Monitoring of rice crop using ENVISAT ASAR data

Rice is the most important food source for people and is cultivated in most countries, among which China is one of the most productive. Increase of the world population and demands for economic devel-opment lead to the need of an efficient monitoring system for rice cultivation and forecasting of rice yield. Conventional methods for rice monitoring are based on ground-collected statistics, which is time consum-ing, inaccurate and expensive. Since the 1980s, satel-lite remote sensing has been c…     

Contribution of climatic and technological factors to crop yield: empirical evidence from late paddy rice in Hunan Province,China

Climatic and technological factors are two remarkable aspects that are thought to contribute to crop yield change. However, the most significant factors and their contribution rate remain debatable. Selecting Hunan Province, which is one of the largest paddy rice producing regions in China as the research area, the marginal contributions of climatic and technological factors to late paddy yield change are estimated using a county-level panel data regression model with explicit consideration of technological variables during 2000–2011. The results indicate that the mean daily temperature and adoption rate of hybrid rice have significant positive impacts on late paddy rice yield. During the research period, 1 °C temperature increase will cause 7.220 t/km² increase in late paddy rice yield, and 1 % increase in the adoption rate of hybrid rice will cause 0.212 t/km² increase. Climatic factors, especially their annual variability, exhibit a stronger overall linkage to changes in late paddy rice yield in the study area compared with the technological factors. Climatic factors accounted for 67.645 and 74.585 % of the trend and variability in late paddy rice yield, respectively. The results of this study can provide valuable information for stakeholders to adjust the input on late paddy rice production and better adapt to the effect of climate change.     

The retrieval of two-dimensional distribution of the earth''''s surface aerodynamic roughness using SAR image and TM thermal infrared image

The research for the land surface fluxes has madea quiet great progress for its breakthroughs in the fieldof regional or global interactions between land surfaceand atmosphere. However, many remote sensing mod-els for estimating the land surface fluxes need the pa-rameters of surface momentum, heat, resistance ofwater vapor at a referenced height, which are the func-tion of aerodynamic surface roughness zad. It hasbeen validated that the retrieval of the land surfacefluxes is very sensitive to…     

Determination of regional distribution of crop transpiration and soil water use efficiency using quantitative remote sensing data through inversion

As is widely known, there is a severe shortage of water resources in North China. There have been frequent droughts in recent years. Developing water saving measures, especially in agricul-ture, has become an urgent task. In water-saving agriculture, one …     

An integrated model to simulate sown area changes for major crops at a global scale

Dynamics of land use systems have attracted much attention from scientists around the world due to their ecological and socio-economic implications. An integrated model to dynamically simulate future changes in sown areas of four major crops (rice, maize, wheat and soybean) on a global scale is pre- sented. To do so, a crop choice model was developed on the basis of Multinomial Logit (Logit) model to model land users' decisions on crop choices among a set of available alternatives with using a crop utility function. A GIS-based Environmental Policy Integrated Climate (EPIC) model was adopted to simulate the crop yields under a given geophysical environment and farming management conditions, while the International Food Policy and Agricultural Simulation (IFPSIM) model was utilized to estimate crop price in the international market. The crop choice model was linked with the GIS-based EPIC model and the IFPSIM model through data exchange. This integrated model was then validated against the FAO statistical data in 2001-2003 and the Moderate Resolution Imaging Spectroradiometer (MODIS) global land cover product in 2001. Both validation approaches indicated reliability of the model for ad- dressing the dynamics in agricultural land use and its capability for long-term scenario analysis. Finally, the model application was designed to run over a time period of 30 a, taking the year 2000 as baseline. The model outcomes can help understand and explain the causes, locations and consequences of land use changes, and provide support for land use planning and policy making.     

