previously proposed techniques, the proposed approach: (a) is completely automatic (i.e. unsupervised with no need for operator intervention); (b) improves flooded-area detection capabilities strongly reducing false alarms; and (c) automatically discriminates (without the need for ancillary information) flooded areas from permanent water bodies. Moreover, it is globally applicable and, because of the complete independence on the specific satellite platform, is easily exportable to different satellite packages.     

FY-3微波成像仪地表参数反演研究

风云3号卫星FY-3是实现全球、全天候、三维、定量、多光谱遥感的我国第2代极轨气象卫星系列。风云3号气象卫星资料中含有丰富的生态环境变化信息,既可以用于对水、火、冰、雪等灾害的监测,也可以用于对植被、土地利用、气溶胶参量的分析。这些结果将会对农业、林业、环境、市政、交通以及政府决策部门提供有效的决策服务。其中搭载的微波成像仪为我国第一个星载微波遥感仪器,其设计频率为10.65 GHz、18.7 GHz、23.8 GHz、36.5 GHz、89 GHz,每个频率有V、H两种不同极化模式,相应的星下点空间分辨率分别为51 km×85 km、30 km×50 km、27 km×45 km、18 km×30 km、9 km×15 km根据FY-3微波成像仪传感器参数特性,利用微波地表辐射传输方程,在10.65、18.7 GHz频段上模拟了地表微波辐射特性,在此基础上建立了地表参数反演算法, 可以同时得到地表土壤水分和地表温度参数。     

Improving volcanic ash cloud detection by a robust satellite technique

Automated and reliable satellite-based techniques are strongly required for volcanic ash cloud detection and tracking. In fact, volcanic ash clouds pose a serious hazard for air traffic and the synoptic (and possibly frequent) coverage offered by satellites can provide exciting opportunities for monitoring activities as well as for risk mitigation purposes.A new, AVHRR-based technique for improved automatic detection of volcanic clouds by means of multi-temporal analysis of historical, long-term satellite records has been recently proposed. The technique basically rests on the Robust AVHRR Techniques (RAT) approach, which is an innovative strategy of satellite data analysis, devoted to a former characterisation of the measured signal, in terms of expected value and natural variability and a further recognition of signal anomalies by an automatic, unsupervised change detection step. In this work, an extension of this method to nighttime observations is presented, by using thermal infrared information coming from AVHRR bands centred approximately at 3.5, 11.0 and 12.0 μm. Results achieved for two recent eruptive events of Mount Etna (occurred in May 2000 and in July 2001) seem to be encouraging, showing clear improvements in terms of ash detection sensitivity as well as in terms of false alarms reduction. The technique performance is also evaluated by comparison with the traditional “split-window” brightness temperature difference method; this exercise revealed a general improvement obtained by the proposed approach, even though some common problems still remain unsolved. The main merits of such an approach are its intrinsic self-adaptability to different environmental/natural/observational conditions and its natural exportability also to different satellite sensors. The results here presented show the benefits of such a technique especially when different observational conditions (time of pass, seasonal period, atmospheric moisture, solar illumination, volcanic cloud composition, satellite angles of view, etc.) are considered.The future prospects, also in terms of possible operational scenarios, coming from the implementation of such an approach on the new generation of satellite sensors (like, for example, SEVIRI aboard Meteosat Second Generation platform) are also discussed.     

A genetic algorithm to simultaneously retrieve land surface roughness and soil wetness

Two key parameters to affect microwave backscattering from the land surface are the surface roughness and soil wetness. A novel genetic algorithm is developed for multi-parameter retrieval of land surface roughness and soil wetness from angular backscattering observations. Parameters of wetness and roughness are encoded into genes. Genes are constituents of chromosomes, which undergo optimal selection based on a natural evolutionary process in the genetic algorithm. The theoretical model of a two-scale rough surface is employed for computation of the cost function. Results retrieved using this genetic algorithm are compared well with ground data measurements. This study presents an example of the genetic algorithm for application of multi-parameter retrieval in remote sensing.     

