遥感数据时空融合技术在农作物监测中的适应性研究

受卫星回访周期及云的影响,大范围研究区同一时期的Landsat卫星数据很难获取,因而国内外学者提出了遥感影像时空融合技术。以石河子为实验区,利用STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)模型融合生成了高时空分辨率TM影像,对不同作物类型真实反射率与融合影像反射率作相关性分析,分析了遥感数据时空融合技术在新疆农作物监测中的适用性。结果表明:利用STARFM模型模拟得到的融合影像与真实影像间的相关性较高,但当地物类型发生变化时,融合影像与真实影像间将存在明显的差异。地物类型变化作物融合影像反射率与真实影像反射率间的相关性较小。     

基于CASA-WOFOST耦合模型的大豆单产遥感估算研究

中国是一个农业大国,在田块甚至是亚田块尺度上进行快速、准确的作物产量估算,不仅可以对农民田间管理进行指导,对于农田生态系统对全球变化的响应评价、制定科学合理的粮食政策、对外粮食贸易和国家粮食安全都具有重要意义。目前主流的估产模型主要有经验统计模型、光能利用率模型、作物生长模型等,每一类模型在各自研究领域相对完整,但是都形成了固定的局限性,为了研究利用遥感技术在小区域范围内田块尺度的作物估产,选取黑龙江省双山农场为研究区,以大豆为研究对象,基于CASA-WOFOST耦合估产模式,利用覆盖作物生长季的时间序列HJ-1A/B遥感影像数据构建高时间分辨率归一化植被指数(Normalized Difference Vegetation Index, NDVI),实现逐日连续监测,分别利用CASA模型和CASA-WOFOST耦合模型对作物进行单产模拟,结果表明：耦合得到的新模型能够具有光能利用率模型较高的运行速度,同时还能发挥作物生长模型模型的机理优势,克服CASA模型在小区域田块尺度上应用的局限性。大豆单产模拟线性回归判定系数(R²)由0.668 53上升到0.844 72,均方根误差(RMSE) 由51.41 kg/hm²下降到29.52 kg/hm²,说明耦合后的模型可以综合考虑光能利用与作物生长生态生理全过程,从而提高作物估产的精度、可靠性和稳定性,为区域田块尺度作物估产提供理论支持,更好地服务于精准农业发展。     

多尺度动态模型单传感器动态系统分布式信息融合

利用多尺度分析的思想,将基于模型的动态系统分析方法与基于统计特性的多尺度 信号变换方法相结合,在不同尺度上拥有对目标状态进行不同描述的多模型动态系统,提出 多尺度分布式信息融合估计新算法,在最细尺度上获得目标状态基于全局信息的融合估计 值,初步解决了多尺度动态模型信息融合问题,这些工作丰富和发展了信息融合理论.     

基于ISODATA法的冬小麦产量分级监测预报

以江苏省姜堰市为例,进行了基于TM卫星遥感技术和小麦估产模型的冬小麦产量监测研究。在利用GPS实地采样调查和建立解译标志的基础上,通过影像校正、采用优化的ISODATA分类方法,结合人机交互式判读解译等操作,将样点的作物信息数据贯穿到整个校验分类过程中,信息解译精度在90%以上。利用分类提取的冬小麦数据,反演叶面积指数、生物量信息等,结合冬小麦估产模型,计算单点产量信息,经过线性转换,对整个区域的冬小麦产量进行监测预报,并制作了冬小麦产量分级专题图。     

水分胁迫条件下玉米生产潜力遥感评估模型——以重庆市长寿区为例

本文将WOFOST作物生长模型与遥感技术相结合,提出一种基于遥感的水分胁迫条件下玉米生产潜力评估模型,可利用遥感数据反演区域干旱强度,并驱动作物生长模型,最终得到作物生产潜力。该模型的核心是基于温度植被干旱指数(TVDI)每日蒸散算法,采用该算法可较为准确地计算水分胁迫条件下玉米农田每日蒸散量;并结合优化选取的作物品种参数,利用自行开发的TVDI-WOFOST软件开展计算,达到精确模拟干旱条件下作物产量的目的。经农业统计数据证明,本文所提出的评估模型可较好模拟水分胁迫条件下玉米生产潜力,对干旱灾情评估、农业发展规划、水利建设规划等工作具有较高的参考价值。     

