TM遥感与地块内冬小麦产量变异

卫星遥感可以为农作物的准确管理提供必要,及时并具有空间连续性的信息,但高成本一直是限制该项技术在农业上深入发展的主要障碍,利用价格相对较为低廉的TM卫星影像作为信息源来评价其对估测小区域内作物产量空间变异并为规划管理单元提供必要信息的可行性做了初步的研究,结果表明,利用TM图像所获得的植被指数能较好地反映小麦各生育时期的基本特点,两种植被指数（NDVI及RVI）都表现出一定程序的空间,而且都以小麦抽穗后期的变异程度为最大,而且,小麦生长发育的三个重要时期（分蘖期,抽穗期及拔节期）的两种植被指数之间具有极显著相关关系,两个试验地块小麦11月8日的归一化植被指数都与产量表现出了良好的相关关系,另外,两种植被指数在表现作物千粒重和亩穗数等产量指标信息方面,也有一定的效果。     

农田农情参数遥感监测进展及应用展望

农情参数是指反映作物生长过程及其产出的状态指标,关键农情参数主要包括作物长势、单产、物候和旱情等,可用于指导农田的生产管理。遥感是关键农情参数获取的有效手段,然而目前农情参数的遥感监测大多停留在大尺度、宏观监测的层面上,由于缺乏高时空分辨率、高准确度、低成本的农田信息获取技术,业务化的农田尺度农情参数获取受到了诸多因素的制约与限制。导致难以为农田生产管理提供及时的信息支持,这已经影响到精准耕作的发展与应用。文章在总结目前长势、单产、物候和旱情等几个主要农情参数遥感监测研究进展的基础上,分析了这些技术在农田尺度应用的瓶颈,并从新数据源和农情参数监测新模型两个角度出发,对农田尺度农情参数的获取进行了展望。     

广东特色农业波谱数据库设计与开发

自20世纪70年代以来,国际和国内波谱数据库的发展虽然如火如荼,但是都存在不同层次的缺陷,不能满足现阶段我国遥感基础研究和应用的需要,对华南特色农业遥感应用来说差距更远,该研究以建立波谱知识实用型库为目标,集成了华南农作物波谱、环境参数、应用模型,试建立了基于WEB的广东荔枝、龙眼、甘蔗等特色农作物波谱数据库,实现了特色农作物波谱数据库的概念设计、数据的组织、功能和界面设计等方面,为多样的华南特色农作物波谱数据库的建立提供了一个示范。     

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。     

A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing

Satellite remote sensing is an invaluable tool to monitor agricultural resources. However, spatial patterns in agricultural landscapes vary significantly across the Earth resulting in different imagery requirements depending on what part of the globe is observed. Furthermore, there is an increasing diversity of Earth observation instruments providing imagery with various configurations of spatial, temporal, spectral and angular resolutions. In terms of spatial resolution, the choice of imagery should be conditioned by knowing the appropriate spatial frequency at which the landscape must be sampled with the imaging instrument in order to provide the required information from the targeted fields. This paper presents a conceptual framework to define quantitatively such requirements for both crop area estimation and crop growth monitoring based on user-defined constraints. The methodological development is based on simulating how agricultural landscapes, and more specifically the fields covered by a crop of interest, are seen by instruments with increasingly coarser resolving power. The results are provided not only in terms of acceptable pixel size but also of pixel purity which is the degree of homogeneity with respect to the target crop. This trade-off between size and purity can be adjusted according to the end-user's requirements. The method is implemented over various agricultural landscapes with contrasting spatial patterns, demonstrating its operational applicability. This diagnostic approach can be used: (i) to guide users in choosing the most appropriate imagery for their application, (ii) to evaluate the adequacy of existing remote sensing systems for monitoring agriculture in different regions of the world and (iii) to provide guidelines for space agencies to design future instruments dedicated to agriculture monitoring.     

