Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia

This study examined the covariability between interannual changes in the normalized difference vegetation index (NDVI) and actual evapotranspiration (ET). To reduce possible uncertainty in the NDVI time series, two NDVI datasets derived from Pathfinder AVHRR Land (PAL) data and the Global Inventory Monitoring and Modeling Studies (GIMMS) group were used. Analyses were conducted using data over northern Asia from 1982 to 2000. Interannual changes over 19 years in the PAL-NDVI and GIMMS-NDVI were compared with interannual changes in ET estimated from model-assimilated atmospheric data and gridded precipitation data. For both NDVI datasets, the annual maximum correlation with ET occurred in June, which is the beginning of the vegetation growing season. The PAL and GIMMS datasets showed a significant, positive correlation between interannual changes in the NDVI and ET over most of the vegetated land area in June. These results suggest that interannual changes in vegetation activity predominantly control interannual changes in ET in June. Based on analyses of interannual changes in temperature, precipitation, and the NDVI in June, the study area can be roughly divided into two regions, the warmth-dominated northernmost region and the wetness-dominated southern region, indicating that interannual changes in vegetation and the resultant interannual changes in ET are controlled by warmth and wetness in these two regions, respectively.     

Estimation of regional evapotranspiration of arable crops from thermal infrared images

By combining the surface energy balance equation with the vertical transport equations for latent and sensible heat, crop temperatures can, in principle, be transformed into instantaneous evapotranspiration values. To obtain 24 hour values the TERGRA model of Soer can be applied.

In the method of Jackson et al. midday surface-air temperature differences are linearly related to 24 hour evapotranspiration and net radiation values. To estimate the slope of this relationship a crop-dependent analytical expression has been derived by Seguin and Itier.

The present authors propose a modification of the method of Jackson by relating the actual crop temperatures to the temperature of the same but potentially transpiring crop. The net radiation term is replaced by the potential evapotranspiration of that crop. This means that the interpretation of heat images for each separate crop would produce a different linear relationship. From results obtained with the TERGRA model the slope of the linear relationship was found to be 0·37, 0·66 and l·62mm d^?1K^?1 for surface roughnesses of, respectively, 1·0, 3·5 and 100 cm.

The possibility of determining regional evapotranspiration is demonstrated for a potato and sugar beet crop using heat images that were taken in the Netherlands at the end of a very dry period in the summer of 1982.

To obtain information about the water use of agricultural crops during the entire growing season simulation models are often used, e.g. the SWATRE model. Results obtained with the SWATRE model are compared with the remote-sensing approach.     

Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India

The Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites has been used for mapping vegetation cover and classification employing the Normalized Difference Vegetation Index (NDVI). Recently, this technique has been improved by converting NDVI with radiation measured in one of the thermal channels and converting brightness temperature into the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). These indices are being used for estimation of vegetation health and monitoring drought. The present study shows the application of vegetation and temperature condition indices for drought monitoring in India.     

Estimation of evaporative fraction and evapotranspiration from MODIS products using a complementary based model

We present a new formulation to derive evaporative fraction (EF) and evapotranspiration (ET) maps from remotely sensed data without auxiliary relationships or site-specific relationships. This formulation is based on Granger's complementary relationship and Priestley-Taylor's equation. The proposed model eliminates the wind function and resistance parameters commonly applied to ET calculation by including a relative evaporation parameter (ET/Epot). By combining this relative evaporation parameter, Granger's complementary relationship and Priestley-Taylor equation, we obtain a simple equation to estimate ET. We tested and validated the proposed formulation over the Southern Great Plains (SGP) region of the United States for seven clear sky days during March-October 2003. MODIS Atmospheric and Land products were the only source of data used in this study. Estimates of ET show an overall root mean square error and bias of 33.89 and − 10.96 Wm^− 2, respectively. Our results suggest that the proposed approach is robust and valid for a wide range of atmospheric and surface conditions.     

Estimation of the evapotranspiration using surface and satellite data

Abstract

A method to derive evapotranspiration from a combination of satellite and conventional data is investigated. For this purpose NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) infrared images on clear days of various seasons are used to derive surface temperatures over France. These temperatures are then compared to the shelter-height temperatures collected at the WMO (World Meteorological Organization) standard meteorological stations at the time of satellite overpass. The difference between the two temperatures varies both with season and latitude. To analyse those results we use a model of the soil-vegetation interface, forced by a reconstruction of the surface fluxes derived from the WMO data. The model simulates reasonably well the diurnal and seasonal variations in the difference between satellite surface temperature and surface-air temperature. The corresponding latitudinal variations which occur in summer may be interpreted in terms of evapotranspiration. The limitations of this method are determined by a model sensitivity study; in particular they are due to the role played by tall vegetation.     