Determination of the soil moisture recession constant from satellite data: a case study of the Yucatan peninsula

Estimation of the recession constant for soil moisture can assist in soil and water management. This article estimates soil moisture recession velocity from satellite data, thereby taking advantage of extensive data coverage in a metric that is more commonly used with point data for rivers. Retrieval from satellites of the surface soil moisture has produced global coverage of multiannual time series data, thereby allowing the application of techniques that require long time series of daily data. We applied two techniques from river hydrology to soil moisture data from the advanced scatterometer aboard the meteorological operational satellite: (1) baseflow separation; and (2) master recession curve (MRC) with the correlation method. The former filtered the data and extracted those for the base soil moisture (BSM), which is considered the water that circulates in the soil by capillarity. The latter technique allowed the estimation of recession constants by the extraction of continuously decreasing BSM segments. The use of MRC for a large range of BSM provides a recession constant representative of all the moisture decrease for each pixel, thereby permitting the identification of drought-sensitive zones. The recession constant, a metric that had not been used for soil moisture, allowed us to determine potential temporal evolution of drought in the Yucatan peninsula. Government agencies could use the approach applied in this study to improve water management and drought prevention.     

Soil moisture variations monitoring by AMSU-based soil wetness indices: A long-term inter-comparison with ground measurements

Soil moisture controls the partitioning of rainfall into runoff and infiltration and, consequently, the runoff generation. On the catchment scale its routine monitoring can be performed through remote sensing technologies. Within this framework, the purpose of this study is to investigate the potential of the Advanced Microwave Sounding Unit (AMSU), radiometer on board the NOAA (National Oceanic and Atmospheric Administration) satellites and operating since 1998, for the assessment of soil wetness conditions by comparing soil moisture data with both those measured in situ and provided by a continuous rainfall-runoff model applied to four catchments located in the Upper Tiber River (Central Italy). In particular, in order to perform a robust analysis an extensive and long-term period (nine years) of data was investigated. In detail, the Soil Wetness Variation Index, derived from the AMSU data modified in order to take account of the difference between the soil layer investigated by the satellite sensor and that used as a benchmark, was found to be correlated both with the in-situ and modeled soil moisture variations showing correlation coefficients in the range of 0.42-0.49 and 0.33-0.48, respectively. As far as the soil moisture temporal pattern is concerned, higher correlations were obtained (0.59-0.84 for the in-situ data and 0.82-0.87 for the modeled data set) partly due to the soil moisture seasonal pattern that enhances the correlation. Overall, the root mean square error was found to be less than 0.05 m³/m³ for both the comparisons, thus assessing the potential of the AMSU sensor to quantitatively retrieve soil moisture temporal patterns. Moreover, the AMSU sensor can be considered as a useful tool to provide a reliable and frequently updated global soil moisture data set, considering its higher temporal resolution now available (about 4 passes per day) thanks to the presence of the sensor aboard different satellites.     

A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations

The backscattering and emission measured simultaneously by radar and radiometer show promise for the estimation of surface variables such as near-surface soil moisture and vegetation characteristics. In this paper, the 10.7 GHz Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) channel and 13.8 GHz precipitation radar (PR) observations are simultaneously used for the estimation of the near-surface soil moisture and vegetation properties. The Fresnel model for soil and a simple model for vegetation are used to simulate the passive microwave emission at 10.7 GHz. To determine the PR backscatter signal from a land surface, a theoretical approach is used based on the Geometric Optics Model for simulating bare soil and a semi-empirical water-cloud model for vegetation. The model parameters required in specifying the nature of the soil and vegetation are calibrated on the basis of in situ soil moisture data combined with remotely sensed observations. The calibrated model is subsequently used to retrieve near-surface soil moisture and leaf area index for assumed values of surface roughness and temperature. Algorithm assessment using synthetic passive and active microwave data shows a nonlinearity effect in the system inversion, which results in a varying degree of error statistics in soil wetness and vegetation characteristics retrieval. The technique was applied on TRMM radar/radiometer observations from three consecutive years and evaluated against in situ near-surface (5 cm) soil moisture measurements from the Oklahoma Mesonet showing a consistent performance.     

基于SPOT-VGT可见光/短波红外波段数据对AMSR-E土壤湿度的降尺度研究

基于SPOT-VGT数据,由短波红外、红和蓝波段反射率计算了表征地表土壤湿度的可见光—短波红外干旱指数(VSDI),通过对1km空间分辨率的VSDI影像进行空间升尺度处理,采用多种函数建立了25km空间分辨率AMSR-E土壤湿度数据与VSDI指数的关系,发现二者关系最符合S型曲线模型,拟合残差在空间上呈现随机分布的特征。基于S曲线函数关系下的1km预测土壤湿度和残差值,对AMSR-E土壤湿度进行降尺度模拟,得到1km空间分辨率的土壤湿度。将原始AMSR-E土壤湿度和实测数据对降尺度结果分别比较验证后,表明基于该方法获得的土壤湿度模拟精度较高。     

