玉米长势区域分异遥感监测──以松嫩平原玉米遥感估产实验区（梨树县）为例

玉米长势区域分异遥感监测──以松嫩平原玉米遥感估产实验区（梨树县）为例王祁春，张柏，张树文，刘兆礼（中国科学院长春地理研究所）松嫩平原是我国重要的玉米种植地区，为国民经济的发展提供着大量以玉米为主的商品粮。玉米产量的及时预测预报对粮食政策的制定、调控...     

江汉平原水稻遥感估产集成系统

水稻遥感估产集成系统主要有以下三方面：（１）系统内容，介绍遥感估产工作中沉淀下来的各种数据、图表、影像等；（２）系统运行，有针对性的选了几个技术含量高的项目来做运行实验；（３）系统维护，介绍集成系统，包括遥感估产工作中需要进一步完善的方面。     

东北平原西部低地草甸的遥感估产模型研究

以ＮＯＡＡ／ＡＶＨＲＲＣＣＴ为遥感信息源，结合野外调查与观测，运用数理统计学方法建立了东北平原西部低地草甸的遥感估产模型。研究表明，直接利用遥感绿度建立模型的方法，较之借助于实测绿度校正遥感绿度建立模型的方法更为方便、合理。     

南方稻区遥感水稻长势监测与估产研究

经过“七五”攻关研究，基本上找出我国南方稻区遥感水稻估产的方法、途径和问题，如用工材资料调查水稻面积、用综合方法进行水稻估产，并获得稻田光谱与水稻长势及产量结构的关系及模式等，为“八五”大区域水稻遥感估产莫定了基础。     

面向对象的遥感估产集成系统研究

遥感估产集成系统是利用遥感技术、地理信息系统支持下的实支运行系统，该系统由图象处理、图形系统、数据库系统、模型工具系统组成，本文评论 用面向对象的分析方法进行系统设计与建立。     

基于MODIS数据的成都市水稻遥感估产研究

以遥感和地理信息系统为主要技术支撑,利用多时相的高光谱分辨率MODIS数据,对成都市2003年水稻进行了估产研究.在利用研究区最佳时相遥感影像提取水稻种植面积的基础上,以多时相的高光谱分辨率遥感数据建立水稻单产模型,并计算出成都市2003年的水稻总产量.研究表明,成都市各行政区当年水稻总产量估算结果的误差为17.45%;利用多时相MODIS数据对农作物进行遥感估产具有一定的可行性,同时通过该研究也为西南地区大范围的农作物遥感估产在方法上提供了一定的借鉴作用.     

基于相对光谱变量的无人机遥感水稻估产及产量制图

及时准确地监测农作物产量信息对国家和区域的粮食生产、贸易及粮食安全预警具有重要意义。当前卫星遥感估产由于高时空分辨率难以同时满足、波段数量少等原因限制估产精度进一步提高,无人机成像高光谱技术以其高时空分辨率、丰富的波段数量和图谱结合的遥感影像等优势被广泛地应用到现代智慧农业与精准农业,使高精度的农作物估产成为了可能。常规无人机估产方法使用的不同时期植被指数在获取时具有不同的光照条件、大气条件和背景,这些外界条件的差异将会引起不同时期植被指数的误差,进而影响估产精度。针对该问题,提出"相对光谱变量"和"相对产量"的概念开展多时期相对变量水稻遥感估产。首先将高光谱成像仪获取的波段进行一对一的组合建立相对归一化光谱指数RNDSI集,并确定水稻不同生育期的最优RNDSI及其构成波段;然后建立不同生育期组合的水稻估产最优模型并做相应的验证。结果显示:使用分蘖期RNDSI[784,635]、拔节期RNDSI[807,744]、孕穗期RNDSI[784,712]和抽穗期RNDSI[816,736]组成的多元线性回归模型是多生育期估产的最优模型,R^2和RMSE分别为0.74和248.97 kg/hm^2,并对此结果进行验证,估产平均相对误差绝对值达到了4.31%,结果表明相对植被指数和相对产量的水稻遥感估产方法可较好地应用于像素级的水稻遥感估产。基于该模型绘制了水稻的田间产量分布图,可更加直观地表现不同区域的产量并进行精准地田间管理。     