位姿测量结果对标定误差的鲁棒性分析

在三个控制点构成等腰三角形的前提下,研究P3P位姿测量结果对像机内参数标定误差的鲁棒性.采用简化条件下的理论推导结合一般条件下的仿真实验,得到了像机内参标定误差引起的位姿测量结果误差与像机焦比成反比,与主点坐标无关,随控制点间距离的增大而减小,随测量距离的增大而增大;6个位姿量中,除光轴方向平移量的误差主要受焦比误差的影响外,其它主要受主点坐标误差的影响等结论.     

Estimation of methane emission from West Siberian wetland by scaling technique between NOAA AVHRR and SPOT HRV

Wetlands are one of the most important ecosystems in the world and at the same time they are presumed to be a source of methane gas, which is one of the most important greenhouse gases. The West Siberian wetlands is the largest in the world and remote sensing techniques can play an important role for monitoring the wetland.High spatial resolution satellite data are effective for monitoring land cover type changes, but can't cover a wide area because of a narrow swath width. On the other hand, global scale data are indispensable in covering a large area, but are too coarse to get the detailed information due to the low spatial resolution. It is necessary to devise a method for the fusion of the data with different spatial resolutions for monitoring the scale-differed phenomena.In this paper, firstly, a SPOT HRV image near Plotnikovo mire was used to map four wetland ecosystems (birch forest, conifer forest, forested bog and open bog) supplemented by field observation. Then, spectral mixture analysis was performed between NOAA AVHRR and SPOT HRV data acquired on the same day.Secondly, field observations were scaled up with these different spatial resolution satellite data. Each of the wetland ecosystem coverage fraction at the sub-pixel level was provided by spectral mixture analysis. Field observation shows that the mean rate of CH₄ emission from forested bog and open bog averaged 21.1 and 233.1 (mg CH₄/m²/day), respectively. The methane emission from the area was estimated by multiplying these average methane emission rates and the fraction coverage in each AVHRR pixel.Finally, the total methane emission over AVHRR coverage was estimated to be 9.46 (10⁹ g CH₄/day) and the mean methane emission over AVHRR coverage was calculated as 59.3 (mg CH₄/m²/day). We could conclude that this mean value is within the probabilistic variability as compared with the airborne measurement results.     

Efficiency Research of Planted Rice Area Estimation From NOAA一AVHRR Data in Taihu Domain

运用NOAA-AVHRR资料估算水稻种植面积，是遥感应用领域中一个新的研究方向，结合国家“八五”攻关项目“太湖地区遥感话产”的要求，在太湖地区进行了初步的尝试:(1)根据估产精度要求和NOAA一AVHRR资料校正精度，探讨了运用NOAA一AVHRR资料估产所需的最小区域范围。(2)针对太湖地区的具休地理环境设计了提取水稻种植曲积的技术方案，并在试验区取得了初步成果。     

Error growth in position estimation from noisy relative pose measurements

We examine how the estimation error grows with time when a mobile robot estimates its location from relative pose measurements without global position or orientation sensors. We show that, in both two-dimensional and three-dimensional space, both the bias and the variance of the position estimation error grows at most linearly with time asymptotically. Non-asymptotic bounds on the bias and variance are obtained, which provide insight into the mechanism of error growth. The bias is crucially dependent on the trajectory of the robot. Conclusions on the asymptotic growth rate of the bias continue to hold even with unbiased measurements or error-free translation measurements. Exact formulas for the bias and the variance of the position estimation error are provided for two specific two-dimensional trajectories–straight line and periodic. Experiments with a P3-DX wheeled robot and Monte Carlo simulations are provided to verify the theoretical predictions. A method to reduce the bias is proposed based on the lessons learned.     

