基于DEM的遥感数据复原方法研究

介绍了一种基于数字高程模型（DEM）的遥感数据复原新方法。此方法将地形因子作为最主要的作用因子，不考虑卫星传感过程中的随机影响。首先，根据基础地理数据，按其等高线层生成DEM； 然后，利用DEM，通过实测样点、DEM和经过纠正的遥感数据的信息融合，进行遥感数据中像元样点的坡度、坡向分析，建立DEM与遥感信息的相关关系模型，以数学统计方法描述地形因子对遥感数据的作用机理； 最后，进行逐像元的遥感信息复原(归一化)。结果表明，该方法具有较好的信息复原效果，可消除或减少地形对遥感数据的影响，增强遥感技术在山区复杂地形下的实用性。     

应用数字图像处理技术处理数字高程模型数据和与数字高程模型数据相结合的遥感信息

本文介绍了数字图像处理技术在处理数字高程模型数据和遥感信息与数字高程模型数据相结 合的综合分析方面的应用；说明了实现人工阴影和人工立体观察像对的方法的基本原理及其在处 理数字高程模型数据上的应用；介绍了为提高遥感图像的可解译性,把遥感信息与数字高程模型 相结合,制作遥感图像的人工立体观察像对和立体透视图像的方法技术。     

多源遥感与作物模型同化模拟作物物生长参数时空域连续变化

本文将遥感信息与作物模型同化实现作物生长参数的时空域连续模拟,进而监测生长参数的时空域变化.首先将作物模型WOFOST(World food studies) 与冠层辐射传输模型PROSAIL 耦合构建WOPROSAIL 模型,利用微粒群算法(PSO) 通过最小化从CCD 数据获取的土壤调节植被指数观测值SAVI(soil adjusted vegetation index) 与耦合模型得到的模拟值SAVI’之间差值优化作物模型初始参数.通过MODIS 数据反演实现参数的区域化,并将区域参数作为优化后作物模型输入参数驱动模型逐像元计算生长参数,实现生长参数的时空域连续模拟与监测,最终建立区域尺度遥感-作物模拟同化框架模型RS-WOPROSAIL .结果表明：同化模型解决了作物模型模拟空间域和遥感信息时间域的不连续问题.模型模拟的叶面积指数(LAI) 、穗重(WSO) 、地上总生物量(TAGP) 等生长参数较好地体现了水稻生长状况时空域变化,研究区水稻模拟产量与实际产量的误差为27.4% .     

遥感技术和地理信息系统在神府-东胜地区环境综合评价中的应用

简要介绍了遥感和地理信息系统在神府—东胜地区环境综合评价中的应用方法。针对这一地区的环境特点,建立了以遥感信息为基础的环境综合评价体系。该体系包括环境压力负荷、环境问题和环境潜力等3个方面的10个因子,运用层次分析法结合专家知识,实现了全部因子的权重排序。以GIS为技术支撑,对各专题进行了时空变化分析,建立环境质量综合评价模型。详细客观地评价了该区的环境质量情况.     

森林火灾后生态变化遥感监测评价模型的构建方法研究

通过攀西林区云南松林的一系列旧火烧迹地的更新恢复和生态变化的遥感调查，对各生态因子的空间分布特征及生态变化的影响规律进行分析，确定了评价因子（变量）及其评价标准，利用遥感信息，以及地形、土壤、林分和林木受害程度等要素的８个因子的模糊综合评判结果和火烧年限等为变量，通过多组数据的多元统计分析，建成森林火灾后生态变化遥感监测评价模型，经野外调查结果验证分析，达到了预期的攻关目标。为使该模型能适应森林生态遥感监测运行系统的需要，对各监测因子数据的获取、植被指数的提取等方面进行了深入的方法探索。     

GPT／2模型用于 GPS 大气可降水汽反演的精度分析

气象参数(温度T、气压P)是GPS大气可降水汽(PWV)反演中必不可少的数据,也是PWV反演的重要误差源之一。文中主要对GPT/2(GPT、GPT2)模型用于PWV反演的精度进行验证和分析。基于非差精密单点定位(PPP)技术,选取SuomiNet网9个测站的观测数据,借助研制的PPP软件,分别采用GPT模型、改进的GPT2模型以及测站实测气象数据进行大气可降水汽(PWV)反演。以实测气象数据处理结果为参考,对两种模型解算的PWV进行了对比和精度分析。结果表明:改进的GPT2模型优于GPT模型,尤其是当测站的高程较大时,GPT2模型的稳定性更优、适用性更广;采用GPT2模型解算的PWV偏差均值小于±1.0mm,精度(RMS)优于±1.5mm。在缺少实测气象数据的情况下,利用GPT2模型数据仍然能够取得较为理想的PWV反演结果。     