基于多异类传感器信息融合的微型多旋翼无人机实时运动估计

针对无人飞行器视觉定位结果存在较大时延而影响飞行器运动状态估计精度的问题,提出了一种基于多传感器数据融合的实时运动估计方法.首先,利用机载惯性测量元件(IMU)提供的姿态信息优化单目视觉定位算法,使得视觉定位结果的时延减小.然后,在利用卡尔曼滤波器估计飞行器运动状态的过程中,考虑了视觉定位结果的时延,利用加速度信息进行时延补偿.最终得到实时的高精度运动估计结果.在自主研制的四旋翼飞行器系统上对本文提出的方法进行了验证.通过与不考虑时延的方法的结果以及真实数据进行比较,证明了本方法的有效性.     

基于环境监测的两级数据融合模型与算法

利用多源传感器采集的数据不仅存在大量冗余,而且会影响最终监测结果.为了提高监测的准确度,本文提出一种面向草原环境监测的两级数据融合模型.在一级数据融合中,首先采用自适应加权平均法对各区域内的同类传感器进行融合,然后利用BP神经网络对该区域内的异类传感器进行训练和融合,从而得到对各区域环境状况的初步判断.由于经BP神经网络融合的结果具有不确定性,因此,二级融合利用D-S证据理论对一级融合结果进行综合分析,从而得到对草原环境的决策判断.最后对模型及算法进行了有效性验证与分析,实验结果表明本文的方法能够较准确地监测草原环境状况,同时对草原环境的高效管理和科学养护等提供一些有价值的指导和决策依据.     

基于HJ-1B卫星遥感数据的水稻识别技术研究

为快速、准确地在遥感图像上识别水稻作物的信息,满足县级尺度水稻遥感监测的需要,以野外实地调查资料、1∶5万地形图数据为辅助,通过光谱分析法,分析研究HJ-1B星CCD数据的水稻作物的光谱反射特性,建立水稻作物遥感信息识别模型。采用决策树分类方法提取水稻作物信息,并将该技术方法应用于广西宾阳县水稻作物信息提取研究。采用实测样地数据,利用混淆矩阵进行精度评价验证,总精度为94.9%,Kappa系数为0.8533。研究表明,该水稻作物的识别技术,可以为了解我国水稻种植情况,进行水稻长势监测和产量估测提供技术参考。     

基于植被指数融合的冬小麦生物量反演研究

作物群体生物量是形成产量的物质基础,遥感技术是高效、客观监测作物地上生物量的重要手段,对农业生产管理具有重要意义。以安徽省龙亢农场为研究区,通过PROSAIL模拟光谱分析了4个LAI相关的可见光-近红外植被指数、2个叶片干物质相关的短波红外植被指数和8个融合植被指数与冬小麦地上生物量的关系,并建立反演模型。模拟结果显示,干物质植被指数与作物生物量的相关性高于LAI相关的植被指数,两者融合的植被指数增强了常用植被指数冬小麦生物量的探测能力。利用实测冬小麦数据对生物量反演模型进行验证,结果显示：融合植被指数普遍提高了单一植被指数的地上生物量反演精度,其中MTVI2×NDMI精度最高（RMSE=606.8 kg/hm²）,并为作物地上生物量的高精度反演提供新的技术途径。     

