水稻病虫害遥感监测与预测研究进展

病虫害现已成为水稻产量的最大制约因素之一，传统的植保技术主要依靠植保人员的视觉和经验，存在一定的主观性，且费时费力，难以满足大范围的实时监测需要。遥感技术的发展提供了一种大面积、全天候、多方位的数据快速获取手段，能够为病虫害的识别分类提供作物种植信息和环境信息，是实现对水稻病虫害进行大面积监测预测的重要手段。在阐述水稻病虫害遥感监测和预测机理的基础上，重点从多尺度遥感监测方法、预测方法、水稻病虫害监测与预测模型构建以及监测预测系统等多方面概述了水稻病虫害监测与预测的研究进展，并指出目前水稻病虫害监测与预测研究存在的问题及未来发展趋势。随着信息化农业的发展与多源数据的融合运用，趋向于精准化与智能化的水稻病虫害遥感监测与预测，将会越来越成熟。     

农作物叶绿素含量遥感估算的研究进展与展望

叶绿素是农作物生长过程中重要的生化参数之一,其含量对农作物长势监测、病虫害监测、成熟期预测都有重要意义。介绍了现有遥感监测农作物叶绿素含量的模型基本原理与方法,总结了国内外在该领域的主要研究成果,进一步将模型进行分类,并分别针对经验模型、物理模型和耦合模型进行详细论述,分析了模型的优缺点及其适用范围,根据遥感估算农作物叶绿素含量的模型研究中存在的问题,对未来估算模型的发展趋势进行了展望。     

作物病虫害遥感监测和预测预警研究进展

危害严重的病虫害胁迫常在我国作物主产区发生,植保部门的田间调查、实地取样等测报方式已经无法满足目前精准、无损、高效的监测预警需求。能够实时动态监测的遥感技术手段为快速获取地表连续信息提供了可能,也是未来作物病虫害遥感监测预测的主要发展方向。通过总结、归纳和整理目前作物病虫害遥感应用中不同病虫害胁迫类型区分、单一胁迫程度估算和作物胁迫预测预警的三大主要方向的研究现状,阐述了现有研究中使用的特征提取方法、特征选择方法,以及胁迫类型区分、程度估算和预测预警的模型算法,并通过国内检索平台对三大粮食作物病虫害的遥感研究应用情况进行了统计分析。在此基础上探讨作物病虫害遥感监测和预测预警现存的问题和未来的发展趋势,推动农业可持续性的长效体制的构建。     

农作物品质遥感反演研究进展

当今农业生产管理迫切需要直接迅速的信息指导。随着科技水平的不断提高,通过利用不同遥感技术手段,实现实时监测农作物生长过程中的主要影响因子,使无损预测预报农作物品质成为可能。通过分析几种农作物的主要品质性状及形成影响因素,在归纳农作物品质监测常用光谱参量的基础上,从地面平台和航天航空平台两方面分别介绍近年来国内外主要研究进展,总结农作物品质遥感监测模型建立使用的主要算法,综合分析农作物品质遥感监测技术实现过程中存在的若干问题,同时提出相应的解决措施,并对遥感监测技术进行了展望。     

农业干旱遥感监测研究进展

农业干旱给社会经济及人民生活造成严重影响,关于农业旱情监测的研究受到了学者们的广泛关注。遥感技术的发展为准确、及时进行旱情监测提供了新的机遇。本文综述了近年来国内外采用遥感方法监测农业旱情的研究进展,包括土壤湿度、作物形态、作物生理等农业旱情指标的遥感反演,指出了在实际应用中存在的一些问题,并提出了进一步改进的思路。     

黄淮海夏玉米物候期遥感监测研究

以黄淮海为研究区,基于MODIS EVI植被指数遥感数据,利用Logistic模型对夏玉米关键物候期(苗期、拔节期、抽雄期、成熟期)进行了监测.使用地面物候观测数据对监测结果加以检验,并与其他常见的遥感监测模型进行了比较.结果表明:研究监测结果总体误差小于士6天,误差较大的区域多出现在地势较高,种植结构较复杂的地区.研究结果可为进行全球气候变化研究提供依据.     

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。     

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。     

主分量分析在监测区域样地分布及异常样地探测中的研究

在考虑刻画监测区域样地的遥感和GIS 因子间存在多重相关性对主分量分析造成危害的基础上, 通过变量选择, 确定刻画监测区域样地的主要遥感和GIS 因子。根据这些因子进行主分量分析, 研究监测区域样地的分布及异常样地的探测。比较考虑多重相关性前后所得结果与监测区域样地的实际分布, 系统研究了多重相关性对主分量分析的危害及如何利用主分量分析了解监测区域样地的分布状况。     

基于改进AlexNet模型的油菜种植面积遥感估测

目前农作物种植面积估测主要是依据遥感影像数据,结合遥感处理技术对遥感地物进行识别监测,估测结果受遥感数据源影响较大。为此提出了改进过的AlexNet卷积神经网络分类识别算法模型,该模型在传统AlexNet模型基础上,针对Landsat8遥感影像数据,创新的提出将五个卷积层的卷积核修改为两个3*3大小和三个2*2大小,并在三个全连接层后加入dropout层,减少过拟合的出现。将改进前后的模型和加入dropout后的改进模型分别对湖北省荆门市2017年油菜作物种植面积进行分析研究,研究从测试精度、Kappa一致性检验和估测面积三方面进行,实验结果表明加入dropout的改进后模型估测效果最好,估测面积与实际面积误差率为2.39%,Kappa一致性检验结果为0.9625,一致性较高。验证了改进后AlexNet模型在油菜作物遥感识别方面的适用性。     