An explicit four-dimensional variational data assimilation method based on the proper orthogonal decomposition: Theoretics and evaluation

The proper orthogonal decomposition (POD) method is used to construct a set of basis functions for spanning the ensemble of data in a certain least squares optimal sense. Compared with the singular value decomposition (SVD), the POD basis functions can capture more energy in the forecast ensemble space and can represent its spatial structure and temporal evolution more effectively. After the analysis variables are expressed by a truncated expansion of the POD basis vectors in the ensemble space, the control variables appear explicitly in the cost function, so that the adjoint model, which is used to derive the gradient of the cost function with respect to the control variables, is no longer needed. The application of this new technique significantly simplifies the data assimilation process. Several assimilation experiments show that this POD-based explicit four-dimensional variational data assimilation method performs much better than the usual ensemble Kalman filter method on both enhancing the assimilation precision and reducing the computation cost. It is also better than the SVD-based explicit four-dimensional assimilation method, especially when the forecast model is not perfect and the forecast error comes from both the noise of the initial filed and the uncertainty of the forecast model. Supported by the National Natural Science Foundation of China (Grant No. 40705035), National High Technology Research and Development Program of China (Grant No. 2007AA12Z144), Knowledge Innovation Project of Chinese Academy of Sciences (Grant Nos. KZCX2-YW-217 and KZCX2-YW-126-2), and National Basic Research Program of China (Grant No. 2005CB321704)     

Assimilation of sea surface temperature into CECOM by flux correction

Sea surface temperature (SST) from a near real-time data set produced from satellites data has been assimilated into a coupled ice–ocean forecasting model (Canadian East Coast Ocean Model) using an efficient data assimilation method. The method is based on an optimal interpolation scheme by which SST is melded into the model through the adjustment of surface heat flux. The magnitude and space–time variation of the adjustment depend on the depth of heat diffusion into the water column in response to changes in surface flux, the correlation time scale of the data, and model and data errors. The diffusion depth is scaled by the eddy diffusivity for temperature. The ratio of the model and data errors is treated as an adjustable parameter. To evaluate the quality of the assimilation, the results from the model with and without assimilation are compared to independent ship data from the Atlantic Zone Monitoring Program and the World Ocean Circulation Experiment. It is shown that the assimilation has a significant impact on the modeled SST, reducing the root mean square difference (RMSD) between the model SST and the ship SST by 0.63°C or 37%. The RMSD of the assimilated SST is smaller than that of the satellite SST by 0.23°C. This suggests that model simulations or predictions with data assimilation can provide the best estimate of the true SST. A sensitivity study is performed to examine the change of the model RMSD with the adjustable parameter in the assimilation equation. The results show that there is an optimal value of the parameter and the model SST is not very sensitive to the parameter.     

巴姆地震地表形变的差分雷达干涉测量

利用星载合成孔径差分雷达干涉技术和ENVISAT卫星雷达数据, 获得了2003年12月26日伊朗巴姆的里氏6.5级地震引起的同震地表形变场,并详细地介绍了信号处理的过程. 利用地震前后的相干图的差异及形变场的突变棱线, 精确确定了地面上断层裂缝的位置、形状和长度. 这对震源理论模型的参数估计提供了依据. 由雷达干涉技术测量得到的形变场与理论模型模拟的结果一致.      

Fundamental Control Functions and Error Analysis in Variational Data Assimilation

The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition function. The equation for the error of the optimal solution (analysis) is derived through the errors of the input data (background and observation errors). The numerical algorithm is developed to compute the sensitivity coefficients for the analysis error using the fundamental control functions. Application to the variational data assimilation problem for a model of ocean thermodynamics is considered.     

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.     

Bam earthquake: Surface deformation measurement using radar interferometry

1 Basic principle of SAR and SAR-interferometry Synthetic aperture radar (SAR) is one kind of microwave side-looking imaging radar (Cur- lander and McDonough, 1991). In order to obtain an image for a large area, the carriers are many for the aerospace vehicle, like airplane, aerospace craft and satellite. As a result of its operational character, all-weather and high resolution, in the recent 20 years, SAR has obtained quicker de- velopment compared with an optical pickoff. Its applicati…     

尾波观测与Q值分布htt

本文依据K.Aki的单次回弹散射模式绘制了DD-1短周期地震仪的尾波包络理论量板,制定了尾波包络的综合观测方法,测定了江苏省地壳内介质的平均品质因子Q。观测表明,尾波包络曲线明显地由两个以上的分支组成。由其中一个分支得到的Q值分布与江苏的地震活动有较好的对应。      

