基于时序定量遥感的冬小麦长势监测与估产研究

遥感技术是高效、客观监测农作物生长状态的重要手段,对农业生产管理具有重要意义。以安徽龙亢农场为研究区,收集了中高分辨率多源卫星遥感数据并进行了定量化处理,构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型,生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律,分析了不同品种冬小麦的长势情况,发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上,构建了基于归一化植被指数（NDVI）的冬小麦估产模型,结果表明：利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算,据此绘制了龙亢农场2017年冬小麦产量遥感估算地图,产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测,为区域范围内农作物长势监测提供了一种有效途径。     

基于时序定量遥感的冬小麦长势监测与估产研究

遥感技术是高效、客观监测农作物生长状态的重要手段,对农业生产管理具有重要意义。以安徽龙亢农场为研究区,收集了中高分辨率多源卫星遥感数据并进行了定量化处理,构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型,生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律,分析了不同品种冬小麦的长势情况,发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上,构建了基于归一化植被指数(NDVI)的冬小麦估产模型,结果表明:利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算,据此绘制了龙亢农场2017年冬小麦产量遥感估算地图,产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测,为区域范围内农作物长势监测提供了一种有效途径。     

基于时序定量遥感的冬小麦长势监测与估产研究

遥感技术是高效、客观监测农作物生长状态的重要手段，对农业生产管理具有重要意义。以安徽龙亢农场为研究区，收集了中高分辨率多源卫星遥感数据并进行了定量化处理，构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型，生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律，分析了不同品种冬小麦的长势情况，发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上，构建了基于归一化植被指数（NDVI）的冬小麦估产模型，结果表明：利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算，据此绘制了龙亢农场2017年冬小麦产量遥感估算地图，产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测，为区域范围内农作物长势监测提供了一种有效途径。     

冬小麦单位面积产量的光谱数据估产模型研究

冬小麦单位面积产量的光谱数据估产模型研究池宏康（中国科学院植物研究所）七十年代中期，美国农业部等单位联合进行了对小麦的大面积作物估产试验（ＬＡＣＩＥ），八十年代又开始进行“空间遥感监测农业资源（ＡｇＲＩＳＴＡＲＳ）计划，它们使美国获得了很大的经济效益...     

冬小麦播期的卫星遥感及应用

播种日期对冬小麦生长发育、产量和品质形成均有一定的影响。利用2003年拔节期的Landsat TM卫星的NDVI数据．成功地监测了冬小麦的播种日期。提出了基于NDVI和播种日期的冬小麦的遥感估产的优化模型，并在抽穗期至乳熟期的3次生育期的遥感估产中得到了成功验证与应用。利用出粉率与播种日期的显相关特性，采用拔节期的Landsat TM卫星的NDVI数据，成功预测了小麦籽粒的出粉率。     

多时相影像的冬小麦种植面积提取及估产

针对多时相影像的农作物种植面积难以实现统一精确提取、不能高效地进行遥感估产研究的问题,以河南省濮阳市为研究区,基于Landsat TM影像,采用基于伪不变特征的相对辐射校正方法,在深入分析濮阳市内6类典型地物光谱的基础上,构建决策树提取冬小麦种植面积。然后,基于MODIS植被指数产品,结合相应年份统计数据进行植被指数校正,分别利用校正后关键生育期的归一化植被指数累计值和增强型植被指数累计值与冬小麦产量进行回归分析,建立冬小麦产量预测模型,利用2011年的产量进行验证。结果表明:各年份冬小麦的提取面积精度均在96.3%以上,利用归一化植被指数和增强型植被指数构建的估产模型,R2分别为0.834和0.926,估产精度分别为95.36%和96.44%。该研究可为市域冬小麦种植区的统一高效提取以及冬小麦产量预测提供参考。     

