共查询到19条相似文献，搜索用时 171 毫秒
1.
黄淮海地区冬小麦物候对气候变化的响应及对产量的影响 总被引：2，自引：0，他引：2
《江苏农业科学》2018,(22)
我国是农业大国,冬小麦是我国主要的粮食作物之一,研究其物候对于气候变化的响应以及对其产量的影响是研究气候变化对于农业生产影响的重要内容。以黄淮海地区5个省(市)为研究区域,利用1999—2015年SPOT VGT/NDVI数据提取出冬小麦主要生育物候期(返青期、抽穗期和成熟期)时空分布信息,分析气候变化对于冬小麦物候的影响以及物候变化与冬小麦产量之间的关系。结果表明,冬小麦返青期与气候数据之间并无明显相关性,而抽穗期和成熟期与3月份气温存在显著相关性,同时冬小麦产量与这2个物候期关系密切,抽穗期至成熟期时间段的长短与冬小麦产量极显著相关。 相似文献
2.
归一化植被指数(NDVI)是利用遥感技术提取作物信息的重要指标,其时序曲线能反映植被的生长变化,在农作物种植面积信息提取上有较大优势。以河北平原为研究区,利用空间分辨率250 m的MODIS NDVI数据提取2011—2016年冬小麦种植面积。首先利用HANTS滤波建立NDVI时序曲线,结合区域物候信息和种植模式,提取冬小麦种植像元。因MODIS影像空间分辨率较低,结果受混合像元影响大。运用像元二分模型分解混合像元,计算单一像元冬小麦覆盖度进而计算研究区冬小麦种植面积,并利用Landsat数据对结果进行验证。结果表明,利用时间序列谐波分析法(HANTS)滤波和像元二分模型相结合可提高冬小麦种植面积提取精度,总体分类精度达90%以上;2011—2016年河北平原冬小麦种植面积总体减少,其中河北平原北部和山前平原冬小麦面积缩减,近海平原呈增长趋势。 相似文献
3.
基于MODIS NDVI数据的江苏省冬小麦物候期提取 总被引：4，自引：0，他引：4
《江苏农业科学》2016,(1)
农作物物候期是作物对气候变化响应的重要指标,也是提高区域农业管理水平的重要参数;遥感技术的发展为大面积提取农作物物候期提供了有效的技术途径。利用2010年MODIS NDVI序列提取江苏省冬小麦关键物候期的方法,首先采用非对称高斯函数、双Logistic函数对NDVI时序曲线进行拟合,进而采用动态阈值法确定冬小麦物候期,并利用观测数据对提取结果进行验证。结果表明,就冬小麦返青期、抽穗期、成熟期的提取而言,双Logistic函数拟合方法略优于非对称高斯函数拟合方法,利用前一种拟合方法提取的返青期、抽穗期、成熟期与观测数据比较的均方根误差分别为5.5、9.4、7.5 d,而利用后一种拟合方法提取的返青期、抽穗期、成熟期均方根误差分别为6.1、9.5、7.8 d;2010年全省的冬小麦普遍于第49天之前开始返青,抽穗期普遍开始于第105天至第113天,成熟期普遍开始于第145天至第161天,抽穗期、成熟期大体上表现出从南到北逐渐延迟的趋势,但是返青期的空间差异并不明显。 相似文献
4.
基于不同时相遥感的冬小麦种植面积的提取 总被引：2，自引：0，他引：2
卫星遥感技术能够快速、准确、大面积对农作物生长进行监测,多时相遥感监测可克服单时相遥感监测的不足,利于实现对农作物生长变化的动态监测。以江苏省大丰市为研究区域,选用拔节期和抽穗期两景环境(HJ)卫星遥感影像进行不同地物光谱信息识别与种植面积提取研究。首先,在分析两景HJ星影像植被光谱信息的基础上,提取出各自影像的归一化差值植被指数(NDVI)影像,并对两景NDVI影像分别进行加运算和减运算,得到另外两景NDVI合成影像。其次,通过对提取到的四景NDVI影像光谱信息进行比较分析,最终选用植被光谱信息特征较为明显的加运算合成影像进行冬小麦种植面积提取。最后,基于影像不同地物的NDVI阈值划分,并叠加GPS样点信息校正,提取到大丰市冬小麦种植面积数据及其空间分布信息。结果显示,大丰市遥感提取冬小麦种植面积为78 712.13 hm2,精度为92.51%。在该市20个乡镇(或农场)冬小麦种植面积提取精度中,精度大于95%有9个乡镇(或农场),精度在90%至95%之间的有7个乡镇(或农场),仅有4个乡镇(或农场)提取精度在80%至90%之间。说明,利用不同时相遥感合成运算方法得到的合成影像,能明显增强冬小麦光谱信息与其他植被信息特征区别,有利于实现高精度提取冬小麦种植面积的目的。 相似文献
5.
