遥感辅助的农作物种植面积小域估计方法研究

遥感影像是大数据的一种,利用遥感对农作物播种面积进行估算常采用回归估计量或校准估计量,通常都需要将地面样本数据与遥感分类信息相结合。但对于大多数回归估计量,对省级总体的农作物面积估算只能满足对省级总体的精度要求而不能分解到更小区域,比如县和乡级。本文利用黑龙江省2011年的地面实测样本数据结合遥感分类结果,构建了单元层次的多响应变量的多元回归形式的小域模型,并将小域效应设定为固定形式。这样基于回归估计方法,既可以估算分县的主要作物播种面积,也可以使得各县播种面积估计结果相加就等于回归模型含义下的省级总体的总量估计。对黑龙江省玉米、水稻、大豆分县小域估计结果的精度评价（变异系数C.V）,平均而言均可以满足县级精度要求。本文的结果表明小域估计方法在解决省级总体对全省和分县的农作物种植面积多级估算问题中具有很好的应用。     

Regression Estimation of Crop Acreages With Transformed Landsat Data as Auxiliary Variables

Methods are developed for combining data collected by satellite with data collected in an area survey to estimate crop acreages. The basic procedure is that of survey regression estimation. Two methods of transforming the satellite information prior to regression estimation are compared.     

A note on the estimation methods of crop production at the block level

This paper presents a method of estimation of crop-production statistics at smaller geographical levels like a community development block (generally referred to as a block) to make area-specific plans for agricultural development programmes in India. Using available district-level data on crop yield from crop-cutting experiments and data on auxiliary variables from various administrative sources, a suitable regression model is fitted. The fitted model is then used to predict the crop production at the block level. Some scaled estimators are also developed using predicted estimates. An empirical study is also carried out to judge the merits of the proposed estimators.     

Statistics,images, and pattern recognition

Data are increasingly being collected in the form of images, especially in fields using remote sensing and microscopy. Statisticians are becoming interested in developing techniques to handle the highly structured data of images. Statistical work in this area is surveyed, and two problems discussed in more detail. The first is a form of image segmentation, classifying the pixels of a satellite picture by land use. The second is the summarization of electron micrographs.     

Robust small area estimation

Small area estimation has received considerable attention in recent years because of growing demand for small area statistics. Basic area‐level and unit‐level models have been studied in the literature to obtain empirical best linear unbiased prediction (EBLUP) estimators of small area means. Although this classical method is useful for estimating the small area means efficiently under normality assumptions, it can be highly influenced by the presence of outliers in the data. In this article, the authors investigate the robustness properties of the classical estimators and propose a resistant method for small area estimation, which is useful for downweighting any influential observations in the data when estimating the model parameters. To estimate the mean squared errors of the robust estimators of small area means, a parametric bootstrap method is adopted here, which is applicable to models with block diagonal covariance structures. Simulations are carried out to study the behaviour of the proposed robust estimators in the presence of outliers, and these estimators are also compared to the EBLUP estimators. Performance of the bootstrap mean squared error estimator is also investigated in the simulation study. The proposed robust method is also applied to some real data to estimate crop areas for counties in Iowa, using farm‐interview data on crop areas and LANDSAT satellite data as auxiliary information. The Canadian Journal of Statistics 37: 381–399; 2009 © 2009 Statistical Society of Canada     

The use of landsat for county estimates of crop areas: evaluation of the huddleston-ray and the battese-fuller estimators for & the case of stratified sampling

The paper addresses the problem of using LANDSAT data to obtain estimates of crop areas at the county level. In the paper, LANDSAT data are used to supplement ground data collected in a nationwide agricultural survey. The paper extends the Battese-Fuller estimation model to a stratified sample design. The resulting estimator is evaluated on a six county area in South Dakota     

The use of power transformations in small area estimation

Sample surveys are usually designed and analysed to produce estimates for larger areas. Nevertheless, sample sizes are often not large enough to give adequate precision for small area estimates of interest. To overcome such difficulties, borrowing strength from related small areas via modelling becomes essential. In line with this, we propose components of variance models with power transformations for small area estimation. This paper reports the results of a study aimed at incorporating the power transformation in small area estimation for improving the quality of small area predictions. The proposed methods are demonstrated on satellite data in conjunction with survey data to estimate mean acreage under a specified crop for counties in Iowa.     

