共查询到19条相似文献，搜索用时 562 毫秒
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针对中国开展的国外农作物产量遥感估测大多依靠中低分辨率耕地信息、省级(州级)或国家级作物产量统计数据的现状,本文以美国玉米为例,探讨利用多年中高分辨率作物分布信息、时序遥感植被指数和县级作物产量统计数据开展国外重点地区作物单产遥感估测技术研究,以期进一步提高中国对国外农作物产量监测精度和精细化水平。首先,利用美国农业部国家农业统计局(NASS/USDA)生产的作物分布数据(CDL)获得多个年份玉米空间分布图,并对相应年份250 m分辨率16天合成的MODIS-NDVI时序数据进行掩膜处理,统计获得每年各县域内玉米主要生育期NDVI均值;其次,以各州为估产区,以多年县级玉米统计单产和县域内玉米主要生育期NDVI均值为基础,建立各州玉米主要生育期NDVI与玉米单产间关系模型;然后,通过主要生育期玉米单产和玉米植被指数间拟合程度,筛选确定各州玉米最佳估产期和最佳估产模型。最终,利用最佳估产模型实现美国各州玉米单产估测和全国玉米单产推算。其中,建模数据覆盖时间为2007年—2010年,验证数据为2011年。结果表明,应用最佳估产模型的2011年美国各州玉米单产估测相对误差在-4.16%—4.92%,均方根误差在148.75—820.93 kg/ha,各州估测结果计算获得全国玉米单产的相对误差仅为2.12%,均方根误差为285.57 kg/ha。可见,本研究的作物单产遥感估测技术方法具有一定可行性,可准确估测全球重点地区作物单产信息。 相似文献
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生物量估测模型中遥感信息与植被光合参数的关系研究 总被引：47，自引：0，他引：47
本文通过对建立估测植被生物量遥感模型中所涉及的遥感信息参数与植被光合参数的关系分析,从理论和实验中阐明了反映植物长势的量发化植被指数和反映植物光合面积的叶面积指数,光合有效辐射及吸收光合有效辐射的相互关系,对在实际中建立更为机理的生物量遥感模型提供可供进一步参考的依据。 相似文献
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遥感技术在主要粮食作物估产中的应用 总被引：3，自引：0，他引：3
文章分析了国内外遥感技术在主要粮食作物估产中应用现状,探讨了遥感技术在作物估产领域的研究进展,研究了作物气候产量预报模型、遗传算法结合神经网络模型、基于人机交互的反演模型、基于决策树分类的县域估产模型、单产估测模型、基于SCE_UA算法的CERES_Wheat模型、雷达遥感估产模型等在我国主要农作物估产中的应用;分析表明遥感关键技术及模型选择为农作物估产精度的提高提供了重要的技术支持.最后对作物估产遥感技术发展趋势及农业信息化相关技术做了展望,指出综合遥感与计算机技术开发自动化系统、推进物联网与遥感技术结合等问题,是进一步的研究趋势. 相似文献
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《国土资源遥感》2017,(4)
叶面积指数(leaf area index,LAI)作为植被冠层的重要参数,对作物长势监测及产量估算具有重要意义。本研究以黑河流域张掖绿洲试验区为例,基于机载航空高光谱遥感影像(compact airborne spectrographic imager,CASI)数据,利用物理模型与统计模型对研究区的LAI进行估测反演。首先,利用归一化植被指数(normalized difference vegetation index,NDVI)与相应实测LAI数据建立最佳线性回归模型;然后,基于混合像元分解模型和多次散射植被冠层模型构建物理模型;最后,以线性回归模型为参比修正多次散射植被冠层模型,构建半经验LAI反演模型,并比较上述模型拟合效果。研究结果表明,半经验模型为绿洲区LAI反演最优模型,模型估算精度R2达到0.89,精度提高较显著。研究对提升作物LAI的估算精度有一定意义,并将进一步推动精细农业定量遥感理论的研究与应用。 相似文献
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复杂背景下被动FTIS定量遥感污染云团光谱 总被引：5，自引：0，他引：5
本文提出复杂背景下定量遥感污染云团光谱的算法。利用被动傅里叶变换红外光谱仪（ＦＴＩＳ）遥测目标气体和背景光谱数据及最小平方误差拟合方法,成功地解决了探测目标气体柱数密度和目标气体等效辐射温度。该处理方法不仅可用于特定污染云团的定量处理,而且可用于多种污染地区如发电厂、机场、城市交叉路口、垃圾处理站等对ＣＯ,ＣＯ２,ＮＯ,ＮＯ２,Ｎ２Ｏ,ＮＨ３,ＣＨ４,ＳＯ２,Ｈ２Ｏ,ＨＣＩ和ＨＣＨＯ等气体浓度的监测。最后,详细讨论了用该算法对模拟剂的处理结果。 相似文献
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海洋水色CCD成像仪光谱非线性校正 总被引：1，自引：0，他引：1
个理想的光学遥感器,在响应波段内光谱响应度是完全平直的,遥感器的输出与入射的辐射成完全的线性关系;而实际的遥感器并非如此,它的光谱响应度在响应波段内并不是平直的,这就使遥感器的输出不仅与入射的辐射有关,还与遥感器的光谱响应度有关,实际测量时,由于目标光谱形状的差异会导致输入与输出之间的非线性关系。利用仪器定标时采用的光源光谱形状与实际测量时目标光谱形状对仪器的输出数据进行校正会有效地减小由于遥感器的光谱响应非均匀性引入的测量误差。文中利用测得的海洋水色ＣＣＤ成像仪的各波段光谱响应度,计算了已归一化为具有相同的带内平均光谱辐亮度的不同温度黑体输出,由输出结果可以看到,不同的光谱形状对海洋水色ＣＣＤ成像仪的输出影响较大,为了减小由此引入的测量误差,文中对实验室定标用积分球光源和海洋水色ＣＣＤ成像仪在轨工作时的典型输入辐亮度的光谱特性进行了等效黑体拟合,并以此为依据计算了海洋水色ＣＣＤ成像仪在轨工作时的光谱非线性修正因子。 相似文献
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遥感和生长模型相结合的小麦长势监测研究现状与展望 总被引：9，自引：0，他引：9
遥感影像的信息波段及其组合可以反映农作物生长的空间信息，可对小麦进行长势监测和产量估算，具有及时性和广域性。生长模型是集气候、土壤、品种和栽培措施等因素为一体的，对作物的物候发育、光合生产、器官建成、同化物积累与分配以及产量与品质形成等生理过程及其与环境和技术因子关系综合量化的动态数学模型，具有机理性和预测性。将二者结合用于长势监测不但具有理论研究价值，还具有广泛的应用前景。本文在简要概述小麦生长模型和长势遥感监测研究进展的基础上，总结了生长模型和遥感相结合的小麦长势监测应用的研究进展，并提出一些今后研究设想。 相似文献
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Sujay Dutta N K Patel S K. Srivastava 《Journal of the Indian Society of Remote Sensing》2001,29(3):175-182