Newtonian nudging for a Richards equation-based distributed hydrological model

The objective of data assimilation is to provide physically consistent estimates of spatially distributed environmental variables. In this study a relatively simple data assimilation method has been implemented in a relatively complex hydrological model. The data assimilation technique is Newtonian relaxation or nudging, in which model variables are driven towards observations by a forcing term added to the model equations. The forcing term is proportional to the difference between simulation and observation (relaxation component) and contains four-dimensional weighting functions that can incorporate prior knowledge about the spatial and temporal variability and characteristic scales of the state variable(s) being assimilated. The numerical model couples a three-dimensional finite element Richards equation solver for variably saturated porous media and a finite difference diffusion wave approximation based on digital elevation data for surface water dynamics. We describe the implementation of the data assimilation algorithm for the coupled model and report on the numerical and hydrological performance of the resulting assimilation scheme. Nudging is shown to be successful in improving the hydrological simulation results, and it introduces little computational cost, in terms of CPU and other numerical aspects of the model’s behavior, in some cases even improving numerical performance compared to model runs without nudging. We also examine the sensitivity of the model to nudging term parameters including the spatio-temporal influence coefficients in the weighting functions. Overall the nudging algorithm is quite flexible, for instance in dealing with concurrent observation datasets, gridded or scattered data, and different state variables, and the implementation presented here can be readily extended to any of these features not already incorporated. Moreover the nudging code and tests can serve as a basis for implementation of more sophisticated data assimilation techniques in a Richards equation-based hydrological model.     

Ocean wind fields retrieved from the advanced synthetic aperture radar aboard ENVISAT

In this paper an algorithm is presented which enables high-resolution ocean surface wind fields to be retrieved from the advanced synthetic aperture radar (ASAR) data acquired by the European remote sensing satellite ENVISAT. Wind directions are extracted from wind-induced streaks that are visible in ASAR images at scales above 200 m and that are approximately in line with the mean surface wind direction. Wind speeds are derived from the normalized radar cross section (NRCS) and image geometry of the calibrated ASAR images, together with the local ASAR-retrieved wind direction. Therefore the empirical C-band model CMOD4, which describes the dependency of the NRCS on wind and image geometry, is used. CMOD4 is a semi-empirical model, which was originally developed for the scatterometer of the European remote sensing satellites ERS-1 and 2 operating at C-band with vertical polarization. Consequently, CMOD4 requires modification when applied to ASAR images that were acquired with horizontal polarization in transmitting and receiving. This is performed by considering the polarization ratio of the NRCS. To demonstrate the applicability of the algorithm, wind fields were computed from several ENVISAT ASAR images of the North Sea and compared to atmospheric model results of the German weather service.Acknowledgements The authors were supported by the German Bundesministerium für Bildung und Forschung (BMBF) in the framework of the project. A new perspective of the Ocean ENVISAT Oceanography (ENVOC). The ENVISAT ASAR data were kindly made available by the European Space Agency in the framework of the ENVISAT Project AO-ID 220, Biological and geophysical parameters from synthetic aperture radar over the ocean (BIGPASO).     

Argo data assimilation in ocean general circulation model of Northwest Pacific Ocean

The Argo temperature and salinity profiles in 2005–2009 are assimilated into a coastal ocean general circulation model of the Northwest Pacific Ocean using the ensemble adjustment Kalman filter (EAKF). Three numerical tests, including the control run (CTL) (without data assimilation, which serves as the reference experiment), ensemble free run (EnFR) (without data assimilation), and EAKF experiment (with Argo data assimilation using EAKF), are carried out to examine the performance of this system. Using the restarts of different years as the initial conditions of the ensemble integrations, the ensemble spreads from EnFR and EAKF are all kept at a finite value after a sharp decreasing in the first few months because of the sensitive of the model to the initial conditions, and the reducing of the ensemble spread due to Argo data assimilation is not much. The ensemble samples obtained in this way can well represent the probabilities of the real ocean states, and no ensemble inflation is necessary for this EAKF experiment. Different experiment results are compared with satellite sea surface temperature (SST) data and the Global Temperature-Salinity Profile Program (GTSPP) data. The comparison of SST shows that modeled SST errors are reduced after data assimilation; the error reduction percentage after assimilating the Argo profiles is about 10?% on average. The comparison against the GTSPP profiles, which are independent of the Argo profiles, shows improvements in both temperature and salinity. The comparison results indicated a great error reduction in all vertical layers relative to CTL and the ensemble mean of EnFR; the maximum value for temperature and salinity reaches to 85?% and 80?%, respectively. The standard deviations of sea surface height are employed to examine the simulation ability, and it is shown that the mesoscale variability is improved after Argo data assimilation, especially in the Kuroshio extension area and along the section of 10°N. All these results suggest that this system is potentially useful for improving the simulation ability of oceanic numerical models.     