WOFOST模型的发展及应用

作物生长模拟模型已经成为一门新兴的科学,可以为农业资源的管理利用、农业最大收益的获取提供科学的依据。WOFOST(W orld Food Stud ies)模型是荷兰瓦根宁农业大学和世界粮食研究中心共同开发研制的,是模拟特定的土壤和气候条件下一年生作物生长的动态的、解释性模型。WOFOST模型已经在欧洲、非洲以及亚洲的一些地区得到了运用和验证,可用于水稻、玉米、小麦等多种一年生作物的模拟。WOFOST模型可用来分析作物产量风险,不同年份产量的变化,土壤类型及气候变化对产量变化的影响;确定播种策略以及农业机械使用的关键时期;该模型还可用于估计某种作物最大潜在产量,提高灌溉和施肥的增产效益,对生长在不利条件以及地区的作物产量进行预测等。该模型对可持续农业的发展具有积极的指导作用。     

Study on Soybean Yield Estimation Using the CoupledCASA and WOFOST Model

China is an agricultural country. Yield estimating on field scales rapidly and accurately is not only instructional to farmers’ field management, but also important for the response evaluation of farmland ecosystems to climate change, making scientific and rational food policies, external food trade and so on. The current primary estimation models include empirical statistical model, light use efficiency model, and crop growth model. Each type of model is relatively complete in its individual research filed, but all of them have certain amount of limitations. Remote sensing technology was used to estimate crop yield on a field scale within small regional areas. A farm of Heilongjiang Province was selected as the study area, and the soybean was as the research object. Based on the coupled CASA-WOFOST model and time-series HJ-1A/B remotely sensed data which covering the entire growing season of soybean to generate high temporal resolution Normalized Difference Vegetation Index (NDVI), we achieved daily continuous monitoring of crop and simulating crop yield by CASA model and CASA-WOFOST model respectively. The results indicated that the coupled model had a faster running speed of the light use efficiency model, it could also give full play to mechanism advantages of crop growth model and overcome the limitations of the CASA model applied to field scales. The R² of soybean yields increased from 0.668 53 to 0.844 72 and RMSE decreased from 51.41 to 29.52 kg/ha. It is indicated that the coupled mode of light use efficiency model and crop growth model could simultaneously consider the light utilization and the whole physiological and ecological process of crop growth. So that the coupled model could improve the precision, reliability, and stability of crop yield estimation, and provide theoretical support for the estimation of crop yields in regional field scales and better serve the development of precision agriculture.     

基于遥感数据和作物生长模型的小麦变量施肥研究进展

就国内外基于遥感数据和作物生长模型在变量施肥技术的研究应用作了阐述, 提出了快速、无损农业测试技术将是精准变量农业和数字农业今后的发展方向, 对作物生长模型以及精准变量施肥技术的研究进展作了较系统的调查研究, 阐述了将遥感数据与作物生长模型进行数据同化, 实现以高产、优质、环保为目的农业生产的可行性。并结合我国国情提出了发展精准农业变量施肥技术所面临的困难和出路。     

Comparing different smoothing methods to detect double-cropping rice phenology based on LAI products – a case study in the Hunan province of China

ABSTRACT

Many studies have demonstrated the remarkable potential of assimilating remotely sensing leaf area index (LAI) products into crop models in estimating regional crop yield. To ensure the temporal consistency between crop models and remote-sensing system, it is prerequisite to derive the crop phenology information from the LAI products. However, previous studies mainly detected the phenology through the vegetation index (VI). Although some pieces of research applied LAI in phenology monitoring for trees and shrubs, fewer focused on crops, especially those with two or three growing seasons annually. Thus, which smoothing algorithm methods are suitable to obtain phenology of double-cropping rice and their difference in smoothing for crops are still unknown. Based on the Global Land Surface Satellite (GLASS)LAI products, we applied four favourite smoothing algorithms (Asymmetric Gaussian fitting, Double Logistic fitting, Savitzky–Golay filter, and Wavelet-based Filter method) to reduce noise and reconstruct the LAI profile and then detected the phenological information of double-cropping rice in Hunan Province. Compared with ground actual observations, we found that two fitting methods are not suitable to smooth double-cropping rice LAI, while the wavelet method performed the best. Based on the wavelet method, we estimated the phenological information of double-cropping rice at different regional scales as well and the results reflected that the accuracy of regional estimation is also acceptable. This study implied that the wavelet method is rather suitable to detect phenological information of crops from LAI products, which provides narrow gaps between two growing season. Our contribution can benefit researchers who focus on agriculture or remote sensing, especially those who would like to assimilate remotely sensed information into crop growth models.     

