共查询到19条相似文献，搜索用时 93 毫秒
1.
作物长势监测是农情监测的核心内容之一,处在不同生育期的作物长势存在显著差异。为了提高大范围作物长势遥感监测的精度,利用2001―2015年间获取的MOD09A1数据,以山东省冬小麦为研究对象,在逐年提取冬小麦抽穗期基础上,探讨研究区近15 a间冬小麦抽穗期长势时空格局。研究表明,与归一化差值植被指数(normalized difference vegetation index,NDVI)相比,基于增强型植被指数(enhanced vegetation index,EVI)提取的冬小麦抽穗期与地面观测数据有更好的一致性。研究区冬小麦抽穗期主要集中在4月中、下旬,并从南向北、自西向东逐渐推迟;与NDVI,EVI和归一化差值红外指数(normalized difference infrared index,NDII)相比,产品改进–NDVI(product improve–NDVI,PI_NDVI)更能反映冬小麦的实际长势。基于该指数监测冬小麦长势,2001―2015年间山东省冬小麦抽穗期长势整体呈上升趋势;但年际间波动较大,相同年份不同区域的冬小麦长势存在明显差异;而大部分区域长势状况比较一致,与多年平均状况持平。研究结果与已有的相关研究较为一致,基于遥感进行大范围和长时间作物长势监测的思路可以为以后研究提供一定的参考。 相似文献
2.
青藏高原小嵩草高寒草甸返青期遥感识别方法筛选 总被引：3，自引：1，他引：2
小嵩草高寒草甸是青藏高原的主要植被类型,研究其返青期识别方法对于模拟及预测青藏高原植被物候变化具有重要意义。常用的植被返青期遥感识别方法主要是先对遥感植被指数原始时序数据进行拟合去噪声再求取返青期,各种方法对研究区域、研究经验、参数设置、函数初值设置等有很强的依赖性。为避免返青期识别方法在曲线拟合时对参数初值的依赖性和陷入局部最优解,本文引入了模拟退火算法对双高斯和双逻辑斯蒂函数进行参数优化,并分别对基于以上两种函数及多项式拟合的植被指数时序曲线进行对比,从而选出最佳拟合方法,最后采用最大斜率阈值法、动态阈值法和曲率法识别返青期。利用青藏高原小嵩草高寒草甸34个样本点的返青期地面观测数据及相应的8 km分辨率的NOAA归一化差值植被指数(NDVI)时序数据对以上各种组合的返青期遥感识别方案进行了测试,并选取了153个遥感实验点求取了近30年(1982年—2011年)青藏高原小嵩草高寒草甸的返青期,结果表明:采用双高斯函数拟合的NDVI曲线与原始NDVI时序数据最为接近,在此基础上采用最大斜率阈值法识别的小嵩草高寒草甸返青期及其变化趋势与地面物候观测结果最为一致;同时发现近30年青藏高原小嵩草高寒草甸的平均返青期主要集中在每年的第120—140天,并且呈逐年提前趋势,30年来提前了7天。 相似文献
3.
首先,基于冬小麦不同生育期的地面实测参数,构建了组成冬小麦冠层的、包括不同尺寸和含水量的介电散射体模拟数据库,并在此基础上建立冬小麦单散射反照率和光学厚度分别在C(6.925 GHz)和X(10.65 GHz)波段之间的依赖关系。然后,根据一阶参数化模型推导得到的微波植被指数MVIs(Microwave Vegetation Indices)的物理表达式,结合AMSR-E被动微波亮温数据,反演了华北平原地区冬小麦不同生育期的单散射反照率。与MODIS日归一化差异植被指数NDVI的对比结果显示:冬小麦单散射反照率与NDVI随时间的变化趋势大致相同,但在冬小麦的抽穗期到乳熟期,NDVI呈现饱和趋势,而单散射反照率对小麦的生长变化仍旧比较敏感,在指示冬小麦生长方面具有一定优势。 相似文献
4.
基于1983—1999年7 d时间分辨率5 km空间分辨率的AVHRR传感器数据,利用曲线特征点的物候监测方法,反演获得华北地区冬小麦关键物候期并分析其时空演变规律。结果表明:1)冬小麦的拔节期、抽穗期和成熟期主要集中在60—100、105—125和120—155 d。冬小麦物候期空间格局特征和纬度相关,纬度每升高一度冬小麦的拔节期、抽穗期和成熟期分别推迟了5.2、3.5和3.1 d。2)1983年以来,整个研究区的冬小麦物候期呈现提前趋势,每十年冬小麦的拔节期、抽穗期和成熟期分别提前了0.7、3.1、1.9 d。 相似文献
5.
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
6.