农作物产量保险区域化差别费率厘定的可行性——基于非参数核密度估计实证

采用非参数核函数平滑法以辽宁省、黑龙江省以及大连市的水稻、玉米和大豆三种农作物历年单位面积产量为例拟合了单产损失分布,同时利用传统的正态概率密度对区域作物单产分布进行了拟合。在拟合损失分布的基础上,分别厘定出不同保险水平农作物区域产量保险的纯保险费率。经测算发现,传统的正态概率密度下厘定的纯保险费率均低于非参数核密度下测算的纯费率,正态法低估了农作物单产的风险。保险水平在70%⁸0%间的参数法及非参数法测算的纯保险费率均低于政策性农业保险的现行费率。另外,在数据可得的基础上,还应该确定适当的厘定保费费率的区域以充分识别风险,更精确的计算保费。     

Exploring spatial dependence in area-level random effect model for disaggregate-level crop yield estimation

This paper describes an application of small area estimation (SAE) techniques under area-level spatial random effect models when only area (or district or aggregated) level data are available. In particular, the SAE approach is applied to produce district-level model-based estimates of crop yield for paddy in the state of Uttar Pradesh in India using the data on crop-cutting experiments supervised under the Improvement of Crop Statistics scheme and the secondary data from the Population Census. The diagnostic measures are illustrated to examine the model assumptions as well as reliability and validity of the generated model-based small area estimates. The results show a considerable gain in precision in model-based estimates produced applying SAE. Furthermore, the model-based estimates obtained by exploiting spatial information are more efficient than the one obtained by ignoring this information. However, both of these model-based estimates are more efficient than the direct survey estimate. In many districts, there is no survey data and therefore it is not possible to produce direct survey estimates for these districts. The model-based estimates generated using SAE are still reliable for such districts. These estimates produced by using SAE will provide invaluable information to policy-analysts and decision-makers.     

小域估计方法在小品种农作物播种面积统计中的应用

 我国以省级目标总体开展的现行农产量抽样调查,着眼于农作物主要品种的省级推算,而小品种农作物的总体分布比较偏态,往往有效样本量相对不足,不能解决小品种农作物播种面积的推算问题,同时对分县的主要品种农作物播种面积进行的直接推算也不能满足精度要求。现阶段对小品种农作物播种面积的统计方法研究成为农村统计方法制度改革迫切需要研究的课题之一。本文选择了河北省张家口的蔚县,利用小域估计方法对小品种农作物播种面积进行了统计推断,从推断结果看得到了比较好的估计精度。利用蔚县为总体的实际数据进行的抽样仿真分析,从实证的角度阐述了小域估计方法对这一问题的有效性,而且分析结果也表明该方法可以显著提高估计效果。     

Estimating time to detect time trends in continuous cropping

SUMMARY In long-term field trials comparing different sequences of crops and husbandry practices, the identification and understanding of trends in productivity over time is an important issue of sustainable crop production. This paper presents a statistical technique for the estimation of time trends in yield variables of a seasonal annual crop under continuous cropping. The estimation procedure incorporates the correlation structure, which is assumed to follow first-order autocorrelation in the errors that arise over time on the same plot. Because large differences in annual rainfall have a major effect on crop performance, rainfall has been allowed for in the estimation of the time trends. Expressions for the number of years (time) required to detect statistically significant time trends have been obtained. Illustrations are based on a 7-year data set of grain and straw yields from a trial in northern Syria. Although agronomic interpretation is not intended in this paper, the barley yield data indicated that a significant time trend can apparently be detected even in a suboptimal data set of 7 years' duration.     