遥感绿度指数图像处理系统

遥感绿度指数图像处理是农作物遥感综合估产的基础，对其软件进行软件改进是作物产量报业务技术进步的表现。近年来，作者着手对旧版遥感绿度指数图像处理系统的改进工作，研制了新版软件，定名为ＧＰＳ２．０。     

NOAA气象卫星植被遥感研究动态

NOAA气象卫星已成为世界上最重要的遥感信息源之一。本文根据近年来国内外应用NOAA气象卫星资料,进行植被遥感的研究,着重介绍了NOAA气象卫星在景观稳定性监测、天然草场牧草长势监测与估产,玉米、水稻、小麦长势监测、面积估测及产量预测中的应用,并且根据NOAA气象卫星的特点,与Landsat及Spot卫星相比,无论是其研究思路、方法,还是提供的成果,都具有本质区别。     

Efficiency Research of Planted Rice Area Estimation From NOAA一AVHRR Data in Taihu Domain

运用NOAA-AVHRR资料估算水稻种植面积，是遥感应用领域中一个新的研究方向，结合国家“八五”攻关项目“太湖地区遥感话产”的要求，在太湖地区进行了初步的尝试:(1)根据估产精度要求和NOAA一AVHRR资料校正精度，探讨了运用NOAA一AVHRR资料估产所需的最小区域范围。(2)针对太湖地区的具休地理环境设计了提取水稻种植曲积的技术方案，并在试验区取得了初步成果。     

基于面向对象的多源卫星遥感影像玉米倒伏面积提取

风灾引起的玉米倒伏可能导致玉米大量减产,利用遥感技术准确监测玉米倒伏面积与空间分布信息对灾情的评估非常重要。利用Planet和Sentinel-2影像分别结合面向对象与基于像元方法提取研究区玉米倒伏,同时评估了不同影像特征（光谱特征、植被指数和纹理特征）与不同分类方法（支持向量机法SVM、随机森林法RF和最大似然法MLC）对玉米倒伏提取精度的影响。结果表明：①使用高空间分辨率的Planet影像进行玉米倒伏提取的精度普遍高于Sentinel-2影像;②从分类精度和面积精度来看,Planet影像的光谱特征+植被指数+均值特征结合面向对象RF分类,总体精度和Kappa系数分别为93.77%和0.87,面积的平均误差最低为4.76%;③采用Planet和Sentinel-2影像结合面向对象分类提取玉米倒伏精度高于基于像元分类。研究不仅分析了面向对象方法的优势,还评估了使用不用影像数据结合面向对象方法的适用性,可以为遥感提取作物倒伏相关研究提供一定的借鉴。     

基于光谱和时相特征的夏玉米遥感识别

精确提取作物种植面积一直是农业遥感关注的主要问题之一。综合运用低分辨率的时相变化特征和中分辨率的光谱特征,提出一种夏玉米识别方法。首先基于MODIS NDVI时间序列曲线,分析夏玉米在时相变化上的识别特征,构建识别模型。夏玉米纯像元利用识别模型识别,而耕地和非耕地类型的植被产生的混合像元,则基于像元分解办法获取耕地组分的NDVI时序特征,再利用识别模型判定,然后结合土地利用数据根据空间关系得到中分辨率结果;玉米与其他作物的混合像元则利用中分辨率尺度光谱差异加以区分。研究结果表明,在伊洛河流域主要农业区,识别精度达到90.33%,为作物类型识别提供了新的思路。     

Application of remote sensing in estimating maize grain yield in heterogeneous African agricultural landscapes: a review