利用遥感技术进行农作物估产

介绍利用GIS,RS和计算机技术,通过对TM卫星影像进行解译处理,结合地面调查数据,在ERDAS IMA GINE和ARC／INFO系统支持下,实现对水稻进行估产,建立适合对农作物估产的模型。     

利用HJ星遥感进行水稻抽穗期长势分级监测研究

对水稻长势进行遥感分级监测,制作能够直观反映水稻长势等级的遥感专题图,便于农业技术人员及时制定有效的田间管理措施,达到增产的目的。以江苏省泰兴市为例,利用HJ-A/B卫星遥感影像,提取水稻的种植面积并分析抽穗期水稻的长势情况。在利用GPS实地取样调查和建立解译标志的基础上,进行HJ-A/B卫星影像校正,人机交互式判读解译等操作,并将GPS样点数据校验贯穿到整个分类过程中,面积信息解译精度在90%以上。最后,利用归一化植被指数(NDVI)反演叶面积指数(LAI)数据信息,依据LAI数据进行水稻长势分级,制作了泰兴市水稻抽穗期长势分级遥感监测专题图。     

利用气象卫星磁带数据监测冬小麦长势

利用遥感技术监测农作物长势,进行产量预测,是遥感应用中的重要课题之一。介绍应用陆地卫星MSS数据进行此项工作的文献较多,大致可归纳为如下几方面: (1) 以目视解译为主。将不同时相、不同波段的影像经光学处理,突出作物信息,配合地面实况资料,推断和评价作物长势,预测产量。 (2) 从农作物光谱特点出发,根据反射率曲线寻求与产量相关性大的日期和波段,建立估产模式。 (3) 引入绿度(G)概念做为评价作物状况的定量标准。用红和近红外波段地物反射率的各种组合来表示,常用的有归一化差值植被指数、比值植被指数等。找出G与产量之间的相关关系。 (4) 在积温基础上建立估产模式。利用作物活动面温度、作物含水量和长势之间的密切关系建立物理     

应用TM图像采用模式识别技术自动提取冬小麦播种面积的研究

在农作物遥感估产研究过程中,如何快速、准确获取当年种植面积是一个关键技术问题。本文重点研究在禹城县冬小麦遥感估产试验中,应用同步TM信息源,根据冬小麦生长发育的特征,选择 TM 的适宜时相,构建多维绿度图,采用模式识别技术,分层自动提取纯麦地、套种麦地信息。这项研究结果与1/5万比例尺 TM 图像目视解译小麦面积相比较,其相对误差甚小,达到了估产实际应用的精度。     

Estimation of wheat production by the integration of MODIS and ground data

This paper presents a methodology capable of combining Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery and ancillary data to estimate durum wheat production in Tuscany (Central Italy). First, the phenological stages of winter wheat are simulated by the use of an agro-meteorological model (Syrius 4.1). Next, MODIS NDVI images at 250 m spatial resolution are utilized to identify fields likely grown with winter wheat. The multitemporal NDVI profiles of these fields are then integrated with Syrius 4.1 outputs through a previously developed procedure in order to simulate wheat above-ground biomass and harvest index. This allows the computation of wheat yield, which, combined with relevant cropped area, provides provincial wheat production estimates. The methodology is tested using ground and MODIS data taken over four Tuscany provinces where winter wheat is widely cultivated. The accuracy of all estimated variables (wheat cropped area, yield and production) is finally evaluated against provincial statistical data. The results of this experiment indicate that the accuracy of wheat cropped area estimation and yield simulation is variable, but interannual production variations are reproduced well for all provinces.     