Correlation between potato yield and MODIS‐derived vegetation indices

Monitoring of crop growth and forecasting its yield well before harvest is very important for crop and food management. Remote sensing images are capable of identifying crop health, as well as predicting its yield. Vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR) calculated from remotely sensed data have been widely used to monitor crop growth and to predict crop yield. This study used 8 day TERRA MODIS reflectance data of 500 m resolution for the years 2005 to 2006 to estimate the yield of potato in the Munshiganj area of Bangladesh. The satellite data has been validated using ground truth data from fields of 50 farmers. Regression models are developed between VIs and field level potato yield for six administrative units of Munshiganj District. The yield prediction equations have high coefficients of correlation (R ²) and are 0.84, 0.72 and 0.80 for the NDVI, LAI and fPAR, respectively. These equations were validated by using data from 2006 to 2007 seasons and found that an average error of estimation is about 15% for the study region. It can be concluded that VIs derived from remote sensing can be an effective tool for early estimation of potato yield.     

Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN)

Late blight (LB) is one of the most aggressive tomato diseases in California. Accurately detecting the disease will increase the efficiency of properly controlling the disease infestations to ensure the crop production. In this study, we developed a method to spectrally predict late blight infections on tomatoes based on artificial neural network (ANN). The ANN was designed as a back‐propagation (BP) neural network that used gradient‐descent learning algorithm. Through comparing different network structures, we selected a 3‐25‐9‐1 network structure. Two experimental samples, from field experiments and remotely sensed image data sets, were used to train the ANN to predict healthy and diseased tomato canopies with various infection stages for any given spectral wavelength (µm) intervals. Results of discrete data indicated different levels of disease infestations. The correlation coefficients of prediction values and observed data were 0.99 and 0.82 for field data and remote sensing image data, respectively. In addition, we predicted the field data based on the remote sensing image data and predicted the remote sensing image data with field data using the same network structure, and the results showed that the coefficient of determination was 0.62 and 0.66, respectively. Our study suggested an ANN with back‐propagation training could be used in spectral prediction in the study.     

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Sugarcane is a semi-perennial grass whose cultivation is characterized by an extended harvest season lasting several months leading to very high spatio-temporal variability of the crop development and radiometry. The objective of this paper is to understand this variability in order to propose appropriate spectral indicators for yield forecast. To do this, we used ground observations and Satellite Pour l‘Observation de la Terre (SPOT4) and SPOT5 time series acquired monthly over a 2-year period over Reunion Island and Guadeloupe (French West Indies). We showed that variations in the Normalized Difference Vegetation Index (NDVI) of sugarcane at the field scale are the result of the interaction between the sugarcane crop calendar and plant phenology in a given climatic environment. We linked these variations to crop variables measured in the field (leaf area index and leaf colour), and derived simple, appropriate NDVI-based indicators of sugarcane yield components at the field scale (cane yield and sugar content). For biomass forecast, the best correlation (R² = 0.78) was obtained with images acquired about 2 months before the harvest season, when all the fields are fully developed but before the maturation stage. For sugar content, a polynomial relationship (R² = 0.75) was observed between the field NDVI acquired during the maturation stage and sugar content in the stalk.     

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。     

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.     

The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature

Remote sensing techniques provide timely, up‐to‐date and relatively accurate information for the management of sugarcane crop. This article reviews the literature on the application of remote sensing to sugarcane agriculture and highlights the challenges and opportunities pertinent to the success of this application. The aim of the review was to provide accurate and fundamental information relating the spectral properties of sugarcane to its agronomic, health and nutritional status characteristics that would be of importance to cane farmers and farm managers. The applications of the remote sensing techniques in sugarcane agriculture have been undertaken with particular emphasis on sugarcane classification and areal extent mapping, thermal age group identification, varietal discrimination, yield prediction and crop health and nutritional status monitoring. It can be concluded that by selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting sugarcane spectral information, remotely sensed data should find use in sugarcane agriculture in all areas of application with satisfactory results.     

Soil water and plant canopy effects on remotely measured surface temperatures

Abstract.

Thermal infrared remote sensing of diurnal crop canopy temperature variations represents a possible method for determining the availability of soil water to plants. This study was performed to assess the effects of soil water and crop canopy on apparent temperatures observed by means of remote sensors, and to determine the impact of these effects on remote soil water monitoring. Airborne thermal scanner and apparent reflectance data (one date) and ground PRT-5 data (three dates) were collected primarily over barley and other small grain canopies. Plant heights, cover, and available soil water for four layers in the top 20 cm were determined. Analysis of the data showed a close inverse linear relationship between the available water and the day minus night temperature difference δT, for thick barley canopies (plant cover above 90 per cent) only. The use of apparent reflectance values in the visible region did not improve available soil water regression equations substantially. These results suggest that the available water or plant stress could only be accurately determined for thick canopies, and that the reflectance data could probably be used to identify such canopies but would not improve regression estimates of soil water from remote sensing data.