Varying-time random effects models for longitudinal data: unmixing and temporal interpolation of remote-sensing data

Remote sensing is a helpful tool for crop monitoring or vegetation-growth estimation at a country or regional scale. However, satellite images generally have to cope with a compromise between the time frequency of observations and their resolution (i.e. pixel size). When concerned with high temporal resolution, we have to work with information on the basis of kilometric pixels, named mixed pixels, that represent aggregated responses of multiple land cover. Disaggreggation or unmixing is then necessary to downscale from the square kilometer to the local dynamic of each theme (crop, wood, meadows, etc.).

Assuming the land use is known, that is to say the proportion of each theme within each mixed pixel, we propose to address the downscaling issue through the generalization of varying-time regression models for longitudinal data and/or functional data by introducing random individual effects. The estimators are built by expanding the mixed pixels trajectories with B-splines functions and maximizing the log-likelihood with a backfitting-ECME algorithm. A BLUP formula allows then to get the ‘best possible’ estimations of the local temporal responses of each crop when observing mixed pixels trajectories. We show that this model has many potential applications in remote sensing, and an interesting one consists of coupling high and low spatial resolution images in order to perform temporal interpolation of high spatial resolution images (20 m), increasing the knowledge on particular crops in very precise locations.

The unmixing and temporal high-resolution interpolation approaches are illustrated on remote-sensing data obtained on the South-Western France during the year 2002.     

Bayesian analysis of the scatterometer wind retrieval inverse problem: some new approaches

Summary.  The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem. A common approach to solving inverse problems is to adopt a Bayesian framework and to infer the posterior distribution of the parameters of interest given the observations by using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters. We show how Gaussian process priors can be used efficiently with a variety of likelihood models, using local forward (observation) models and direct inverse models for the scatterometer. We present an enhanced Markov chain Monte Carlo method to sample from the resulting multimodal posterior distribution. We go on to show how the computational complexity of the inference can be controlled by using a sparse, sequential Bayes algorithm for estimation with Gaussian processes. This helps to overcome the most serious barrier to the use of probabilistic, Gaussian process methods in remote sensing inverse problems, which is the prohibitively large size of the data sets. We contrast the sampling results with the approximations that are found by using the sparse, sequential Bayes algorithm.     

Estimation of land equivalent ratios

The estimation of the land equivalent ratios is proposed to be done by the (sum of) ratios of means of intercrop yield to sole crop yield. The bias and standard error of the estimates are obtained for large samples. Comparisons of the cropping systems have been made on the basis of these estimates and illustrated with field data.     

The Bayesian choice of crop variety and fertilizer dose

Recent contributions to the theory of optimizing fertilizer doses in agricultural crop production have introduced Bayesian ideas to incorporate information on crop yield from several environments and on soil nutrients from a soil test, but they have not used a fully Bayesian formulation. We present such a formulation and demonstrate how the resulting Bayes decision procedure can be evaluated in practice by using Markov chain Monte Carlo methods. The approach incorporates expert knowledge of the crop and of regional and local soil conditions and allows a choice of crop variety as well as of fertilizer level. Alternative dose–response functions are expressed in terms of a common interpretable set of parameters to facilitate model comparisons and the specification of prior distributions. The approach is illustrated with a set of yield data from spring barley nitrogen–response trials and is found to be robust to changes in the dose–response function and the prior distribution for indigenous soil nitrogen.     

Small area estimation of the mean using non-parametric M-quantile regression: a comparison when a linear mixed model does not hold