基于决策树分层分类法的粉煤灰堆场信息提取研究

粉煤灰污染环境,危害人类健康。应用遥感方法快速、实时、准确地识别粉煤灰堆场信息,对保护环境和人类健康具有重要意义。通过分析包头市辖区内典型地物的光谱信息,基于Landsat 5 TM影像数据,采用决策树分层分类法对研究区内的粉煤灰堆场进行提取实验。首先,分析研究区内典型地物的光谱特征,对不同地物之间的关系进行比较。其次,建立决策树,利用土壤调节植被指数(SAVI)、改进归一化差异水体指数(MNDWI)、归一化建筑指数(NDBI)以及光谱阈值法对图像进行了分类。最后利用形状特征和空间位置特征等对分类图像进行后处理,分类精度达到70.7%。实验结果表明:该方法适合粉煤灰堆场信息的自动提取,结合目视解译能够达到较高的识别精度。     

Use of remote sensing data for estimation of winter wheat yield in the United States

This paper shows the application of remote sensing data for estimating winter wheat yield in Kansas. An algorithm uses the Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) computed for each week over a period of 23 years (1982–2004) from Advance Very High Resolution Radiometer (AVHRR) data. The weekly indices were correlated with the end of the season winter wheat (WW) yield. A strong correlation was found between winter wheat yield and VCI (characterizing moisture conditions) during the critical period of winter wheat development and productivity that occurs during April to May (weeks 16 to 23). Following the results of correlation analysis, the principal components regression (PCR) method was used to construct a model to predict yield as a function of the VCI computed for this period. The simulated results were compared with official agricultural statistics showing that the errors of the estimates of winter wheat yield are less than 8%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.     

利用多时相NDVI 监测京郊冬小麦种植信息

物候和时相信息在农作物种植信息提取方面有十分重要的应用价值, 利用多时相L andsat TM 数据, 结合冬小麦的波谱和时相信息, 成功提取了北京地区的冬小麦种植信息。首先, 选用了2003 年4 月7 日、5 月1 日、5 月25 日、6 月18 日不同时相的4 景TM 卫星影像, 计算了不同时相的NDV I 时间谱图像数据; 其次, 结合北京地区农作物种植的实际情况, 提取并分析了北京春夏季主要植被地物(冬小麦、苜蓿、苗圃、春玉米、树林等) 的NDV I 时间谱特征; 第三, 利用不同时相的NDV I 图像数据, 通过NDV I 图像通道间的逻辑运算算法, 成功提取了2003 年北京地区的冬小麦种植信息, 提取精度达到96. 92%; 最后, 与2002 年收割小麦的统计数据相对比, 监测了北京各郊区县的冬小麦种植结构调整情况。结果表明, 多时相、多光谱遥感数据在作物种植信息的监测中有十分明显的技术优势和重要的应用潜力。     

NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa

Crop Normalized Difference Vegetation Index (NDVI) time profiles and crop acreage estimates were derived from the application of linear mixture modelling to Advanced Very High Resolution Radiometer (AVHRR) data over a test area in the southern part of the Pampa region, Argentina. Bands 1 and 2 from seven AVHRR scenes (June to January 1991) were combined to produce fraction images of winter crops, summer crops and pastures. A Landsat Thematic Mapper (TM) scene of the region was classified and superimposed to the AVHRR Local Area Coverage (LAC) data by means of a correlation technique. Each class signature was extracted by regressing the AVHRR response on the cover types proportions, estimated from Landsat-TM data, over sets of calibration windows. The crop NDVI profiles were hence derived from the class signatures in bands 1 and 2. These profiles appeared consistent with the cover types, but variability depending on the set of windows was noted. The assessment of the class signatures was indirectly accomplished through the subpixel classifications of the AVHRR data, performed using the different sets of class spectra. Although some discrepancies between AVHRR and Landsat–TM estimates were observed at the individual window level, the classification results compared quite well on a regional scale with Landsat–TM estimates: crop acreage was estimated to an overall accuracy ranging from 89 to 95 per cent according to the spectra used in the classification. Definitely, the proposed methodology should permit a better exploitation of the temporal resolution of AVHRR data in both the areas of yield prediction and vegetation classification. Furthermore, the perational application of such a methodology for crop monitoring will undoubtedlybe facilitated with the coming sensor systems such as the ModerateResolution Imaging Spectroradiometer (MODIS), the SPOT Vegetation Monitoring Instrument or the ‘Satelite Argentino Cientifico’ (SAC–C).     