Emissivity peculiarities of the inland salt marshes in the south of Western Siberia

The presence of water-dissoluble salts in soils leads to a wide variation in soil microwave radiation that makes precise determination of water-physical characteristics of soil using satellite data hardly possible. To assess the effect of soil salinization, the emissivity of salt-affected soils in the Kulunda plain located in the south of Western Siberia was studied. Steppe site and an inland salt marsh (ISM) formed at the bottom of depressions of dried-up salt lakes were the main objects of the research. Both sites are situated in the same natural-climatic conditions and have similar relief. The spatial distribution of the underlying surface brightness temperature was obtained from L1C product of Soil Moisture and Ocean Salinity (SMOS) mission. The diurnal temperature variations of ISM were measured in the course of a daily ground-based experiment to determine the temperature gradients. During the laboratory experiment, we defined the dielectric properties of soil and calculated its emissivity. The comprehensive studies are evidence of great diurnal variation in ISM microwave radiation. The joint use of satellite and laboratory data allows us to specify the contribution of salt-affected soils into total microwave radiation of the underlying surface. The brightness temperature obtained from SMOS and the one calculated from the laboratory dependencies of ISM emissivity on temperature and moisture are in fairly well correlation.     

Derivation of pan-Arctic soil decomposition rate constant,heterotrophic respiration and NEE using AMSR-E and MODIS data

An approach was developed for regional assessment and monitoring of land-atmosphere carbon dioxide (CO₂) exchange, soil heterotrophic respiration (R _h), and vegetation productivity of Arctic tundra using global satellite remote sensing at optical and microwave wavelengths. C- and X-band brightness temperatures were used from the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) to extract surface wetness and temperature, and MODerate Resolution Imaging Spectroradiometer (MODIS) data were used to derive land cover, Leaf Area Index (LAI), and Net Primary Production (NPP) information. Calibration and validation activities involve comparisons between satellite remote sensing and tundra CO₂ eddy flux towers, and hydroecological process model simulations. Analyses of spatial and temporal anomalies and environmental drivers of land-atmosphere net CO₂ exchange at weekly and annual time steps were conducted. Surface soil moisture and temperature, as detected from satellite remote-sensing observations, were found to be major drivers for spatial and temporal patterns of tundra net ecosystem CO₂ exchange and photosynthetic and respiration processes. Satellite microwave measurements are capable of capturing seasonal variations and regional patterns in tundra soil heterotrophic respiration and CO₂ exchange, while the ability to extract spatial patterns at the scale of surface heterogeneity is limited by the coarse spatial scale of the satellite remote-sensing footprint. The microwave-derived surface temperature and soil moisture were used to estimate net ecosystem carbon exchange (NEE) at the boreal-Arctic region. These were validated using flux tower sites data. Existing satellite-based measurements of vegetation structure (i.e. LAI) and productivity (i.e. Gross Primary Production (GPP) and NPP) from the Aqua/Terra MODIS with the AMSR-E-derived land-surface temperature and soil moisture were used and integrated. Spatially explicit estimates of NEE for the pan-Arctic region at daily, weekly and annual intervals were derived. Comparative analysis of satellite data-derived NEE with measurements from CO₂ eddy flux tower sites and the BIOME-BGC model were carried out and good agreement was found. The comparative analysis is statistically significant with high regression (i.e. R ²?=?0.965), especially in the R _h calculation and the overall NEE regression is 0.478. The results also indicate that the carbon cycle response to climate change is nonlinear and is strongly coupled to Arctic surface hydrology.     

Assessing snowmelt dynamics with NASA scatterometer (NSCAT) data and a hydrologic process model

The presence of snow strongly impacts the exchange of moisture and energy between the land surface and atmosphere. In the interior of the northern hemisphere continents, snowmelt on frozen soils can cause or exacerbate major floods. Microwave remote sensing from satellite platforms has the potential to monitor the freeze-thaw status of soil and overlying snow packs over large areas. We evaluate the backscatter response of the NSCAT scatterometer to changing snow surface conditions, especially freeze and thaw status, using a macroscale hydrology model and the NSCAT backscatter data for the upper Mississippi River basin of the north central U.S. and the Boreal Ecosystem Atmosphere Study (BOREAS) region in central Canada. We compared the snowmelt conditions simulated by the Variable Infiltration Capacity (VIC) macroscale hydrology model driven with surface meteorological observations with NSCAT measurements for 1996-1997 snow season. A mid-winter thaw event (in February) and late season melt (April-May) are evaluated for both regions. Comparison of backscatter images with daily and hourly-modeled snow surface wetness and temperature showed that the model agreed with the backscatter for snow surface wetness on some days but not on others. Factors such as NSCAT overpass times, vegetation on the ground and their freeze-thaw state, and liquid moisture content appear to contribute to these discrepancies.     