Pre-harvest crop production forecast has been successfully provided by remote sensing technique. However, the probability to get cloud-free optical remote sensing data during kharif season is poor. Microwave data having the capability to penetrate cloud is used in the absence of cloud free optical remote sensing data. Yield models in broad band frequency range are in development stage. Meteorological yield models are developed and predicted yield is combined with area estimated by remote sensing data to provide rice production forecast. This paper describes the methodology adopted for improving the predictability of rice yield before harvest of the crop in Bihar province by taking into consideration meteorological parameters during its growth cycle upto October. Models developed using fortnightly meteorological data have been found to give reasonably fair indications of expected yield of rice in advance of harvest. The yield predictions have been made based on meteorological data and effective rainfall based on water requirement calculations representing a group of districts under similar agro-climatic zones, which could be further improved by incorporating meteorological data of individual districts within each group. 相似文献
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Monitoring winter wheat growth in North China by combining a crop model and remote sensing data 总被引：6，自引：0，他引：6
Ma Yuping Wang Shili Zhang Li Hou Yingyu Zhuang Liwei He Yanbo Wang Futang 《International Journal of Applied Earth Observation and Geoinformation》2008,10(4):426
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. 相似文献
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Ishfaq Ahmad Umer Saeed Muhammad Fahad Asmat Ullah M. Habib ur Rahman Ashfaq Ahmad Jasmeet Judge 《Journal of the Indian Society of Remote Sensing》2018,46(10):1701-1711
Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha?1, while remote sensing showed the RMSE of 397 kg ha?1. Overall strength of relationship between estimated and actual grain yields were good with R2 of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool. 相似文献
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Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery 总被引：1，自引：0，他引：1
Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales. 相似文献
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农情遥感信息与其他农情信息的对比分析 总被引：11，自引：0，他引：11
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。 相似文献
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本文旨在研究基于地块数据约束的深度学习模型的分类特征表示方法,以识别不同作物在不同时相上光谱差异从而对作物类型进行分类。通过Google Earth Engine平台获取作物生育期内全部Landsat 8影像,利用其质量评定波段完成研究区无云时相及区域上的地块统计,提取地块级别的各波段反射率均值按照时相顺序及波长进行排列,构建波谱、时相二维特征图作为该地块的抽象表示。通过构建相对最优的卷积神经网络CNN（Convolutional Neural Network）结构完成对特征图的分类,从而完成对地块的分类。构建CNN模型并不需要手工特征和预定义功能的需求,可完成提取特征并遵循端到端原则进行分类。将该模型的分类结果与其他最为常用机器学习分类器进行了比较,获得了优于常用遥感分类算法的分类精度。结果表明地块数据的加入可以有效的缩减计算规模并提供了准确的分类边界。所提出得方法在地块特征表示及作物分类中具有突出的应用潜力,应视为基于地块的多时相影像分类任务的优选方法。 相似文献
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Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008–2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail. 相似文献