Lidar遥感基本原理及其发展

介绍了航空激光扫描(Airborne laser scanning)或者Lidar遥感信息获取系统的基本原理、系统的组成、数据获取的方法及其步骤;对近数十年来应用激光扫描遥感信息获取地形表面模型方面取得的主要成果、应用现状做了简要回顾和评述;结合GIS和影像融合方法对Lidar遥感技术未来发展趋势进行了展望。     

Lidar遥感基本原理及其发展

介绍了航空激光扫描(Airborne laser scanning)或者Lidar遥感信息获取系统的基本原理、系统的组成、数据获取的方法及其步骤;对近数十年来应用激光扫描遥感信息获取地形表面模型方面取得的主要成果、应用现状做了简要回顾和评述;结合GIS和影像融合方法对Lidar遥感技术未来发展趋势进行了展望.     

GPS遥感大气综合水汽含量局部区域模型与通用模型结果的比较分析

为进一步提高利用GPS遥感大气综合水汽含量的精度,部分学者提出了利用当地探空气象资料建立局部区域模型,以改善采用通用模型计算综合水汽含量结果的观点。为此本文就局部区域模型和通用模型反演的水汽结果进行了比较分析,得出了一些重要的结论。     

用户模型驱动的遥感信息智能服务方法

传统的浏览下载的遥感信息服务模式存在被动性、同一性等缺点,如何主动、准确地满足用户的个性化需求已成为遥感信息服务的焦点问题。本文针对遥感信息在空间和波谱上的覆盖特性,引入区间数学的方法建立了用户模型,描述用户兴趣在遥感信息核心元数据上的分布特征。提出关联度和兴趣度概念用来评价遥感信息对用户兴趣的满足程度,设计了基于拓扑关系的关联函数定量计算关联度。通过将待分发遥感信息作为备选方案构建了决策矩阵,从而将遥感信息的智能服务问题转化为多属性决策问题,实现了面向用户兴趣的遥感信息主动推荐。     

陆面温度反演算法——劈窗算法的敏感度分析

首先给出ＣＯ２ 倍增下遥感光合作物产量的概念模型,之后分析未受ＣＯ２ 倍增的遥感光合作物产量估测模型;在考虑ＣＯ２ 倍增对作物产量的影响后,对影响干物质累积的作物光合速率的模型进行修正,进而修正遥感光合作物产量估测模型。建立ＣＯ２ 倍增下作物产量响应模型,求取各参数,并在ＣＯ２ 倍增下对我国华北地区冬小麦产量响应进行填图,表明模型的估测结果有良好的可比性。     

农作物单产预测的运行化方法

提出了适于运行化农作物单产预测的方法。即以农作物单产区划为基础 ,通过搜集不同地区不同作物的单产预测模型 ,分析每个模型的空间适用范围 ,并从模型参数等角度筛选模型 ,然后利用这些模型进行气象站点的作物单产预测 ,并以NDVI分布图为参考数据将点上的单产数据空间外推到区域尺度。借助耕地分布估计区域水平的农作物单产。最后以 2 0 0 3年冬小麦为例 ,进行了全国 10个省的冬小麦平均单产估算 ,花费了较少的人力和时间 ,符合运行化遥感估产要求     

Monitoring winter wheat growth in North China by combining a crop model and remote sensing data

Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL–PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation.     

面向农作物监测的遥感信息处理技术研究

以区域性主要农作物种类识别、长势分析与产量估算及农业种植结构现状监测为主要研究对象,开展适于农业管理部门业务化运行的卫星遥感信息处理的关键技术研究。从分析主要作物类型识别的遥感物理依据入手,提出了卫星遥感数据处理及专题信息提取的基本技术框架、主要农作物类型及种植面积信息的提取方法以及主要粮食作物长势分析和产量估算模型,并对结果进行了简要的精度分析。     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.     

Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).     

A simple assessment approach for winter wheat loss risk impacted by water stress

In this study, an empirical assessment approach for the risk of crop loss due to water stress was developed and used to evaluate the risk of winter wheat loss in China, the United States, Germany, France and the United Kingdom. We combined statistical and remote sensing data on crop yields with climate data and cropland distribution to model the effect of water stress from 1982 to 2011. The average value of winter wheat loss due to water stress for the three European countries was about ?931 kg/ha, which was higher than that in China (?570 kg/ha) and the United States (?367 kg/ha). Our study has important implications for the operational assessment of crop loss risk at a country or regional scale. Future studies should focus on using higher spatial resolution remote sensing data, combining actual evapotranspiration to estimate water stress, improving the method for downscaling of statistical crop yield data and establishing more sophisticated zoning methods.     

农情遥感信息与其他农情信息的对比分析

农情信息多种多样 ,来源不同 ,分散于各个部门或单位 ,缺乏相互交换与验证 ,综合分析与集成不够 ,特别是遥感信息为经济领域决策服务的渠道不通畅。为更好地应用各种信息 ,必须加强信息综合分析。对耕地面积、作物面积、作物单产、作物长势、粮食产量等几种农情信息中不同来源的信息进行了初步对比分析 ,肯定了遥感监测农情信息在客观性、时空连续性、可对比与可预测、低成本等几个方面的优势 ,同时也分析了遥感信息的不足和局限。认为遥感信息与其他信息不是互相替代的关系 ,而是互相补充、互相验证的关系。只有通过多源农情信息的综合分析和集成 ,才能更全面准确地反映农情。     

统计数据总量约束下全局优化阈值的冬小麦分布制图

大范围、长时间和高精度农作物空间分布基础农业科学数据的准确获取对资源、环境、生态、气候变化和国家粮食安全等问题研究具有重要现实意义和科学意义。本文针对传统阈值法农作物识别过程中阈值设置存在灵巧性差和自动化程度低等弱点,以中国粮食主产区黄淮海平原内河北省衡水市景县为典型实验区,首次将全局优化算法应用于阈值模型中阈值优化选取,开展了利用全局优化算法改进基于阈值检测的农作物分布制图方法创新研究。以冬小麦为研究对象,国产高分一号(GF-1)为主要遥感数据源,在作物面积统计数据为总量控制参考标准和全局参数优化的复合型混合演化算法SCE-UA (Shuffled Complex Evolution-University of Arizona)支持下,提出利用时序NDVI数据开展阈值模型阈值参数自动优化的冬小麦空间分布制图方法。最终,获得实验区冬小麦阈值模型最优参数,并利用优化后的阈值参数对冬小麦空间分布进行提取。通过地面验证表明,利用本研究所提方法获取的冬小麦识别结果分类精度均达到较高水平。其中冬小麦识别结果总量精度达到了99.99%,证明本研究所提阈值模型参数优化方法冬小麦提取分类结果总量控制效果良好;同时,与传统的阈值法、最大似然和支持向量机等分类方法相比,本研究所提阈值模型参数优化法区域冬小麦作物分类总体精度和Kappa系数分别都有所提高,其中,总体精度分别提高4.55%、2.43%和0.15%,Kappa系数分别提高0.12、0.06和0.01,这体现出SCE-UA全局优化算法对提高阈值模型冬小麦空间分布识别精度具有一定优势。以上研究结果证明了利用本研究所提基于作物面积统计数据总量控制以及SCE-UA全局优化算法支持下阈值模型参数优化作物分布制图方法的有效性和可行性,可获得高精度冬小麦作物空间分布制图结果,这对提高中国冬小麦空间分布制图精度和自动化水平具有一定意义,也可为农作物面积农业统计数据降尺度恢复重建和大范围区域作物空间分布制图研究提供一定技术参考。     

Winter wheat yield forecasting in Ukraine based on Earth observation,meteorological data and biophysical models

Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived from the MODIS sensor at the 250 m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in April–May provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 2–3 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in April–May that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches were run to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3 t ha⁻¹ in June and 0.4 t ha⁻¹ in April, while performance of three approaches for 2011 was almost the same (0.5–0.6 t ha⁻¹ in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest, while providing minimum requirements to input datasets.