Maize (Zea mays L.) is the second most commonly grown crop worldwide and number one staple food in Africa where it accounts for more than 50% of the energy requirements. However, despite its widespread cultivation and the significance of maize information in Africa, maize crop maps and yield forecasts are hardly available. Yet, systematic area, spatial distribution, and maize yield estimates are important in understanding and addressing food security in Africa. Objective monitoring of maize yield statisics in a systematic way is possible with remotely sensed data. However, absence of maize yield forecasts using remote sensing in Africa has been attributed to the cost of acquiring satellite imagery and the heterogeneity of agricultural landscapes. The recent advances in sensors technology and availability of free high-resolution (spatial and temporal) multispectral satellite images afford an opportunity to forecast maize yield as well as mapping its spatial distribution in near real-time basis. This review gives an overview of maize yield estimation using remotely sensed information and its potential application in a fragmented and highly granular agricultural landscapes in Africa, including inherent challenges and research needs. The review was motivated by challenges faced by researchers and national agricultural statistical services agents when forecasting maize yield using conventional ground-based survey methods. These problems include, but are not limited to, restricted accuracy, and cost and time spent resulting in missed opportunities in food security early warning systems and proper developmental interventions. We conclude that by picking multispectral sensors with high spatial, temporal, and spectral resolution, as well as appropriate classification techniques and accurate ground-truthing data, remote sensing can be a practical option for estimating maize grain yield and its spatio-temporal dynamics in heterogeneous African agricultural landscapes for designing appropriate developmental interventions and technological out scaling.     

Spring Maize Yield Estimation based on Radiation Use Efficiency and Multi-temporal Remotely Sensed Data

In order to develop highly accurate model for crop yield estimation,an approach of retrieving regional crop yield was studied by Radiation Use Efficiency (RUE) and remote sensing data,the Jifangzha irrigation is of Hetao irrigation district of Inner Mongolia Autonomous Region was selected as a research case.Based on this model,the difference of the Dry Matter Accumulation (DMA) between the maize’s different growing stages is made and a comparative analysis of the measured yields,and the predicted results based on this model.The results show that the DMA of the maize’s jointing stage is maximal,about 40% of total amount.The measured yield and the predicted ones based on the model has a greater correlation,the Correlation Coefficient was 0.853 and passed the reliability of 0.01.The difference of the DMA in different growing stages showed that the sensitive degree was displayed between main growing stages and yield,and that were some key aspects:the main limiting factor in growing stages and raising yield.These results indicate the model for spring maize yield estimation is feasible and effective based on the RUE and multi-temporal remotely sensing data.     

Research on Using End-member Abundance to Estimate Summer Maize Planting Area

Multispectral satellite remote sensing data of low or moderate spatial resolution are widely used in large range crop planting area extraction.For those areas with complex structure，when the low or moderate spatial resolution remote sensing data sources is used to extract the planting area of target crop，mixed pixel is the main obstacle factor to restrict the area extracting precision.Extracting it on sub\|pixel scale could overcome the restriction of low or moderate spatial resolution and develop the extraction precision.However，the extraction method of target crop planting area on sub\|pixel scale now usually directly use the end\|member abundance to instead the percentage of planting area.Therefore it may cause some errors.On the basis of previous researches，taking Hebi City，Henan Province as the study area，which located in Huang\|Huai\|Hai plain，has the largest summer maize planting area and the complex planting structure.Taking FY3/MERSI data as the main information source.Using the method of spectral matched adaptive best end\|member combination of pixel unmixing to extract the summer maize end\|member abundances.Making regression modeling in various equation forms between summer maize end\|member abundances in pixel and the percentage of planting area.Then select the optimal regression equation form to build regression model，and estimate the actual summer maize ground planting area.Summing up the correlation coefficient when the model was building，significance test and the RMS errors condition of sample point verification.Then choose the cubic model to estimate the planting area of summer maize in the study area.It is proved by remote sensing estimation that the area precision of summer maize planting area is 97.1%，the position precision is 82.5%.     