基于波谱库的特色农作物智能专家分类系统设计

农作物的长势监测和产量估算一直是遥感技术应用的重要方面,而一个好的农作物分类算法对于农作物产量和长势进行监测十分关键。目前对于一些特色农作物而言,这方面的研究比较缺乏。因此拳研究设计了符合特色农作物的长势监测和产量测算功能模块,将数据挖掘和知识发现应用到专家分类算法中,自行开发了适合农作物数据发现和挖掘的归纳学习算法,充分利用了波谱库中大量的波谱数据、相关属性和空间数据,形成了基于波谱库的特色农作物智能专家分类系统。     

Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images

This paper demonstrates that Radarsat ScanSAR data can be an important data source of radar remote sensing for monitoring crop systems and estimation of rice yield in large areas in tropical and sub-tropical regions. Experiments were carried out to show the effectiveness of Radarsat ScanSAR data for rice yield estimation in the whole province of Guangdong, South China. A methodology was developed to deal with a series of issues in extracting rice information from the ScanSAR data, such as topographic influences, levels of agro-management, irregular distribution of paddy fields and different rice cropping systems. A model was provided for rice yield estimation based on the relationship between the backscatter coefficient of multi-temporal SAR data and the biomass of rice. The study indicates that the whole procedure can become a low-cost and convenient operational system for large-scale rice yield estimation which is difficult for conventional methods.     

Linear mixture modelling applied to AVHRR data for crop area estimation

Abstract

A technique for estimating crop coverage using linear mixture modelling of multi-temporal Advanced Very High Resolution Radiometer (AVHRR) data is presented for a study area in northern Greece. This paper identifies some of the problems associated with using satellite sensor data with coarse spatial resolution for crop area estimation. Using satellite sensor imagery with a high spatial resolution to extrapolate ground measurements to AVHRR scales, the paper shows how the mixture model can be applied to AVHRR data in a mixed agricultural system. Crop areas are estimated to an average accuracy of 89 percent on regional scale using this technique. The results show that this linear mixture modelling has potential for operational crop area monitoring on a regional basis.     

基于卫星遥感预测作物成熟期的可行性分析

精准收获是精准农业的重要环节,首先分析了收获时间对作物产量与品质的影响,论述了作物成熟期监测的重要性,然后从气象统计模型、作物生长模型及遥感监测3个方面回顾了作物成熟期预测的研究进展。在此基础上,通过对目前主要作物成熟状态指示因子遥感监测研究进展的分析,认为在当前新型传感器不断涌现的条件下,利用卫星遥感预测大范围作物成熟期、制订收割顺序并指导农业生产的条件已经成熟。并指出研究面向遥感的作物成熟期指示因子及其变化规律,发展高精度的作物冠层叶绿素及水分含量的遥感估算方法,研究面向农田尺度动态监测的高时空分辨率数据集构建技术和多种模型的耦合将成为该领域未来的研究重点。     

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.     

Using remotely sensed and ancillary data to predict spatial variability of rainfed crop yield

Rainfed agriculture is dominant in Sudan. The current methods for crop yield estimation are based on taking random cutting samples during harvesting time. This is ineffective in terms of cost of information and time. The general objective of this study is to highlight the potential role of remote-sensing techniques in upgrading methods of monitoring rainfed agricultural performance. The specific objective is to develop a relationship between satellite-derived crop data and yield of rainfed sorghum. The normalized difference vegetation index (NDVI), rainfall, air temperature (AT) and soil moisture (SM) are used as independent variables and yield as a dependent variable. To determine the uncertainty associated with the independent variables, a sensitivity analysis (SA) is conducted. Multiple models are developed using different combinations of data sets. The temporal images taken during sorghum’s mid-season growth stage give a better prediction than those taken during its development growth stage. Among predictor variables, SM is associated with the highest uncertainty.