Genetic inverse algorithm for retrieval of component temperature of mixed pixel by multi-angle thermal infrared remote sensing data

After carefully studying the results of retrieval of land surface temperature(LST) by multi-channel thermal infrared remote sensing data, the authors of this paper point out that its accuracy and significance for applications are seriously damaged by the high correlation coefficient among multi-channel information and its disablement of direct retrieval of component temperature. Based on the model of directional radiation of non-isothermal mixed pixel, the authors point out that multi-angle thermal infrared remote sensing can offer the possibility to directly retrieve component temperature, but it is also a multi-parameter synchronous inverse problem. The results of digital simulation and field experiments show that the genetic inverse algorithm (GIA) is an effective method to fulfill multi-parameter synchronous retrieval. So it is possible to realize retrieval of component temperature with error less than 1K by multi-angle thermal infrared remote sensing data and GIA.     

Use of remote sensing for evapotranspiration monitoring over land surfaces

Abstract

Monitoring evapotranspiration (ET) at large scales is important for assessing climate and anthropogenic effects on natural and agricultural ecosystems. This paper describes techniques used in evaluating ET with remote sensing, which is the only technology that can efficiently and economically provide regional and global coverage. Some of the empirical/statistical techniques have been used operationally with satellite data for computing daily ET at regional scales. The more complex numerical simulation models require detailed input parameters that may limit their application to regions containing a large database of soils and vegetation properties. Current efforts are being directed towards simplifying the parameter requirements of these models. Essentially all energy balance models rely on an estimate of the available energy (net radiation less soil heat flux). Net radiation is not easily determined from space, although progress is being made. Simplified approaches for estimating soil heat flux appear promising for operational applications. In addition, most ET models utilize remote sensing data in the shortwave and thermal wavelengths to measure key boundary conditions. Differences between the radiometric surface temperature and aerodynamic temperature can be significant and progress in incorporating this effect is evident. Atmospheric effects on optical data are significant, and optical sensors cannot see through clouds. This has led some to use microwave observations as a surrogate for optical data to provide estimates of surface moisture and surface temperature; preliminary results are encouraging. The approaches that appear most promising use surface temperature and vegetation indices or a time rate of change in surface temperature coupled to an atmospheric boundary layer model. For many of these models, differences with ET observations can be as low as 20% from hourly to daily time scales, approaching the level of uncertainty in the measurement of ET and contradicting some recent pessimistic conclusions concerning the utility of remotely sensed radiometric surface temperature for determining the surface energy balance.     

Field‐based and spectral indicators for soil erosion mapping in semi‐arid mediterranean environments (Coastal Cordillera of central Chile)

The Coastal Cordillera of central Chile is naturally sensitive to soil erosion due to moderate to steep slopes, intense winter rains when the vegetation cover is scarce, and deeply weathered granitic rocks. In 1965, 60 per cent of its surface was moderately to very severely eroded. Today this process is still largely active, but no data are currently available to evaluate the real extent, distribution and severity of soil degradation on a regional scale. This information is vital to support efficient soil conservation plans. A multi‐scale approach was implemented to produce regional land degradation maps based on remote sensing technologies. Fieldwork has shown that the surface colour or ‘redness’ and the density of coarse fragments are pertinent erosion indicators to describe a typical sequence of soil degradation in the context of mediterranean soil developed on granitic materials and micaschists. Field radiometric experiments concluded that both factors influence the reflectance of natural surfaces and can be modelled using radiometric indices accessible from most satellites operating in the optical domain, i.e. redness index and brightness index. Finally the radiometric indices were successfully applied to SPOT images to produce land degradation maps. Only broad classes of erosion status were discriminated and the detection of the degradation processes was only possible when most of the fertile layer had already been removed. This technology provides decision‐making information required to develop regional soil conservation plans and to prioritize actions between catchment areas, especially in vast inter‐tropical regions where spatialized data are not always readily available. Copyright © 2006 John Wiley & Sons, Ltd.     