The demand for reliable statistics in subpopulations, when only reduced sample sizes are available, has promoted the development of small area estimation methods. In particular, an approach that is now widely used is based on the seminal work by Battese et al. [An error-components model for prediction of county crop areas using survey and satellite data, J. Am. Statist. Assoc. 83 (1988), pp. 28–36] that uses linear mixed models (MM). We investigate alternatives when a linear MM does not hold because, on one side, linearity may not be assumed and/or, on the other, normality of the random effects may not be assumed. In particular, Opsomer et al. [Nonparametric small area estimation using penalized spline regression, J. R. Statist. Soc. Ser. B 70 (2008), pp. 265–283] propose an estimator that extends the linear MM approach to the case in which a linear relationship may not be assumed using penalized splines regression. From a very different perspective, Chambers and Tzavidis [M-quantile models for small area estimation, Biometrika 93 (2006), pp. 255–268] have recently proposed an approach for small-area estimation that is based on M-quantile (MQ) regression. This allows for models robust to outliers and to distributional assumptions on the errors and the area effects. However, when the functional form of the relationship between the qth MQ and the covariates is not linear, it can lead to biased estimates of the small area parameters. Pratesi et al. [Semiparametric M-quantile regression for estimating the proportion of acidic lakes in 8-digit HUCs of the Northeastern US, Environmetrics 19(7) (2008), pp. 687–701] apply an extended version of this approach for the estimation of the small area distribution function using a non-parametric specification of the conditional MQ of the response variable given the covariates [M. Pratesi, M.G. Ranalli, and N. Salvati, Nonparametric m-quantile regression using penalized splines, J. Nonparametric Stat. 21 (2009), pp. 287–304]. We will derive the small area estimator of the mean under this model, together with its mean-squared error estimator and compare its performance to the other estimators via simulations on both real and simulated data.     

Statistical estimation of time-trends in two-course crop rotations

An assessment of time-trends in yield parameters is essential to the utilization of data from long-term field trials for the comparison of different crop rotations and input regimes, and the identification of sustainable production systems. The barley-vetch rotation established at Breda in northern Syria has provided the basis for estimation of the time-trends in yield data from selected treatments in a two-course crop rotation trial. The model used for the estimation accounts for the effect of rainfall, a major determinant of each annual yield value, and the first-order autocorrelation structure in the errors arising from the same plot over time. An expression for the minimum number of cycles required to detect a significant time-trend has been obtained. Results from the barley-vetch rotation under two fertilizer regimes have been discussed.     

遥感和空间分析支持下的耕地、城镇动态变化空间采样方法研究

一、引言我国是一个地域广阔、资源类型多样、土地利用类型和土地利用结构复杂的国家,同时,又是一个发展中的国家,正处在工业化的过程中,土地资源的利用类型变化巨大,尤其是和国民经济发展密切相关的耕地、城镇等重要利用类型的动态变化程度极高,而国民经济的高速发展迫切要求及时掌握耕地、城镇的动态变化信息。如何获取耕地、城镇的动态变化尤其是年际变化数据,不仅涉及到相关技术手段,同时也涉及到各项社会经济指标,是一项复杂的综合工程。由于现阶段我国特有的社会发展状况以及复杂多变的自然条件,使得采用传统方法需要大量的…     

Physically constrained spatiotemporal modeling: generating clear-sky constructions of land surface temperature from sparse,remotely sensed satellite data

Satellite remote-sensing is used to collect important atmospheric and geophysical data at various spatial resolutions, providing insight into spatiotemporal surface and climate variability globally. These observations are often plagued with missing spatial and temporal information of Earth''s surface due to (1) cloud cover at the time of a satellite passing and (2) infrequent passing of polar-orbiting satellites. While many methods are available to model missing data in space and time, in the case of land surface temperature (LST) from thermal infrared remote sensing, these approaches generally ignore the temporal pattern called the ‘diurnal cycle’ which physically constrains temperatures to peak in the early afternoon and reach a minimum at sunrise. In order to infill an LST dataset, we parameterize the diurnal cycle into a functional form with unknown spatiotemporal parameters. Using multiresolution spatial basis functions, we estimate these parameters from sparse satellite observations to reconstruct an LST field with continuous spatial and temporal distributions. These estimations may then be used to better inform scientists of spatiotemporal thermal patterns over relatively complex domains. The methodology is demonstrated using data collected by MODIS on NASA''s Aqua and Terra satellites over both Houston, TX and Phoenix, AZ USA.     

A survey of statistical methods on remote sensing in russian literature from 1976 to 1985

In this article we review the major areas of remote sensing in the Russian literature for the period 1976 to 1985 that use statistical methods to analyze the observed data. For each of the areas, the problems that have been studied and the statistical techniques that have been used are briefly described