太湖水体散射特性及其空间分异

利用Wetlabs公司研制的AC-S和BB9于2006年10月24目至11月2目对太湖水体的散射系数和后向散射系数进行了测量,在此基础上建立了太湖水体散射系数与后向散射系数之间的关系模型。用2种曲线函数模拟,即在蓝光波段使用S形曲线模型,在绿光和红光波段使用逆函数模型,各模型的MAPE和RMSE变化范围分别为0.027-0.156m^-1、0.005-0.050m^-1,模型整体预测精度都较高。研究发现后向散射系数与散射系数的空间分异现象明显,北部梅梁湾、湖心区、西部及西南部水域散射较强,而东太湖、胥口湾等东部水域的散射相对较弱。     

Optimizing spectral wave estimates with adjoint-based sensitivity maps

A discrete numerical adjoint has recently been developed for the stochastic wave model SWAN. In the present study, this adjoint code is used to construct spectral sensitivity maps for two nearshore domains. The maps display the correlations of spectral energy levels throughout the domain with the observed energy levels at a selected location or region of interest (LOI/ROI), providing a full spectrum of values at all locations in the domain. We investigate the effectiveness of sensitivity maps based on significant wave height (H _s ) in determining alternate offshore instrument deployment sites when a chosen nearshore location or region is inaccessible. Wave and bathymetry datasets are employed from one shallower, small-scale domain (Duck, NC) and one deeper, larger-scale domain (San Diego, CA). The effects of seasonal changes in wave climate, errors in bathymetry, and multiple assimilation points on sensitivity map shapes and model performance are investigated. Model accuracy is evaluated by comparing spectral statistics as well as with an RMS skill score, which estimates a mean model–data error across all spectral bins. Results indicate that data assimilation from identified high-sensitivity alternate locations consistently improves model performance at nearshore LOIs, while assimilation from low-sensitivity locations results in lesser or no improvement. Use of sub-sampled or alongshore-averaged bathymetry has a domain-specific effect on model performance when assimilating from a high-sensitivity alternate location. When multiple alternate assimilation locations are used from areas of lower sensitivity, model performance may be worse than with a single, high-sensitivity assimilation point.     

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.     

Predicting future water demand for Long Xuyen Quadrangle under the impact of climate variability

Long Xuyen Quadrangle is one of the important agricultural areas of the Mekong Delta of Vietnam accounting for 25% of rice production. In recent years, the area faces drought and salinization problems, as part of climate change impact and sea level rise. These are the main causes that led to the crop water deficits for agricultural production. Therefore, this work was conducted to predict crop water requirement (CWR) based on consideration of other related meteorological factors and further redefine the crop planting calendar (CPC) for three main cropping seasons including winter–spring (WS), summer–autumn (SA) and autumn–spring (AS) using the Cropwat crop model based on the current climate conditions and future climate change scenarios. Meteorological data for the baseline period (2006–2016) and future corresponding to timescales 2020s, 2055s and 2090s of Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios are used to predict CWR and CPC for the study area. The results showed that WS and SA crops needed more irrigation water than AS crop and the highest irrigation water requirement of the WS and SA crops occurred on developmental stage, while the AW crop appeared on growth, developmental and late stage for the baseline and timescales of RCP4.5 and RCP8.5 scenarios. Calculation results of the shift of CPC indicated that the CWR of the AW crop decreased lowest approximately 6.6–20.6% for timescales of RCP4.5 scenario and 20.6–25.5% for RCP8.5 scenario compared with the baseline.     

Grain yield reliability analysis with crop water demand uncertainty

A new method of reliability analysis for crop water production function is presented considering crop water demand uncertainty. The procedure uses an advanced first-order second moment (AFOSM) method in evaluating the crop yield failure probability. To determine the variance and the mean of actual evapotranspiration as the component of interest for AFOSM analysis, an explicit stochastic optimization model for optimal irrigation scheduling is developed based on the first and second-order moment analysis of the soil moisture state variables. As a result of the study, the violation probabilities of crop yield at different levels were computed from AFOSM method. Also using the optimization results and the double bounded density function estimation methodology, the weekly soil moisture density function is derived which can be used as a short term reliability index. The proposed approach does not involve any discretization of system variables. The results of reliability analysis and optimization model compare favorably with those obtained from simulation.