2009年江苏省冬小麦和水稻种植面积信息遥感提取及长势监测 总被引：4，自引：0，他引：4
以中国农业遥感监测系统(CHARMS)大尺度业务运行作物长势监测需求为实际驱动力,进行基于遥感影像全覆盖的大尺度农作物类型遥感综合自动识别的方法研究,通过分析2009年江苏地区冬小麦和水稻的种植结构、物候历特征及其生物学特性和时序NDVI曲线特征,确定冬小麦和水稻信息提取的NDVI阈值,建立不同作物面积提取模型,并最终获取了2009年CHARMS中江苏冬小麦和水稻长势监测所需的作物空间分布,并与多年平均统计数据比较,总体精度分别达到了78%和85%以上,基本可以满足农情业务化需要.其次,基于面积识别的结果,利用目前长势监测中应用最广泛的归一化植被指数NDVI对江苏2009年冬小麦和水稻长势进行监测,并用差值模型,与近5年长势的平均状况进行对比研究.结果表明,2009年江苏冬小麦和水稻长势均呈现"前期较好,中期变差,后期恢复"的趋势;空间分布上,淮北和苏中地区冬小麦全年长势较多年平均稍差,而水稻长势较差的地区主要分布在苏南地区. 相似文献
6.
基于NDVI数据的华北地区耕地物候空间格局 总被引：17，自引：4，他引：13
【目的】探讨基于多时相遥感信息监测中国华北地区耕地种植制度和物候空间格局特征。【方法】选择NDVI时间序列数据,采用非对称性高斯函数拟合方法重建平滑曲线,依据年内NDVI变化曲线峰值数目监测华北地区耕地的多熟种植制度,并利用动态阈值法获取该种植制度下耕地物候空间格局。【结果】华北地区耕地种植制度以一年两熟为主,其分布具有明显的空间差异性,随着纬度递减呈现出从简单到复杂的总体趋势。在该种植制度下,华北地区耕地第一季作物的生长开始期和生长结束期存在十分明显的空间差异,一年两熟区域的第一季作物生长开始期和生长结束期要明显早于一熟区域。与第一季作物物候期明显的空间差异相比,华北地区耕地第二季作物物候期差异不显著。【结论】华北地区耕地种植制度与物候分布格局和自然地理环境紧密相关,不同区域的温度、降水和光照等气候资源的禀赋和匹配程度对该区域的种植结构和作物布局有很大影响。此外,这种耕地物候空间格局还与作物品质、耕作水平、灌溉、施肥和农药等有密切关系。如何区别生态环境因子和人类活动因子对耕地物候的影响是今后值得深入研究的问题。 相似文献
7.
基于多时相高分一号遥感影像,以江苏里下河平原典型区为研究区域,在地物光谱特征和植被物候特征分析的基础上,开展平原区水田遥感信息模式研究。在辐射校正和几何校正的基础上,构建时序归一化植被指数(NDVI)数据集,利用时序NDVI最大值将研究区划分为植被和非植被2种类型,结合农作物物候特征,利用时序NDVI曲线的双峰特征,进一步将农作物与林草地等其他植被类型区分开,最后利用动态时序弯曲(DTW)逐像元计算像元的水田光谱相似性距离,从而实现水稻与其他农作物的分离。该方法较好地解决了平原河网区水田与水体、林草地等地物的混分问题,总体分类精度和Kappa系数均在0.84以上。空间分析结果合理地反映了区域地物类型以水田为主,且空间上以中部地带集中分布并呈向东减少的趋势特征,证明了该方法在平原区水田信息提取中的有效性。 相似文献
8.