The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images

ABSTRACT

The traditional area extraction method mainly depends on manual field survey methods, it is workload, slow and high cost. While remote sensing technology has the advantages of accuracy, rapidity, macroscopic and dynamic, which has become an effective means to extract crop growing area. In this paper, we took Kaifeng City in Henan Province as the study area. Firstly, we explored the advantages of Sentinel-2A RENDVI in crop identification. Then used the supervised classification SVM, object-oriented classification method and assisted with field measured data to extract the winter wheat planting area, the characteristics of the two methods were compared and analysed. Finally, we combined the above two classification methods and proposed a new classification method V2OAE to remove unnecessary influencing factors. The experiment results showed that RENDVI has better recognition ability than the NDVI (Normalized Difference Vegetation Index) in distinguishing vegetation with similar spectrum, the classification effect of object-oriented classification is better than supervised classification SVM, and our classification method removes unnecessary influence factors in the results of object-oriented classification, which is further improve the monitoring accuracy.

Firstly, we have preprocessed the Sentinel-2A image data, its steps are: (1) In the first step, we made radiation calibration for remote sensing images to eliminate the image distortion caused by external factors, data acquisition and transmission systems and so on; (2) In the second step, we made atmospheric correction to eliminate changes in the spectral feature of remote sensing images caused by atmospheric absorption or scattering; (3) In the third step, we made band resampling to unify the resolution of remote sensing images and facilitate the mathematical combination operation of vegetation index; (4) In the fourth step, we made mosaic and cutting to get preprocessed remote sensing images of Kaifeng City. Secondly, we analysed the spectral features of each object and established the interpretation mark with the field measured data. then we explored the ability to identify the ground objects based on NDVI(Normalized Difference Vegetation Index) and RENDVI. Third, we used the rule-based object-oriented classification method and SVM classification to extract the planting area of the study area, the input definition of SVM is spectral feature images of ground objects and the output definition of SVM is the recognition result of ground objects in the process of data training. Then the advantages and disadvantages of the two methods in classification results were analysed. Finally, In order to extract winter wheat information more accurately, we combined the above two classification methods and proposed a new classification method V2OAE (Vector Object Oriented Area Extraction) to remove unnecessary influencing factors, then the winter wheat planting area in Kaifeng City was statistically obtained.     

Estimating soil moisture using Temperature–Vegetation Dryness Index (TVDI) in the Huang-huai-hai (HHH) plain

Soil moisture is an important indicator to describe soil conditions, and can also provide information on crop water stress and yield estimation. The combination of vegetation index (VI) and land surface temperature (LST) can provide useful information on estimation soil moisture status at regional scale. In this paper, the Huang-huai-hai (HHH) plain, an important food production area in China was selected as the study area. The potential of Temperature–Vegetation Dryness Index (TVDI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data in assessing soil moisture was investigated in this region. The 16-day composite MODIS Vegetation Index product (MOD13A2) and 8-day composite MODIS temperature product (MOD11A2) were used to calculate the TVDI. Correlation and regression analysis was carried out to relate the TVDI against in-situ soil moisture measurements data during the main growth stages of winter wheat/summer maize. The results show that a significantly negative relationship exists between the TVDI and in-situ measurements at different soil depths, but the relationship at 10–20 cm depth (R ²?=?0.43) is the closest. The spatial and temporal patterns in the TVDI were also analysed. The temporal evolution of the retrieved soil moisture was consistent with crop phenological development, and the spatial distribution of retrieved soil moisture accorded with the distribution of precipitation during the whole crop growing seasons. The TVDI index was shown to be feasible for monitoring the surface soil moisture dynamically during the crop growing seasons in the HHH plain.