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11.
准确及时的农情信息是国家与地方政府保障粮食安全与社会稳定的必要条件。云计算的出现为这一需求的实现提供了契机。本文阐述了农情遥感监测云服务平台建设的重要意义、设计思想、总体架构、组成部分以及建设内容。在此基础上,以农情遥感监测产品信息服务为例,开发了一个农情遥感监测信息在线原型系统。该系统是农情遥感监测云服务平台的重要组成部分,负责多尺度时间序列农情遥感监测结果与信息的管理、存储和维护,并且向用户提供查询与下载服务。农情遥感监测云服务平台建设框架的设计为全面整合专家智慧、IT技术、数据资源、服务方式以及平台的实现提供理论指导与建设依据。该平台的建立,将深刻改变农情遥感应用的模式,推动农情遥感的广泛应用与产业化发展。 相似文献
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《International Journal of Digital Earth》2013,6(3):203-218
Abstract While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R 2 of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail. 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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基于典型物候特征的MODIS-EVI时间序列数据
农作物种植面积提取方法
—小区域冬小麦实验研究 总被引:10,自引:1,他引:10
利用MODIS植被指数时间序列这一特性,以北京市通州及周边为实验区,冬小麦种植面积为研究对象,提出
了农作物种植面积指数模型(Pan-CPI模型)的概念,并构造了冬小麦特征物候期植被指数与种植面积的定量函数关系,
通过样区TM影像求解关键参数,对研究区冬小麦种植面积测量方法进行了试验研究。研究结果表明:(1)Pan-CPI模
型能够很好地反映特定目标农作物种植面积状况,为基于植被指数时间序列影像识别农作物种植面积提供了新方法;
(2)精度分析结果表明:Pan-CPI模型具有很高的稳定性,且不受样本变化的影响,只要达到满足模型计算的样本量(如:
5%),多次测量结果间具有很好的一致性。选取MODIS 6×6像元大小的窗口时,TM样本的复相关系数(R2)稳定在0.85
左右,与TM结果比较,窗口相对精度稳定在95%左右,区域精度稳定在92%以上,经调整的区域精度高达96%以上;
(3)对于种植结构复杂、目标作物种植破碎的地区,Pan-CPI模型可以充分利用MODIS植被指数时间序列的优势,有效改
善TM单时相和多时相提取信息因时相缺失无法表征作物变化的不足。 相似文献
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Abha Chhabra K.R. ManjunathAuthor VitaeSushma PanigrahyAuthor Vitae 《International Journal of Applied Earth Observation and Geoinformation》2010
The paper presents a detailed understanding of nitrogenous fertilizer use in Indian agriculture and estimation of seasonal nitrogen loosses from rice crop in Indo-Gangetic plain region, the ‘food bowl’ of the Indian sub-continent. An integrated methodology was developed for quantification of different forms of nitrogen losses from rice crop using remote sensing derived inputs, field data of fertilizer application, collateral data of soil and rainfall and nitrogen loss coefficients derived from published nitrogen dynamics studies. The spatial patterns of nitrogen losses in autumn or ‘kharif’ and spring or ‘rabi’ season rice at 1 × 1 km grid were generated using image processing and GIS. The nitrogen losses through leaching in form of urea-N, ammonium-N (NH4-N) and nitrate-N (NO3-N) are dominant over ammonia volatilization loss. The study results indicate that nitrogen loss through leaching in kharif and rabi rice is of the order of 34.9% and 39.8% of the applied nitrogenous fertilizer in the Indo-Gangetic plain region. This study provides a significant insight to the role of nitrogenous fertilizer as a major non-point source pollutant from agriculture. 相似文献
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Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R2 = 0.65, whereas VWSI showed overestimated values with mean RD 4%. 相似文献
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