Correlation-based temperature and emissivity separation algorithm

Based on analyzing the relationship between the atmospheric downward radiance and surface emis- sivity, this paper proposes a correlation criterion to optimize surface temperature during the process of temperature and emissivity separation from thermal infrared hyperspectral data, and puts forward the correlation-based temperature and emissivity separation algorithm (CBTES). The algorithm uses the correlation between the atmospheric downward radiance and surface emissivity to optimize surface temperature, and obtains surface emissivity with this temperature. The accuracy of CBTES was evalu- ated by the simulated thermal infrared hyperspectral data. The simulated results show that the CBTES can achieve high accuracy of temperature and emissivity inversion. CBTES has been compared with the iterative spectrally smooth temperature/emissivity separation (ISSTES), and the comparison results show that they have relative accuracy. Besides, CBTES is insensitive to the instrumental random noise and the change of atmospheric downward radiance during the measurements. As regards the noniso- thermal pixel, its radiometric temperature changes slowly with the wavenumber when its emissivity is defined as r-emissivity. The CBTES can be used to derive the equivalent temperature of nonisothermal pixel in a narrow spectral region when we assumed that the radiometric temperature is invariable in the narrow spectral region. The derived equivalent temperatures in multi-spectral regions in 714―1250 cm?1 can characterize the change trend of nonisothermal pixel's radiometric temperature.     

On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: A numerical study for mixed-pixel environment

In this paper, we present a genetic algorithm-based methodology to quantify agricultural and water management practices from remote sensing (RS) data in a mixed-pixel environment. First, we formulated a linear mixture model for low spatial resolution RS data where we considered three agricultural land uses as dominant inside the pixel—rainfed, irrigated with two, and three croppings a year; the mixing parameters we considered were the sowing dates, area fractions of agricultural land uses in the pixel, and their corresponding water management practices. Then, we carried out numerical experiments to evaluate the feasibility of the proposed approach. In the process, the mixing parameters were parameterized by data assimilation using evapotranspiration and leaf area index as conditioning criteria. The soil–water–atmosphere–plant system model SWAP was used to simulate the dynamics of these two biophysical variables in the pixel. The results of our numerical experiments showed that it is possible to derive some sub-pixel information from low spatial resolution data e.g. the existing agricultural and water management practices in a region, which are relevant for regional agricultural monitoring programs.     

Response of remotely sensed Normalized Difference Water Deviation Index to the 2006 Drought of eastern Sichuan Basin

One of the most serious droughts in last century occurred in eastern Sichuan Basin in the summer of 2006 (hereinafter called the Drought). The response of Moderate Resolution Imaging Spectroradiometer (MODIS, boarding on NASA satellites of Terra and Aqua) to the Drought was analyzed in order to reach one practicable monitoring solution for regional soil moisture. Temporal process and spatial extension of the Drought were firstly estimated with ground meteorological and hydrological observations. Then, for the whole region of Sichuan and Chongqing, the remotely sensed Normalized Difference Water In- dex (NDWI) for the summers of 2001―2006 were calculated based on 8-day composite MODIS products, which were further used to construct a new water index (Normalized Difference Water Deviation Index, NDWDI) to examine the sensitivity of remote sensing in the Drought. The study showed that the NDWDI is more sensitive to regional drought than other absolute-soil-moisture-based indices. With the new index, the study extracted the spatial-temporal characteristics of the 2006 Drought, and explored its developing and withdrawing processes, which agreed with related statistics. Compared with ground method of drought observation, the NDWDI-based remote sensing solution of this paper is more pref- erable and practicable in that the local soil properties of water consumption and supply are implicitly taken into account, and the spatial representativity limit of ground observation is circumvented to a degree as satellite remotely senses the earth surface in a way of two-dimensional pixel matrix. So, the NDWDI-based method can be used to monitor regional soil water stress situation more practically and efficiently.     

Remote sensing and ground‐based measurements of evapotranspiration in an extreme cold Patagonian desert

Accurate estimates of seasonal evapotranspiration (ET) at different temporal and spatial scales are essential for understanding the biological and environmental determinants of ecosystem water balance in arid regions and the patterns of water utilization by the vegetation. For this purpose, remote sensing ET estimates of a Patagonian desert in Southern Argentina were verified with field measurements of soil evaporation and plant transpiration using an open top chamber. Root distribution and seasonal variation in soil volumetric water content were also analysed. There was a high correlation between remote sensing and field measurements of ecosystem water fluxes. A substantial amount of the annual ET occurred in spring and early summer (73.4 mm) using winter rain stored in the soil profile and resulting in water content depletion of the upper soil layers. A smaller amount of annual ET was derived from few rainfall events occurring during the mid or late summer (41.4 mm). According to remote sensing, the 92.9% of the mean annual precipitation returns to the atmosphere by transpiration or evaporation from the bare soil and by canopy interception. Only 7.1% infiltrates to soil layers deeper than 200 cm contributing to the water table recharge. Fourier time series analysis, cross‐correlation methods and multiple linear regression models were used to analyse 11 years of remote sensing data to assess determinants of water fluxes. A linear model predicts well the variables that drive complex ecosystem processes such as ET. Leaf area index and air temperature were not linearly correlated to ET because of the multiple interaction among variables resulting in time lags with ET variations and thus these two variables were not included in the linear model. Soil water content, the fraction of photosynthetic active radiation and precipitation explained 86% of the ET monthly variations. The high volumetric water content and the small seasonal variations at 200‐cm depth were probably the result of little water uptake from deeper soil horizons by roots with low hydraulic conductivity. Copyright © 2016 John Wiley & Sons, Ltd.     