为了进一步提高冬小麦产量估测的精度,基于集合卡尔曼滤波算法和粒子滤波（particle filter, PF）算法,对CERES–Wheat模型模拟的冬小麦主要生育期条件植被温度指数（vegetation temperature condition index,VTCI）、叶面积指数（leaf area index, LAI）和中分辨率成像光谱仪（moderate-resolution imaging spectroradiometer, MODIS）数据反演的VTCI、LAI进行同化,利用主成分分析与Copula函数结合的方法构建单变量和双变量的综合长势监测指标,建立冬小麦单产估测模型,并通过对比分析选择最优模型,对2017—2020年关中平原的冬小麦单产进行估测。结果表明,单点尺度的同化VTCI、同化LAI均能综合反映MODIS观测值和模型模拟值的变化特征,且PF算法具有更好的同化效果;区域尺度下利用PF算法得到的同化VTCI和LAI所构建的双变量估产模型精度最高,与未同化VTCI和LAI构建的估产模型精度相比,研究区各县（区）的冬小麦估测单产与实际单产的均方根误差降低了56.25 kg/hm2,平均相对误差降低了1.51%,表明该模型能有效提高产量估测的精度,应用该模型进行大范围的冬小麦产量估测具有较好的适用性。 相似文献
7.
基于卫星遥感数据的地表信息特征--NDVI-Ts空间描述 总被引：9，自引：0，他引：9
介绍了基于卫星遥感数据的可操作NDVI、Ts和Ts／NDVI计算方法，讨论了NDVI、Ts和Ts／NDVI数据对植被覆盖信息表达的差异，分析了不同地表覆盖在NDVI—Ts空间的年内变化特征。利用信息熵和平均梯度定量分析了NDVI、Ts和Ts／NDVI数据在信息表达丰富度方面的差异，讨论了在不同地表植被覆盖下，Ts／NDVI数据信息提高程度的敏感性。 相似文献
8.
针对目前遗址探测研究多基于单时相遥感数据开展,存在偶然性,对最佳探测时间的研究较少等问题,该文以洛阳盆地为研究对象,利用多时相遥感数据,通过时间序列谐波分析算法(HANTS)重构时间序列植被指数数据集,去噪的同时对比分析出利用冬小麦长势信息进行地下遗址遥感监测的最佳时间区间。研究表明,受地下遗址的胁迫,在分蘖期,冬小麦长势明显比非遗址区的长势差,表明该时期是进行地下遗址探测的最佳时期,进而对最佳探测时期内NDVI积分,有效增强了遗址区和非遗址区之间差异,突出地下遗址的位置和轮廓信息。利用该研究成果,文章成功探测到汉魏洛阳故城以及古伊洛河异常区,与现有的考古资料相吻合。 相似文献
9.
基于环境星CCD数据的冬小麦叶面积指数遥感监测模型研究 总被引：11，自引：0，他引：11
以山东禹城为研究区,利用我国自主研发的环境星数据,计算了4种植被指数,即归一化植被指数(NDVI)、比值植被指数(RVI)、土壤调节植被指数(SAVI)及增强型植被指数(EVI);结合同步观测数据,将植被指数与实测叶面积指数(LAI)进行回归分析,比较各种植被指数模型对冬小麦LAI的估测精度。结果表明,4种植被指数与LAI均具有较高的相关性,其中,比值植被指数(RVI)对LAI反演精度最高,即LAI=2.967 lnRVI-1.201是估算冬小麦LAI的最优模型。使用2009年5月冬小麦LAI观测数据对模型进行验证,平均相对误差为19%。 相似文献
10.
《国土资源遥感》2020,(2)
为自动获取大面积冬小麦种植区域,通常利用中等空间分辨率遥感影像中的物候信息,基于时间序列曲线进行识别与提取。但在实际工程项目中,只使用物候信息提取精度偏低。因此提出了一种基于时间序列曲线数据分类模型与图像分割相结合的冬小麦识别方法。首先,构建多源数据的归一化植被指数(normalized difference vegetation index,NDVI)时间序列曲线,采用时间序列谐波分析方法(harmonic analysis of time series,HANTS)对NDVI时间序列数据进行平滑和去噪;然后,对NDVI时间序列进行坐标转换,获取波段均值、标准差和均方根3个参数,构建新的分类模型,提升冬小麦与其他作物的差异值;最后,通过与高空间分辨率数据的分割结果相结合,利用图像的空间结构信息,提高地物边界的准确性。以南京市江宁区为例,利用2017年12月—2018年6月间高分一号、Landsat8和Sentinel-2A 3种类型的共21景多源数据进行实验,最终提取精度达到98. 74%,比其他方法有所提高,为农业管理部门提供了准确的冬小麦种植区域和分布的地理信息数据。 相似文献
11.