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。     

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。因此本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。     

高光谱遥感图像的单形体分析方法

将n个波段的高光谱图像像元与n维空间里的散点联系起来，结合凸体几何中单形体概念研究高光谱遥感图像纯净像元提取方法，实现图像的地物精确分类识别及像元波谱分解。寻找高光谱遥感图像n维空间里的单形体并认知分析单形体是该研究方法的重要环节。通过MNF(minimum noise fraction)变换和PPI(pixel purity index)计算技术寻找到单形体，基于单形体进行像元分解分析单形体，并结合应用实例和SAM(spectral angle mapper)分类技术完成高光谱图像地物精确分类制图，验证了该研究方法的可操作性。该研究方法的优点在于不需要用户提供地物波谱信息，用于制图和波谱分解的终端单元可由图像本身得到，并由用户控制分类制图和波谱分解的详细程度。     

Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model

Recent developments in unmanned aerial system (UAS) require an urgent introduction to monitoring technologies of crop diagnostic information because of their advantage in manoeuvering tasks at a high-spatial resolutions and low costs in a user-friendly manner. In this study, an advanced application method of an UAS remote sensing system was performed using the grid GRAMI-rice model such that it can be driven using weather and remote sensing data to monitor the spatiotemporal productivities of rice (Oryza sativa). Remotely sensed data for the model were supplied, along with normalized difference vegetation index images obtained using the UAS remote sensing system. The model was first evaluated using paddy data from experimental fields (treated with two nitrogen (N) applications) at Chonnam National University, Gwangju, Republic of Korea (ROK). Practical application was then performed using paddy data from farm fields under conventional farm management practices at the Gimje plain in ROK. The grid GRAMI-rice model statistically well reproduces the field conditions of spatiotemporal rice productivities, showing an acceptable statistical accuracy in the comparison of growth between the simulated and observed values, using a Nash–Sutcliffe efficiency range of 0.113–0.955. According to t-tests (α = 0.05), there were no significant differences between the simulated and observed grain yields from both the evaluation and practical applications. The scientific approach adopted here is unique, advanced, and practical, in a way that UAS remote sensing methods were effectively incorporated with crop modelling techniques. Therefore, it was concluded that the UAS-based remote sensing techniques proposed in this study could represent an innovative way of projecting reliable spatiotemporal crop productivities for precision agriculture.     

光谱遥感岩矿识别基础与技术研究进展

遥感技术的发展与地物光谱特征的研究密不可分。主要从光谱遥感发展与地质应用的趋势出发,从光谱遥感岩矿识别基础与识别技术方法两方面阐述了光谱遥感的研究进展。对于遥感岩矿的识别基础,主要阐述物谱关联和物理模型研究的技术方法与进展以及其对遥感地质应用的促进与深化。在技术方法方面,主要从多光谱与成像光谱两个层次上,分析利用光谱特征进行岩石矿物识别的研究进展及其潜力与可行性。强调了岩石矿物光谱特征在遥感岩矿识别与地质成因信息提取中的重要性。     

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international marketplaces. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.     

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the west African Sahel

Abstract

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.     

Geo-parcel-based crop classification in very-high-resolution images via hierarchical perception

ABSTRACT

The basic application of remote sensing is classifying surface objects in images. Traditional pixel-based or object-based classification methods are poorly suited to very high-resolution (VHR) images captured by remote sensors with high spatial resolutions. In the field of computer vision, deep learning has recently achieved great advances in natural image processing. Inspired by this, we propose a methodology guided by hierarchical perception to classify crops in VHR images based on geo-parcels. Geo-parcel-based crop classification is used in agriculture and in refined farmland management. The proposed methodology can be divided into three steps: zoning, location and quality. In the first step, the image is divided into blocks based on the road network. In the second step, geographical entities are extracted from every block defined in the zoning step. In the last step, the geographical entity types are identified based on the texture information. These steps provide mutual constraints. In each step, the information is extracted by neural networks that have been adapted to the VHR images. The experimental results indicate that our methodology performs well, with a precision greater than 90%. Furthermore, our methodology combines deep learning techniques and theory regarding image perception by humans, providing a valuable method for processing remote sensing information.     

基于遥感信息的农作物生物量估算研究进展

科学准确地估算农作物生物量是生物质能源开发利用战略的必要前提.随着遥感技术的不断发展,可获取遥感数据的时间、空间、光谱分辨率都在不断提高,为长时间跨度和大空间尺度的农作物生物量估算提供了有力支撑.对目前农作物生物量估算方法进行了分析总结,重点阐述了基于遥感信息的农作物生物量估算方法,并根据基于模型的不同将其分为４类(基于植被指数、净初级生产力、作物生长模型、作物表面模型的农作物生物量估算方法),对每一类方法的原理进行了详细论述,并就其在国内外典型的应用情况进行了分析,在此基础上总结了各种估算方法的优势及存在问题,展望了该领域未来主要的发展方向.     