被动微波遥感土壤水分反演研究综述

由于微波具有全天候、穿透性以及不受云的影响等特征,使其在遥感研究全球变化中具有越来越大的优势。在微波传感器技术发展的过程中,人们通过研究发现被动微波遥感是反演土壤水分的各种技术中最有效的方法之一,而植被覆盖地区的土壤水分反演是反演算法中的难点。简略地介绍针对裸地的Q/P模型和针对植被的τ-ω模型,以及主要土壤水分反演算法。     

微波遥感土壤湿度研究进展

发展实用的微波遥感土壤湿度卫星反演算法,以提供区域尺度上的土壤湿度信息,对水文学、气象学以及农业科学研究与应用至关重要。简要分析了主动微波遥感土壤湿度的研究进展情况,重点对被动微波遥感土壤湿度的原理、算法发展及研究趋势进行了详细论述。主动微波具有卫星数据空间分辨率高的特点,随着携载主动微波器的一系列卫星的发射,主动微波遥感土壤湿度将受到重视。被动微波遥感研究历史长、反演算法特别是卫星遥感算比较成熟,是今后区域尺度乃至全球尺度监测土壤湿度的重要手段。     

预警卫星半实物仿真系统设计与实现

国防支援计划(DSP)是美国现役的预警卫星系统,主要对导弹进行早期探测和预警;为评估DSP卫星对目标的探测能力,文中分析了预警卫星的部署情况和工作模式,提出了预警卫星仿真系统的总体设计方案,确定该系统的功能模块以及实现方式;根据预警卫星双波段探测的工作模式构建了红外双色扫描探测模型,提出了基于双霍夫变换的多帧关联目标检测算法,利用目标的运行特性有效去除背景噪声,并结合双波段探测信息融合策略降低卫星虚警率;最后利用双星定位方法预估目标轨迹并对不同发射角目标的预测轨迹与真实轨迹之间的误差进行对比分析;通过试验运行,该系统达到了预期结果,具备对目标导弹的探测和跟踪能力,为导弹性能评估提供一种仿真手段和依据。     

Identification of clay pans from AMSR-E passive microwave observations

A new methodology to derive the spatial distribution of clay pans from satellite microwave data is presented. Soil moisture has a different temporal signature in clay pans compared with other soils, which is directly reflected in the satellite-observed brightness temperatures. Three years of Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) 6.9 GHz microwave observations were compiled and analysed over continental Australia to identify clay pans. This led to the development of a brightness temperature variance index (BTVI), which shows a strong spatial correspondence to an existing soil texture map and the ability to map clay pans for semi-arid regions. This simple method emphasizes the potential use of passive microwave remote sensing for soil type mapping.     

Multitemporal SAR images for monitoring cultivation systems using case-based reasoning

This paper demonstrates that multitemporal satellite SAR images are most suitable for monitoring the rapid changes of cultivation systems in a subtropical region. A new method is proposed by applying case-based reasoning (CBR) techniques to the classification of SAR images. Stratified sampling is carried out to collect the cases so that the variations of backscatters within a class can be appropriately captured. The use of discrete cases can conveniently represent the internal changes of a class under complicated situations, such as spatial changes in soil conditions and terrain features. These spatial variations are difficult to represent by using rules or mathematical equations. The proposed method has better classification performance than supervised classification methods in the study area. The case library is reusable for time-independent classification when the SAR images are acquired at the same time of the crop growth cycles for different years. The proposed method has been tested in the Pearl River Delta in South China.     

雷达高度计线性调频脉冲产生的新技术

线性调频脉冲(Chirp)信号技术是海洋平面高度测量的关键技术,在国外利用卫星测量海面高度计中已得到广泛应用。80年代后期,我们开始摸索Chirp信号技术的应用。1995年进行了以飞机为载体的高度计平台海上飞行实验,圆满获得了平均海平面高度数据和波高数据,其波高分辨率为0.75m。这一新技术应用成功,为我国遥感领域填补了空白。