Monitoring phenological change in agricultural land improves our understanding of the adaptation of crops to a warmer climate. Winter wheat–maize and winter wheat–cotton double-cropping are practised in most agricultural areas in the North China Plain. A curve-fitting method is presented to derive winter wheat phenology from SPOT-VEGETATION S10 normalized difference vegetation index (NDVI) data products. The method uses a double-Gaussian model to extract two phenological metrics, the start of season (SOS) and the time of maximum NDVI (MAXT). The results are compared with phenological records at local agrometeorological stations. The SOS and MAXT have close agreement with in situ observations of the jointing date and milk-in-kernel date respectively. The phenological metrics detected show spatial variations that are consistent with known phenological characteristics. This study indicates that time-series analysis with satellite data could be an effective tool for monitoring the phenology of crops and its spatial distribution in a large agricultural region. 相似文献
12.
利用HJ-1A/B卫星CCD数据,提取2013—2015年三年江汉平原农田的归一化植被指数NDVI,构建时间序列曲线,利用小波变换对HJ-1A/B卫星所得的NDVI数据进行平滑降噪处理,结合地面调研资料,提取江汉平原农作物的物候信息。研究结果表明,HJ-1A/B卫星可用于农田物候监测,对于小区域的农田作物长势监测具有独特的优势。 相似文献
13.
统计数据总量约束下全局优化阈值的冬小麦分布制图 总被引：6，自引：0，他引：6
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。 相似文献
14.
With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data. 相似文献
15.
Vegetation indices derived from satellite image time series have been extensively used to estimate the timing of phenological events like season onset. Medium spatial resolution (≥250 m) satellite sensors with daily revisit capability are typically employed for this purpose. In recent years, phenology is being retrieved at higher resolution (≤30 m) in response to increasing availability of high-resolution satellite data. To overcome the reduced acquisition frequency of such data, previous attempts involved fusion between high- and medium-resolution data, or combinations of multi-year acquisitions in a single phenological reconstruction. The objectives of this study are to demonstrate that phenological parameters can now be retrieved from single-season high-resolution time series, and to compare these retrievals against those derived from multi-year high-resolution and single-season medium-resolution satellite data. The study focuses on the island of Schiermonnikoog, the Netherlands, which comprises a highly-dynamic saltmarsh, dune vegetation, and agricultural land. Combining NDVI series derived from atmospherically-corrected images from RapidEye (5 m-resolution) and the SPOT5 Take5 experiment (10m-resolution) acquired between March and August 2015, phenological parameters were estimated using a function fitting approach. We then compared results with phenology retrieved from four years of 30 m Landsat 8 OLI data, and single-year 100 m Proba-V and 250 m MODIS temporal composites of the same period. Retrieved phenological parameters from combined RapidEye/SPOT5 displayed spatially consistent results and a large spatial variability, providing complementary information to existing vegetation community maps. Retrievals that combined four years of Landsat observations into a single synthetic year were affected by the inclusion of years with warmer spring temperatures, whereas adjustment of the average phenology to 2015 observations was only feasible for a few pixels due to cloud cover around phenological transition dates. The Proba-V and MODIS phenology retrievals scaled poorly relative to their high-resolution equivalents, indicating that medium-resolution phenology retrievals need to be interpreted with care, particularly in landscapes with fine-scale land cover variability. 相似文献
16.
Jyoteshwar R. Nagol Joseph O. Sexton Anupam Anand Ritvik Sahajpal Thomas C. Edwards 《International Journal of Digital Earth》2018,11(3):233-245
Vegetation phenology is commonly studied using time series of multi-spectral vegetation indices derived from satellite imagery. Differences in reflectance among land-cover and/or plant functional types are obscured by sub-pixel mixing, and so phenological analyses have typically sought to maximize the compositional purity of input satellite data by increasing spatial resolution. We present an alternative method to mitigate this ‘mixed-pixel problem’ and extract the phenological behavior of individual land-cover types inferentially, by inverting the linear mixture model traditionally used for sub-pixel land-cover mapping. Parameterized using genetic algorithms, the method takes advantage of the discriminating capacity of calibrated surface reflectance measurements in red, near infrared, and short-wave infrared wavelengths, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index. In simulation, the unmixing procedure reproduced the reflectances and phenological signals of grass, crop, and deciduous forests with high fidelity (RMSE?相似文献
17.