基于单亲遗传算法的SAR图像混合像元分解

已有的遥感影像混合像元分解理论方法都要求遥感影像的通道数目大于地物种类,而合成孔径雷达(SAR)的自身特点决定了SAR图像不可能有过多的通道数目,为解决SAR图像地物种类大于通道数目情况下的混合像元分解问题,本文基于单亲遗传算法提出了一种新的混合像元分解方法,创建了一种新的染色体编码方式及进化迭代方式,新算法很好地实现混合像元的分解,可以分解出比通道数目更多的地物种类.并从北京地区ENVISAT-ASAR图像中截取天安门附近区域作为数据源进行实验,实验结果表明了本文算法的正确性和有效性.     

Airborne Thermal Remote Sensing for Estimation of Groundwater Discharge to a River

Traditional methods for studying surface water and groundwater interactions have usually been limited to point measurements, such as geochemical sampling and seepage measurement. A new methodology is presented for quantifying groundwater discharge to a river, by using river surface temperature data obtained from airborne thermal infrared remote sensing technology. The Hot Spot Analysis toolkit in ArcGIS was used to calculate the percentage of groundwater discharge to a river relative to the total flow of the river. This methodology was evaluated in the midstream of the Heihe River in the arid and semiarid northwest China. The results show that the percentage of groundwater discharge relative to the total streamflow was as high as 28%, which is in good agreement with the results from previous geochemical studies. The data analysis methodology used in this study is based on the assumption that the river water is fully mixed except in the areas of extremely low flow velocity, which could lead to underestimation of the amount of groundwater discharge. Despite this limitation, this remote sensing‐based approach provides an efficient means of quantifying the surface water and groundwater interactions on a regional scale.     

Verification of land surface evapotranspiration estimation from remote sensing spatial contextual information

Estimation of evapotranspiration (ET) is of great significance in modeling the water and energy interactions between land and atmosphere. Negative correlation of surface temperature (T_s) versus vegetation index (VI) from remote sensing data provides diagnosis on the spatial pattern of surface soil moisture and ET. This study further examined the applicability of T_s–VI triangle method with a newly developed edges determination technique in estimating regional evaporative fraction (EF) and ET at MODIS pixel scale through comparison with large aperture scintillometer (LAS) and high‐level eddy covariance measurements collected at Changwu agro‐ecological experiment station from late June to late October, 2009. An algorithm with merely land and atmosphere products from MODIS onboard Terra satellite was used to estimate the surface net radiation (R_n) and soil heat flux. In most cases, the estimated instantaneous R_n was in good agreement with surface measurement with slight overestimation by 12 W/m². Validation results from LAS measurement showed that the root mean square error is 0.097 for instantaneous EF, 48 W/m² for instantaneous sensible heat flux, and 30 W/m² for daily latent heat flux. This paper successfully presents a miniature of the overall capability of T_s–VI triangle in estimating regional EF and ET from limited number of data. For a thorough interpretation, further comprehensive investigation needs to be done with more integration of remote sensing data and in‐situ surface measurements. Copyright © 2011 John Wiley & Sons, Ltd.     

Research on the method for retrieving soil moisture using thermal inertia model

1 Introduction Thermal inertia is a bulk property that shows the re- sistance of a material to an input or output of heat. This plays a very important role in certain geological and hydrological studies, and climate modeling. In the 1970s, a simple thermal inertia model was proposed by Watson et al.[1―3]. Pratt (1979)[4] improved the thermal inertia model based on application tests where more factors were considered such as solar ra- diance, thermal conductivity effect, average humidity of g…     

A remote sensing model of CO2 flux for wheat and studying of regional distribution