遥感新技术的若干进展及其应用

概述了高光谱遥感、微波遥感、激光雷达和偏振探测等遥感新技术的特点，并在此基础上，综述了这些新技术在地质、海洋、大气、农业、环境、测绘等领域的应用现状。     

Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial feature extraction. This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification (HMAODL-CTC) algorithm on HSI. The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI. To accomplish this, the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality. In addition, the presented HMAODL-CTC model develops dilated convolutional neural network (CNN) for feature extraction. For hyperparameter tuning of the dilated CNN model, the HMAO algorithm is utilized. Eventually, the presented HMAODL-CTC model uses an extreme learning machine (ELM) model for crop type classification. A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm. Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.     

Combining close‐range and remote sensing for local assessment of biophysical characteristics of arable land

For crop management, information on the actual status of the crop is important for taking decisions on nitrogen supply, water supply or harvesting. One would also like to take into account the local spatial variation of the crop. Remote sensing has proved to be a useful technique for estimating and mapping the spatial variation of various biophysical variables. Calibration of the image data is crucial in the performance and applicability of this technique. The aim of this paper is to show the possibility to calibrate remotely sensed imagery using fast and non‐destructive close‐range (below 1.3 m height) sensing instruments, thus providing a means for the assessment of plant characteristics over large areas at low costs. This concept was tested on a homogeneously managed grassland field, subdivided into 20 plots of 15×3 m, at the end of July 2004. Reflected radiation was recorded with an active close‐range sensing device, consisting of a visible light and near‐infrared (NIR) imaging spectrograph, and a 3CCD camera, equipped with special band filters (central wavelengths are at 600, 710 and 800 nm). An airborne campaign with a four‐band UltraCam digital CCD camera was used for extrapolation to larger scales. Plots were harvested, and fresh and dry biomass and leaf nitrogen content were determined. Partial least squares (PLS) models combining spectral and spatial information from the close‐sensing device yielded acceptable results in predicting grassland yields and nitrogen content. Subsequently, these predictions were used to calibrate a model with the image data of the remote sensing device. These were then compared, using leave‐one‐out cross‐validation, with the measured field variables, and the model proved to have an acceptable predictive power.     

Use of satellite remote sensing in Malaysian forestry and its potential

The use of satellite remote sensing in Malaysian forestry and its potential are discussed under three headings (1) Application of satellite remote sensing in Malaysian forestry; (2) Current efforts in remote sensing research application; (3) Potentials of remote sensing techniques in monitoring logging operations and forest change; and remote sensing as a tool in rehabilitation and reforestation. It is concluded that there is a high potential of satellite remote sensing application in Malaysia, especially with the Landsat and SPOT data supported with aerial photographs. This is due to its fast delivery of relevant, timely and accurate information needed for sustainable forestry and a sound management decision.     

Research Progress in Monitoring and Forecasting of Crop Diseases and Pests by Remote Sensing

Crop diseases and pests are the first natural biological hazards that threaten food production and quality.The investigation and sampling in field of plant protection department can’t meet demand of the accurate,non-destructive and efficient monitoring and warning.Currently,remote sensing which can monitor dynamically in real time provides the possibility for the rapid acquisition of continuous surface information,and is also the main development direction monitoring and prediction of crop diseases and pests in the future.Research status of three main directions,including classification of different stresses,severity estimation and stress forecasting,are summarized,and the methods of feature extraction,feature selection,and algorithms are expounded.Then,the application of diseases and pests of three major foodsby remote sensing was analyzed by means of domestic retrieval platforms.On this basis,the existing problems and future development trend of monitoring and forecasting of crop diseases and pests by remote sensing are discussed to promotethe long-term mechanism of agricultural sustainable development.     

航空数字相机的发展与应用

90年代迅速发展的航空数字相机已向传统的航空胶片摄影发出了挑战,它建立在当代多项 高技术成果基础上并具有一系列优点,在各种遥感应用领域已显现了巨大的应用潜力。重点介绍了 其国际发展概况,并就空间分辨率、作业效率、采集存贮技术、图像质量、平台运动影响、光谱响应和 立体成像能力等问题进行了分析。预计它将是21世纪航空遥感最具发展潜力的技术手段之一。