Within-season forecasting of crop yields is of great economic, geo-strategic and humanitarian interest. Satellite Earth Observation now constitutes a valuable and innovative way to provide spatio-temporal information to assist such yield forecasts. This study explores different configurations of remote sensing time series to estimate of winter wheat yield using either spatially finer but temporally sparser time series (5daily at 100 m spatial resolution) or spatially coarser but denser (300 m and 1 km at daily frequency) time series. Furthermore, we hypothesised that better yield estimations could be made using thermal time, which is closer to the crop physiological development. Time series of NDVI from the PROBA-V instrument, which has delivered images at a spatial resolution of 100 m, 300 m and 1 km since 2013, were extracted for 39 fields for field and 56 fields for regional level analysis across Northern France during the growing season 2014-2015. An asymmetric double sigmoid model was fitted on the NDVI series of the central pixel of the field. The fitted model was subsequently integrated either over thermal time or over calendar time, using different baseline NDVI thresholds to mark the start and end of the cropping season. These integrated values were used as a predictor for yield using a simple linear regression and yield observations at field level. The dependency of this relationship on the spatial pixel purity was analysed for the 100 m, 300 m and 1 km spatial resolution. At field level, depending on the spatial resolution and the NDVI threshold, the adjusted R² ranged from 0.20 to 0.74; jackknifed – leave-one-field-out cross validation – RMSE ranged from 0.6 to 1.07 t/ha and MAE ranged between 0.46 and 0.90 t/ha for thermal time analysis. The best results for yield estimation (adjusted R² = 0.74, RMSE =0.6 t/ha and MAE =0.46 t/ha) were obtained from the integration over thermal time of 100 m pixel resolution using a baseline NDVI threshold of 0.2 and without any selection based on pixel purity. The field scale yield estimation was aggregated to the regional scale using 56 fields. At the regional level, there was a difference of 0.0012 t/ha between thermal and calendar time for average yield estimations. The standard error of mean results showed that the error was larger for a higher spatial resolution with no pixel purity and smaller when purity increased. These results suggest that, for winter wheat, a finer spatial resolution rather than a higher revisit frequency and an increasing pixel purity enable more accurate yield estimations when integrated over thermal time at the field scale and at the regional scale only if higher pixel purity levels are considered. This method can be extended to larger regions, other crops, and other regions in the world, although site and crop-specific adjustments will have to include other threshold temperatures to reflect the boundaries of phenological activity. In general, however, this methodological approach should be applicable to yield estimation at the parcel and regional scales across the world. 相似文献
18.
Satellite derived vegetation vigour has been successfully used for various environmental modeling since 1972. However, extraction of reliable annual growth information about natural vegetation (i.e., phenology) has been of recent interest due to their important role in many global models and free availability of time-series satellite data. In this study, usability of Moderate Resolution Imaging Spectro-radiometer (MODIS) and Global Inventory Modelling and Mapping Studies (GIMMS) based products in extracting phenology information about evergreen, semi-evergreen, moist deciduous and dry deciduous vegetation in India was explored. The MODIS NDVI and EVI time-series data (MOD13C1: 5.6 km spatial resolution with 16 day temporal resolution—2001 to 2010) and GIMMS NDVI time-series data(8 km spatial resolution with 15 day temporal resolution—2000 to 2006) were used. These three differently derived vegetation indices were analysed to extract and understand the vegetative growth rhythm over different regions of India. Algorithm was developed to derive onset of greenness and end of senescence automatically. The comparative analysis about differences in the results from these products was carried out. Due to dominant noise in the values of NDVI from GIMMS and MODIS during monsoon period the phenology rhythm were wrongly depicted, especially for evergreen and semi-evergreen vegetation in India. Hence, care is needed before using these data sets for understanding vegetative dynamics, biomass cestimation and carbon studies. MODIS EVI based results were truthful and comparable to ground reality. The study reveals spatio-temporal patterns of phenology, rate of greening, rate of senescence, and differences in results from these three products. 相似文献
19.
Gargi Upadhyay S. S. Ray Sushma Panigrahy 《Journal of the Indian Society of Remote Sensing》2008,36(1):37-50
Crop phenological parameters, such as the start and end time of the crop growth, the total length of the growing season, time of peak vegetation and rate of greening and senescence are important for planning crop management and crop diversification/intensification. Multi-temporal remote sensing data provides opportunity to characterize the crop phenology at regional level. This study was conducted during the kharif season of the year 2001–02 for Punjab. The ten-day Normalised Difference Vegetation Index (NDVI) composite products, with 1 km spatial resolution, available from the Vegetation sensor onboard SPOT4 were used for the study. Twenty-one temporal datasets from May 1, 2001 to November 21, 2001 were used. Logical modelling approach was followed to compute the minimum and maximum NDVI, the amplitude of NDVI, the threshold NDVI during sowing and harvest, the crop duration, integrated NDVI and skewness of profile. The analysis showed that before July beginning, in the whole of Punjab, sowing/planting was over. It was found that the crop emergence in the eastern part of the state started earlier than the western region. The maximum NDVI, which represented peak vegetative stage, was above 0.7 and occurred mostly during August. The duration of crops ranged between 90–140 days, with majority between 110–120 days. Total integrated NDVI in Punjab was generally above 60. Using principal component analysis and divergence analysis seven best metrics were selected for crop discrimination. 相似文献