A model for calculating CO₂ flux in the wheat field and an algorithm for estimating CO₂ flux in the mejonal scale were presented using the remote sensing data and supplementary micpo-met~orological data. First of all a-longertenn measurement wae carried out during winter wheat growing period in Yucheng Experimental Station udng the spectmradiometer system, the thermal infrared radiometer system, the Bowen-ratio device as well as the eddy-correlation device. Two kinds of issues concerning remote sensing and CO₂ flux can be obtained. Based on the obeervations a remote sensing model was estabilished. Then when the NOAA-AVHRR passed over the experimental area simultaneous measurements were carried out with the satellites. A regional distribution image for CO₂ flux over wheat canopy in North China (500×500 km²) was made using the supplementary ground data and NOAA-AVHRR remote sensing data which was calibrated by the synchronous observation. The sources and sinks for CO₂ fluxes in the region can be seen obviously. Project supported by the National Natural Science Foundation of China (Grant Nos. 49671058, 49890330)     

Remote sensing application system for water environments developed for Environment Satellite 1

Remote sensing data collected by the Environment Satellite I are characterized by high temporal resolution, high spectral resolution and mid-high spatial resolution. We designed the Remote Sensing Application System for Water Environments (RSASWE) to create an integrated platform for remote sensing data processing, parameter information extraction and thematic mapping using both remote sensing and GIS technologies. This system provides support for regional water environmental monitoring, and prediction and warning of water pollution. Developed to process and apply data collected by Environment Satellite I, this system has automated procedures including clipping, observation geometry computation, radiometric calibration, 6S atmospheric correction and water quality parameter inversion. RSASWE consists of six subsystems: remote sensing image processing, basic parameter inversion, water environment remote sensing thematic outputs, application outputs, automated water environment outputs and a non-point source pollution monitoring subsystem. At present RSASWE plays an important role in operations at the Satellite Environment Center.     

Improvement of TOPLATS‐based discharge predictions through assimilation of ERS‐based remotely sensed soil moisture values

In this paper, we investigate the possibility to improve discharge predictions from a lumped hydrological model through assimilation of remotely sensed soil moisture values. Therefore, an algorithm to estimate surface soil moisture values through active microwave remote sensing is developed, bypassing the need to collect in situ ground parameters. The algorithm to estimate soil moisture by use of radar data combines a physically based and an empirical back‐scatter model. This method estimates effective soil roughness parameters, and good estimates of surface soil moisture are provided for bare soils. These remotely sensed soil moisture values over bare soils are then assimilated into a hydrological model using the statistical correction method. The results suggest that it is possible to determine soil moisture values over bare soils from remote sensing observations without the need to collect ground truth data, and that there is potential to improve model‐based discharge predictions through assimilation of these remotely sensed soil moisture values. Copyright © 2002 John Wiley & Sons, Ltd.     

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.     

Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals

The upcoming deployment of satellite-based microwave sensors designed specifically to retrieve surface soil moisture represents an important milestone in efforts to develop hydrologic applications for remote sensing observations. However, typical measurement depths of microwave-based soil moisture retrievals are generally considered too shallow (top 2–5 cm of the soil column) for many important water cycle and agricultural applications. Recent work has demonstrated that thermal remote sensing estimates of surface radiometric temperature provide a complementary source of land surface information that can be used to define a robust proxy for root-zone (top 1 m of the soil column) soil moisture availability. In this analysis, we examine the potential benefits of simultaneously assimilating both microwave-based surface soil moisture retrievals and thermal infrared-based root-zone soil moisture estimates into a soil water balance model using a series of synthetic twin data assimilation experiments conducted at the USDA Optimizing Production Inputs for Economic and Environmental Enhancements (OPE³) site. Results from these experiments illustrate that, relative to a baseline case of assimilating only surface soil moisture retrievals, the assimilation of both root- and surface-zone soil moisture estimates reduces the root-mean-square difference between estimated and true root-zone soil moisture by 50% to 35% (assuming instantaneous root-zone soil moisture retrievals are obtained at an accuracy of between 0.020 and 0.030 m³ m⁻³). Most significantly, improvements in root-zone soil moisture accuracy are seen even for cases in which root-zone soil moisture retrievals are assumed to be relatively inaccurate (i.e. retrievals errors of up to 0.070 m³ m⁻³) or limited to only very sparse sampling (i.e. one instantaneous measurement every eight days). Preliminary real data results demonstrate a clear increase in the R² correlation coefficient with ground-based root-zone observations (from 0.51 to 0.73) upon assimilation of actual surface soil moisture and tower-based thermal infrared temperature observations made at the OPE³ study site.     