以贵州省森林植被为研究对象，基于对MODIS卫星遥感影像的分析，探讨了遥感监测手段在区域尺度上提高森林植被物候监测精细程度的可能性。结果表明：通过分析比较归一化植被指数(NDVI)、增强植被指数(EVI)、相对绿度指数(Gcc)、绝对绿度指数(ExG)等不同植被指数，发现增强型植被指数(EVI)更能表达森林植被物候变化趋势；采用S-G滤波及动态阈值法提取物候关键期，发现贵州省森林植被物候变化趋势呈现西部地区植被生长开始期晚于东部地区，结束期早于东部地区，整个植被生长季长度西部短于东部；基于EVI的贵州省森林物候空间分异规律总体上不明显，仅纬度与生长开始期(SOS)和生长季长度(LOS)相关性显著，经度海陆地带性及海拔垂直地带性差异不显著。 相似文献
9.
冬小麦是我国的主导粮食作物之一,及时掌握冬小麦种植信息对农业部门制定政策、调整农业结构具有十分重要的意义。以京津冀地区为例,综合考虑遥感数据源成本、数据处理时间以及时间分辨率等要素,提出了一种以MODIS-NDVI产品为主要数据源,提取冬小麦播种面积的技术方法。通过对冬小麦种植区的实地考察与采样,准确获取了反映冬小麦生长周期的NDVI时间序列曲线,并将采样点NDVI时间序列曲线与物候期相结合,识别冬小麦的典型物候期,以决策树分类方法设定阈值,逐级快速剔除了非耕地区和林地区,实现了冬小麦种植区的提取。提取的冬小麦种植区与实地采样点的匹配度达到93.33%,与统计年鉴公布数据的比较结果表明分类精度达到91.84%,为快速提取大面积农作物种植信息提供了一种可操作的技术手段。 相似文献
10.
基于MODIS NDVI数据的郑州市植物物候监测研究 总被引：1，自引：0，他引：1
基于2011年MODIS NDVI 500 m空间分辨率的16 d合成时间序列数据和郑州市域植物分布图,利用MODIS数据中高质量的植被指数研究河南省郑州市域植被的季节性变化规律,并通过时间序列谐波分析法(HANTS)和滑动平均法提取3种植被类型的关键物候期。结果表明:2011年郑州市域大部分林地、草地在第85~110天集中生长,到第220~240天基本成熟,在第305~325天逐渐停止生长,生长季长度为200~220 d。大部分冬小麦开始期在第40~60天,在第100~120天生长达到极限,在第135~150天逐渐停止生长,玉米生长期集中在第165~180天,在第220~235天进入抽穗期,在第260~270天生长结束,农作物的生长季长度为90~110 d。研究结果与实地调研、前人研究资料基本相符,具有可靠性,因此利用MODIS NDVI数据能快速监测植被、有效获取区域植被物候信息。 相似文献
11.
An algorithm is presented to fuse the Normalized Difference Vegetation Index (NDVI) with Light Detection and Ranging (LiDAR) elevation data to produce a map potentially useful for site-specific management practices in cotton. A bi-variate Gaussian probability density distribution is modified to predict an improper probability distribution that also incorporates categorical variables associated with quadrant direction from the population means for the NDVI and elevation data layers. Water availability, influenced by slope and relative changes in elevation (as captured by the elevation data layer), affects crop phenology (as captured by the NDVI data layer). Thus, this fusion procedure results in a map potentially describing the joint effects of NDVI and elevation on cotton growth in a spatial and temporal way. 相似文献
12.
Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor 
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下载免费PDF全文 A spectral reflectance sensor(SRS) fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI) and photochemical reflectance index(PRI). NDVI is useful for indicating crop growth/phenology, whereas PRI was developed for observing physiological conditions. Thus, the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring system. However, capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions, such as solar irradiance and precipitation. Further, unlike growth/phenology, the physiological condition has diurnal changes as well as seasonal characteristics. This study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice, barley, and garlic. First, the measurement accuracy of SRSs was compared with handheld spectrometers, and the R~2 values between the two devices were 0.96 and 0.81 for NDVI and PRI, respectively. Second, the experimental study of threshold criteria with respect to meteorological variables(i.e., insolation, cloudiness, sunshine duration, and precipitation) was conducted, and sunshine duration was the most useful one for excluding distorted values of the vegetation indices. After data processing based on sunshine duration, the R2 values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI) and 0.065–0.298(PRI) on the three crops, and the seasonal signals of vegetation indices became noticeably improved. This method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions. 相似文献
13.
Characterizing Spatial Patterns of Phenology in Cropland of China Based on Remotely Sensed Data 总被引：3，自引：0，他引：3
WU Wen-bin YANG Peng TANG Hua-jun ZHOU Qing-bo CHEN Zhong-xin Ryosuke Shibasaki 《中国农业科学(英文版)》2010,9(1):101-112
This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season （SGS） and the ending date of growing season （EGS） was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies. 相似文献
14.
农作物分布格局动态变化的遥感监测——以东北三省为例 总被引：4，自引：1，他引：3
【目的】当前对涉及到耕地内部不同作物空间分布及其变化的研究较少。本文旨在探讨大尺度作物种植面积和分布格局遥感提取方法及景观生态学中景观格局指数在作物格局动态变化分析中的应用。【方法】基于2005年和2010年作物生育期内遥感影像全覆盖的MODIS-NDVI数据,利用RS、GIS技术,通过分析东北地区主要作物（水稻、玉米、大豆）的种植结构、物候历及NDVI曲线特征,建立不同作物面积遥感提取模型,提取大尺度农作物空间分布格局信息。同时,利用景观格局指数方法分析农作物格局动态变化特征和变化规律。【结果】与多年平均统计数据比较,基于MODIS遥感数据提取的作物面积信息,2005年和2010年平均精度达到了90%以上;5年间,东北地区主要作物种植结构发生了较大变化。其中大豆平均斑块面积减少,面积年动态度为-4.47%,水稻和玉米平均斑块面积均增加,且5年的变化幅度均超过20%。【结论】成本和收益是作物面积增加或减少的主要原因;用中等分辨率的遥感数据进行大尺度作物面积提取的方法是可行的;景观生态学中格局指数可以用来分析耕地内部作物格局的动态变化规律。 相似文献
15.
《农业科学学报》2016,(10)
Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta(YRD) region using moderate resolution imaging spectroradiometer(MODIS) time-series data. The normalized difference vegetation index(NDVI) was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection. 相似文献
16.
Nicolas Tremblay Zhijie Wang Bao-Luo Ma Carl Belec Philippe Vigneault 《Precision Agriculture》2009,10(2):145-161
Nitrogen (N) fertilizer rates applied spatially according to crop requirements can improve the efficiency of N use. The study
compares the performance of two commercial sensors, the Yara N-Sensor/FieldScan (Yara International ASA, Germany) and the
GreenSeeker (NTech Industries Inc., Ukiah, California, USA), for assessing the status of N in spring wheat (Triticum aestivum L.) and corn (Zea mays L.). Four experiments were conducted at different locations in Quebec and Ontario, Canada. The normalized difference vegetation
index (NDVI) was determined with the two sensors at specific growth stages. The NDVI values derived from Yara N-Sensor/FieldScan
correlated with those from GreenSeeker, but only at the early growth stages, where the NDVI values varied from 0.2 to 0.6.
Both sensors were capable of describing the N condition of the crop or variation in the stand, but each sensor had its own
sensitivity characteristics. It follows that the algorithms developed with one sensor for variable-rate N application cannot
be transferred directly to another sensor. The Yara N-Sensor/FieldScan views the crop at an oblique angle over the rows and
detects more biomass per unit of soil surface compared to the GreenSeeker with its nadir (top-down) view of the crop. The
Yara N-Sensor/FieldScan should be used before growth stage V5 for corn during the season if NDVI is used to derive crop N
requirements. GreenSeeker performed well where NDVI values were >0.5. However, unlike GreenSeeker, the Yara N-Sensor/FieldScan
can also record spectral information from wavebands other than red and near infrared, and more vegetation indices can be derived
that might relate better to N status than NDVI. 相似文献
17.