分形理论在遥感中的应用

论述了分形理论在遥感中的三个应用方面:①分析遥感图像的结构信息量,包括不同图像、不同地物类型;②辅助遥感图像分类,对光谱分类的结果利用分形理论进行检验或确定类型;③模拟遥感图像,根据真实图像的分维,利用分形表面产生算法并产生模拟遥感图像。     

Discrimination of maize crop with hybrid polarimetric RISAT1 data

Microwave remote sensing provides an attractive approach to determine the spatial variability of crop characteristics. Synthetic aperture radar (SAR) image data provide unique possibility of acquiring data in all weather conditions. Several studies have used fully polarimetric data for extracting crop information, but it is limited by swath width. This study aimed to delineate maize crop using single date hybrid dual polarimetric Radar Imaging Satellite (RISAT)-1, Fine Resolution Stripmap mode (FRS)-1 data. Raney decomposition technique was used for explaining different scattering mechanisms of maize crop. Supervised classification on the decomposition image discriminated maize crop from other land-cover features. Results were compared with Resourcesat-2, Linear Imaging Self Scanner (LISS)-III optical sensor derived information. Spatial agreement of 91% was achieved between outputs generated from Resourcesat-2, LISS-III sensor and RISAT-1 data.     

遥感湖泊面积MTMF提取方法及空间尺度效应研究

主要研究遥感湖泊面积亚像元分解提取方法和空间尺度效应,为遥感湖泊面积提取、检验及基于此的局地气候变化分析提供科学的基础数据。在对TM遥感数据进行升尺度处理的基础上,采用混合调制匹配滤波(Mixture Tuned Matched Filtering,MTMF)进行亚像元分解,得到不同空间分辨率的湖泊面积。进而分析不同面积湖泊随遥感空间尺度的变化。结果表明:(1)当通过对高空间分辨率的遥感数据重采样获取多尺度遥感影像进行湖泊面积提取及湖泊空间尺度效应分析时,采用最近邻法比像元聚合重采样法更合理。(2)MTMF亚像元分解法可以用于基于水体光谱特征的遥感湖泊边界提取和面积计算,但边界提取过程中容易将湖泊与河流或其他非湖泊的水体混淆。(3)遥感湖泊面积的提取结果受所用遥感影像空间分辨率的影响较大,影像的空间分辨率越低,湖泊面积提取的偏差越大,尤其对面积较小的湖泊。     

Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects

Remote sensing provides a broad view of landscapes and can be consistent through time, making it an important tool for monitoring and managing protected areas. An impediment to broader use of remote sensing science for monitoring has been the need for resource managers to understand the specialized capabilities of an ever-expanding array of image sources and analysis techniques. Here, we provide guidelines that will enable land managers to more effectively collaborate with remote sensing scientists to develop and apply remote sensing science to achieve monitoring objectives. We first describe fundamental characteristics of remotely sensed data and change detection analysis that affect the types and range of phenomena that can be tracked. Using that background, we describe four general steps in natural resource remote sensing projects: image and reference data acquisition, pre-processing, analysis, and evaluation. We emphasize the practical considerations that arise in each of these steps. We articulate a four-phase process that guides natural resource and remote sensing specialists through a collaborative process to articulate goals, evaluate data and options for image processing, refine or eliminate unrealistic paths, and assess the cost and utility of different options.     

多源遥感图像融合效果评价方法研究

在多源遥感图像融合中,针对同一对象不同的融合方法可以得到不同的融合结果,即可以得到不同的融合图像。如何准确地评价融合效果是图像融合的一个重要组成部分。该文系统地研究了遥感图像融合中的各种评价指标,在对已有的评价指标进行了总结和分析的同时,还提出了一些新的评价指标,并按使用条件和使用用途对遥感图像融合效果评价方法进行了分类。最后通过实验进行了验证。