Operational applications of remote sensing in hydrology: success,prospects and problems

Abstract

The use of remote sensing information in operational hydrology is relatively limited, but specific examples can be cited for determining precipitation, soil moisture, groundwater, snow, surface water and basin characteristics. The application of remote sensing in hydrology can be termed operational if at least one of two conditions are met: (a) the application produces an output on a regular basis, or (b) the remote sensing data are used regularly on a continuing basis as part of a procedure to solve a problem or make decisions. When surveying the various operational applications, simple approaches and simple remote sensing data sets are the most successful. In the data-sparse developing countries, many operational remote sensing approaches exist (out of necessity) that may not be needed in developed countries because of existing data networks. To increase the use of remote sensing in operational hydrology in developed countries, pilot projects need to be increased and information services must be improved. Increased utilization of GIS to combine remote sensing with other information will promote new products and applications. End user training must be improved by focusing on satellite data processing and manipulation. In developing countries the same improvements are needed plus some more basic ones. There is a need for international monetary assistance to establish long-term remote sensing data, improved database systems and image processing capabilities. There is also the need to set up innovative regional training centres throughout the developing world.     

Estimation of spatially distributed surface energy fluxes using remotely‐sensed data for agricultural fields

Land surface energy fluxes are required in many environmental studies, including hydrology, agronomy and meteorology. Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near ground level. Spatial variability of energy fluxes limits point measurements to be used for larger areas. Remote sensing provides the basis for spatial mapping of energy fluxes. Remote‐sensing‐based surface energy flux‐mapping was conducted using seven Landsat images from 1997 to 2002 at four contiguous crop fields located in Polk County, northwestern Minnesota. Spatially distributed surface energy fluxes were estimated and mapped at 30 m pixel level from Landsat Thematic Mapper and Enhanced Thematic Mapper images and weather information. Net radiation was determined using the surface energy balance algorithm for land (SEBAL) procedure. Applying the two‐source energy balance (TSEB) model, the surface temperature and the latent and sensible heat fluxes were partitioned into vegetation and soil components and estimated at the pixel level. Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m⁻² and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean R² = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m³ ha⁻¹). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. Copyright © 2005 John Wiley & Sons, Ltd.     

A scheme for regional rice yield estimation using ENVISAT ASAR data

Information on rice growing areas and rice production is critical for most rice growing countries to make state and economic policies. However, the areas where rice crop is cultivated are often cloudy and rainy, which entails the use of radar remote sensing data for rice monitoring. In this paper, a practical scheme to integrate multi-temporal and multi-polarization ENVISAT ASAR data into rice crop model for regional rice yield estimation has been presented. To achieve this, rice distribution information should be obtained first by rice mapping method to retrieve rice fields from ASAR images, and then an assimilation method is applied to use the observed multi-temporal rice backscattering coefficients which are grouped for each rice pixel to re-initialize ORYZA2000 to predict rice yield. The assimilation method re-initializes the model with optimal input parameters, allowing a better temporal agreement between the rice backscattering coefficients retrieved from ASAR data and the rice backscattering coefficients simulated by a coupled model, i.e., the combination of ORYZA2000 and a semi-empirical rice backscatter model through LAI. The SCE-UA optimization algorithm is employed to determine the optimal set of input parameters. After the re-initialization, rice yield for each rice pixel is calculated, and the yield map over the area of interest is produced. The scheme was validated over Xinghua study area located in the middle of Jiangsu Province of China by using the data set of an experimental campaign carried out during the 2006 rice season. The result shows that the obtained rice yield map generally overestimates the actual rice production by 13% on average and with a root mean square error of approximately 1133 kg/ha on validation sites, but the tendency of rice growth status and spatial variation of the rice yield are well predicted and highly consistent with the actual production variation. Supported by the ESA-NRSCC Dragon Cooperation Program (), the Project for Jiangsu Graduate in Scientific Research and Innovation (No. CX07B_048z), and the Special Program for Scientific Research in Public Welfare Meteorological Services (No. GYHY200806008)     

Data assimilation methods in the Earth sciences

Although remote sensing data are often plentiful, they do not usually satisfy the users’ needs directly. Data assimilation is required to extract information about geophysical fields of interest from the remote sensing observations and to make the data more accessible to users. Remote sensing may provide, for example, measurements of surface soil moisture, snow water equivalent, snow cover, or land surface (skin) temperature. Data assimilation can then be used to estimate variables that are not directly observed from space but are needed for applications, for instance root zone soil moisture or land surface fluxes. The paper provides a brief introduction to modern data assimilation methods in the Earth sciences, their applications, and pertinent research questions. Our general overview is readily accessible to hydrologic remote sensing scientists. Within the general context of Earth science data assimilation, we point to examples of the assimilation of remotely sensed observations in land surface hydrology.