Early-season crop type mapping could provide important information for crop growth monitoring and yield prediction, but the lack of ground-surveyed training samples is the main challenge for crop type identification. Although reference time series based method(RBM) has been proposed to identify crop types without the use of ground-surveyed training samples, the methods are not suitable for study regions with small field size because the reference time series are mainly generated using data set with low spatial resolution. As the combination of Landsat data and Sentinel-2 data could increase the temporal resolution of 30-m image time series, we improved the RBM by generating reference normalized difference vegetation index(NDVI)/enhanced vegetation index(EVI) time series at 30-m resolution(30-m RBM) using both Landsat and Sentinel-2 data, then tried to estimate the potential of the reference NDVI/EVI time series for crop identification at early season. As a test case, we tried to use the 30-m RBM to identify major crop types in Hengshui, China at early season of 2018, the results showed that when the time series of the entire growing season were used for classification, overall classification accuracies of the 30-m RBM were higher than 95%, which were similar to the accuracies acquired using the ground-surveyed training samples. In addition, cotton, spring maize and summer maize distribution could be accurately generated 8, 6 and 8 weeks before their harvest using the 30-m RBM; but winter wheat can only be accurately identified around the harvest time phase. Finally, NDVI outperformed EVI for crop type classification as NDVI had better separability for distinguishing crops at the green-up time phases. Comparing with the previous RBM, advantage of 30-m RBM is that the method could use the samples of the small fields to generate reference time series and process image time series with missing value for early-season crop classification; while, samples collected from multiple years should be further used so that the reference time series could contain more crop growth conditions. 相似文献
18.
Olga S. Walsh A. R. Klatt J. B. Solie C. B. Godsey W. R. Raun 《Precision Agriculture》2013,14(3):343-356
Previous studies have shown the importance of soil moisture (SM) in estimating crop yield potential (YP). The sensor based nitrogen (N) rate calculator (SBNRC) developed by Oklahoma State University utilizes the Normalized Difference Vegetation Index (NDVI) and the in-season estimated yield (INSEY) as the estimate of biomass to assess YP and to generate N recommendations based on estimated crop need. The objective was to investigate whether including the SM parameter into SBNRC could help to increase the accuracy of YP prediction and improve N rate recommendations. Two experimental sites (Lahoma and Perkins) in Oklahoma were established in 2006/07 and 2007/08. Wheat spectral reflectance was measured using a GreenSeeker? 505 hand-held optical sensor (N-Tech Industries, Ukiah, CA). Soil–water content measured with matric potential 229-L sensors (Campbell Scientific, Logan, UT) was used to determine volumetric water content and fractional water index. The relationships between NDVI, INSEY and SM indices at planting and sensing at 5, 25, 60 and 75-cm depths versus grain yield (GY) were evaluated. Wheat GY, NDVI at Feekes 5 and soil WC at planting and as sensed at three depths were also analyzed for eight consecutive growing seasons (1999–2006) for Lahoma. Incorporation of SM into NDVI and INSEY calculations resulted in equally good prediction of wheat GY for all site-years. This indicates that NDVI alone was able to account for the lack of SM information and thus lower crop YP. Soil moisture data, especially at the time of sensing at the 5-cm depth could assist in refining winter wheat YP prediction. 相似文献
19.
基于MODIS NDVI时间序列谱匹配的耕地信息提取研究——以浙中地区为例 总被引：4，自引：0，他引：4
基于MODIS NDVI数据和时间序列谱匹配提取季相相似的耕地面积.选取耕地样点像元的时间序列作为标准时间序列谱,其他像元的时间序列作为测试时间序列谱,对匹配系数进行t测验以确定耕地像元.当匹配位置m=0时,在t测验10%显著水平时耕地的匹配精度最高,相关系数r为0.60;而对均方根误差(RMSE)评价图执行非监督分类,获得的耕地匹配精度显著提高,相关系数为0.79,优于t测验方法.该研究证实了基于时间序列谱匹配的耕地信息提取的可行性,同时匹配精度可反映研究区作物季相的一致性信息. 相似文献