Retrieval and validation of land surface temperature (LST) from NOAA AVHRR thermal images of Gujarat,India

A study was undertaken to retrieve land (soil-vegetation complex) surface temperature (LST) over a 100 km 2 100 km area in Gujarat (India) using thermal bands (channel 4 and 5) and estimated emissivity from atmospherically corrected NDVI, derived from NOAA-14 AVHRR data. The LST values were compared with near synchronous soil and air temperature measurements over five sites in December and May 1997 during Land Surface Processes Experiment (LASPEX) in Gujarat, India. The estimated LST of a semi-arid mixed agricultural barren landscape at 10.00 GMT was found to vary from 302 to 305.6 K on 13 December 1997 (winter) and from 317.5 to 328.5 K at 08.30 GMT on 15 May 1997 (Summer). During December, the LST values were near midway between air temperature (AT) and soil surface temperature (ST) with mean bias of m 2.9 K and 7.0 K respectively. However, in May, the LST values were found to be closer to ST, which may be due to lower fractional vegetation cover and NDVI.     

Estimation of daily evapotranspiration using a two‐layer remote sensing model

A two‐layer remote sensing model for estimating daily evapotranspiration over a large area was developed. The model followed an energy balance approach, where evapotranspiration is estimated as a residual when net radiation, sensible heat flux and ground flux are known. The accuracy of the model outputs was determined using harmonized surface conditions derived from measurements from ground stations. An accurate agreement (r ²?=?0.844, n?=?48) between estimates and ground‐based measurement was obtained.     

A similarity method to derive turbulence parameters and mixed-layer depth from sodar measurements

In this paper a summary is given of a similarity method which enables an indirect estimation of mixed-layer depth, surface heat flux and friction velocity from sodar measurements of the mean horizontal wind, and the vertical velocity variance. The results are compared with direct measurements from the Oresund experiment, which was carried out during the period 15 May-14 June 1984.

The similarity method is intended for use in the convective planetary boundary layer     

A method to derive texture-relief from ASTER bands 3N and 3B

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard the TERRA satellite generates a pair of bands from which a stereoscopic view can be derived. These bands, called 3N and 3B, correspond to the spectral range 0.78–0.86 μm. In this work, a method based on principal component analysis (PCA) was used to derive, from these bands, the texture-relief of the terrain. We compare our method with a digital elevation model (DEM) obtained from an algorithm that takes advantage of the stereoscopic view. Two examples are presented and discussed in detail.     

Estimation of particulate matter from satellite- and ground-based observations over Hyderabad,India

Long-term trends in surface-level particulate matter of dynamic diameter ≤2 µm (PM₂) in regard to air quality observations over Greater Hyderabad Region (GHR), India are estimated by the synergy of ground-based measurements and satellite observations during the period 2001–2013 (satellite) and July 2009–Dec 2013 (ground-based). Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical thickness (AOT) (MODIS-AOTs) was validated against that measured from Microtops-II Sunphotometer (MTS) AOTs (MTS-AOTs) and then utilized to estimate surface-level PM₂ concentrations over GHR using regression analysis between MODIS-AOTs, MTS-AOTs, and measured PM₂. In general, the MODIS-estimated PM₂ concentrations fell within the uncertainty of the measurements, thus allowing the estimate of PM₂ from MODIS, although in some cases they differed significantly due to vertical heterogeneity in aerosol distribution and the presence of distinct elevated aerosol layers of different origin and characteristics. Furthermore, significant spatial and temporal heterogeneity in the AOT and PM₂ estimates is observed in urban environments, especially during the pre-monsoon and monsoon seasons, which reduces the accuracy of the PM₂ estimates from MODIS. The estimates of PM₂ using MTS or MODIS-AOT exhibit a root mean square deference (RMSD) of about 8–16% against measured PM₂ on a seasonal basis. Furthermore, a tendency of increasing PM₂ concentrations is observed, which however is difficult to quantify for urban areas due to uncertainties in PM₂ estimations and gaps in the data set. Examination of surface and columnar aerosol concentrations, along with meteorological parameters from radiosonde observations on certain days, reveals that changes in local emissions and boundary-layer dynamics, and the presence or arrival of distinct aerosol plumes aloft, are major concerns in the accurate estimation of PM₂ from MODIS, while the large spatial distribution of aerosol and pollutants in the urban environment makes such estimates a considerable challenge.     

Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India

This paper evaluates the ability of wavelet transform in improving the accuracy of artificial neural network (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) models. In this study, the performance of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating daily evapotranspiration in arid regions was evaluated. Prior to the development of models, gamma test was used to identify the best input combinations that could be used under limited data scenario. Performance of the proposed hybrid models was compared to ANN, ANFIS, and conventionally used Hargreaves equation. The results revealed that use of wavelet transform as data preprocessing technique enhanced the efficiency of ANN and ANFIS models. Wavelet-ANN and Wavelet-ANFIS performed reasonably better than other models. Better handling of wavelet-decomposed input variables enabled Wavelet-ANN models to perform slightly better than the Wavelet-ANFIS models. W-ANN2 (RMSE = 0.632 mm/day and R = 0.96) was found to be the best model for estimating daily evapotranspiration in arid regions. The proposed W-ANN2 model used second-level db3 wavelet-decomposed subseries of temperature and previous day evapotranspiration values as inputs. The study concludes that hybrid Wavelet-ANN and Wavelet-ANFIS models can be effectively used for modeling evapotranspiration.

     

Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains

Evapotranspiration (ET) cannot be measured directly from satellite observations but remote sensing can provide a reasonably good estimate of evaporative fraction (EF), defined as the ratio of ET and available radiant energy. It is feasible to estimate EF using a contextual interpretation of radiometric surface temperature (T_o) and normalized vegetation index (NDVI) from multiple satellites. Recent studies have successfully estimated net radiation (R_n) over large heterogeneous areas for clear sky days using only remote sensing observations. With distributed maps of EF and R_n, it is now possible to explore the feasibility and robustness of ET estimation from multiple satellites. Here we present the results of an extensive inter-comparison of spatially distributed ET and related variables (NDVI, T_o, EF and R_n) derived from MODIS and AVHRR sensors onboard EOS Terra, NOAA14 and NOAA16 satellites respectively. Our results show that although, NDVI and T_o differ with the sensor response functions and overpass times, contextual space of NDVI-T_o diagram gives comparable estimates of EF. The utility of different sensors is demonstrated by validating the estimated ET results to ground flux stations over the Southern Great Plains with a root mean square error of 53, 51 and 56.24 Wm^− 2, and a correlation of 0.84, 0.79 and 0.77 from MODIS, NOAA16 and NOAA14 sensors respectively.     

Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China

Daily actual evapotranspiration over the upper Chao river basin in North China on 23 June 2005 was estimated based on the Surface Energy Balance Algorithm for Land (SEBAL), in which the parameterization schemes for calculating the instantaneous solar radiation and daily integrated radiation were improved by accounting for the variations in slope and azimuth of land surface and terrain shadow in mountainous areas. The evapotranspiration (ET) estimated from satellite data in this study for the whole watershed ranges from 0 mm to 7.3 mm day^?1 with a mean of 3.4 mm day^?1, which was validated by Penman–Monteith approaches for water body and paddy land. The comparison of ET estimates for a wide range of land cover types reflected distinct mechanisms of energy partition and water removal of various land cover types, showing differences in the spatial distribution pattern of ET, which could be not only the reflection but also the driving force of advection and local circulation that may violate the surface energy balance equation in the vertical direction. The spatial variation in daily solar radiation and ET estimates under the complex terrain of forest land were elaborated and evaluated by exploring the relationship between ET estimates and elevations for wood land and grass land. In addition, the utility and limitations of SEBAL's applicability to watersheds with various land cover types and complex terrain were analysed.     

Canonical transformations used to derive robot control laws from a port-controlled Hamiltonian system perspective

This paper addresses the problem of deriving control laws for robot manipulators in the framework of port-controlled Hamiltonian systems via canonical transformations and passivity-based control. The control design is focused on the presentation of a new energy-shaping methodology for tracking control based on the introduction of virtual non-homogeneous fields where a desired energy is defined to compensate for the actual energy of the robot manipulator while a virtual field forces the system to track a general reference trajectory. This requires use of the Legendre-Fenchel transformation and allows for the derivation standard control laws in the robotics field such as PD control with gravity compensation or PD with precompensation. Finally, the passivity of the input-output mapping of the non-autonomous Hamiltonian system is analyzed in detail, resulting in new Lyapunov candidate functions having their roots in physics.     

The latent learning model to derive semantic relations of words from unstructured text data in social media

Multimedia Tools and Applications - Unstructured text data is very important in many applications because it reflects the thought of the people who create this data. However, it is difficult to...