县域尺度的小麦高分辨率遥感估产

从县域尺度准确掌握粮食产量,对于国计民生具有重要意义。目前,基于高分辨率遥感影像的县域尺度的估产研究尚不多见。本文以山东省泰安市岱岳区为研究区,利用2019—2021年高分一号B星、高分一号C星影像,辅以Landsat-8影像,运用随机森林法进行遥感解译,识别耕地变化,探究了小麦种植区的时空格局,选取植被指数NDVI监测小麦长势,建立估产模型,并制作了像元级产量专题图。结果显示：小麦种植面积提取相对误差均在5.150%以内,2021年小麦种植面积降幅达26.021%;小麦长势虽然存在年际变化,但总体平稳;小麦总产量遥感估产误差不超过4.300%,2020年增幅约4.261%,2021年降幅约21.981%。研究表明：2020—2021年,研究区小麦种植面积显著下降,长势基本持平,小麦总产量减少。研究成果有利于地方政府及时准确了解粮食产量,对高分辨率遥感估产研究有积极意义,对于县域尺度的作物监测有较高参考价值。     

遥感估产技术研究现状与展望

遥感技术估产快速,减少了传统估产方式的繁重工作,且增加了精准性。阐述了遥感估产的原理与建模基础及模型类型,对未来遥感估产技术进行了展望。     

小麦光谱特性与估产模式的研究——遥感估产研究之一

通过在两个县麦田和校试验地共29个样点的光谱反射率测定,结合小麦生育状况和籽粒产量分析,可以看到小麦具有和其它绿色植物一样的光谱反射率特征,但也有自己本身值域。而在小麦不同发育期内又有它一定的变化规律,其中以抽穗期反射率为最高。从对不同波段反应上看第一波段(兰光)和第四波段(红外)与产量相关最为密切,经过对不同波段资料处理和运用积分回归技术我们得到了小麦光谱特性与估产模式。     

浙江省水稻遥感估产区划研究

从水稻遥感估产的特点出发,在分析浙江省自然条件的基础上,确定了以稻作制度为主导因子的分区指标,采用地理信息系统GIS空间邻接分析与图论的树算法相结合的方法进行浙江省水稻遥感估产区划研究,得到了较为满意的结果。     

上海水稻遥感估产研究获重要成果

本成果源于国家八五重点攻关项目（８５７２４－０２－０６）“太湖平原水稻遥感估产”的子专题，内容包括卫星信息源处理技术研究，遥感估产地面框架布设，背景数据库建设，水稻种植面积提取，遥感综合估产模型研制，信息系统集成六部分。该成果经１９９２至１９９６年连续五年业务应用试验，取得较好效果，专家鉴定认为成果总体达国际先进水平，总课题获１９９５年度中国科学院科技进步一等奖。     

作物遥感估产的现状及其展望

 总结了国内外作物遥感估产的状况，并对作物遥感的技术集成和便利化服务、数据处理、农业标准化、农学机理、扩大用户需求共4个方面作了展望。     

冬小麦遥感估产多种模型研究

综合冬小麦地面光谱资料及相应的农学参数资料,ＮＯＡＡ／ＡＶＨＲＲ 资料,历年各县冬小麦单产、播种面积、总产资料,历年新疆各站气象资料,监测点历年冬小麦发育期、密度、产量分析等资料,证明地面光谱植被指数与冬小麦密度、生物量、叶面积指数关系密切,从而建立了密度与生物量的光谱监测模型,进而建立了北疆试验区各层冬小麦种植面积估算和产量预报卫星遥感模型,辅以冬小麦产量农业气象预报模型、农学模型及模拟模型,自１９９４ 年投入应用以来的结果表明,这套模型预报精度高、效果很好     

冬小麦遥感估产研究进展

本文对冬小麦遥感估产的研究现状及进展进行了分析。内容包括应用的信息源类型、冬小麦产量估测模型的建立、冬小麦面积信息的提取等，并不冬小麦估产有关问题进行了探讨。     

中国玉米遥感估产区划研究

农作物遥感估产区划是大面积农作物遥感估产研究和实践的基础。根据农作物遥感估产技术的具体要求,结合农作物区划理论,重点考虑春玉米和夏玉米估产,提出了我国玉米遥感估产区划的原则和依据,针对玉米遥感估产中的各项工作,分别作出了全国玉米遥感估产最佳时相、信息源和土地利用结构分类方案,并在此基础上,设计区划指标,把全国分为13个估产区和28个估产亚区。     

基于高光谱遥感预测鲜桃产量的现实意义及可行性分析

高光谱遥感技术已经在林业、农业中得到广泛应用,但国内外未见有应用该技术在经济林果产量的监测预测研究的报道。文章分析了利用高光谱遥感技术预测鲜桃产量的必要性和意义,拓宽了遥感应用的思路,并对其可行性进行了深入探讨,认为利用果实成熟前冠层的光谱特征可以预测果实的产量指标,关键是在果树冠层光谱特征中寻找相应的光谱反射率或其衍生指数与果实产量指标之间的相关关系,并建立预测模型。     

水稻卫星遥感估产研究现状与对策

概述了卫星遥感估产的原理和国内外在水稻产量估算方面的研究进展,分析了其中存在的问题,并对水稻卫星遥感估产研究的发展方向进行了思考.     

水稻遥感估产信息系统中数据压缩方法的研究

水稻遥感估产信息系统拥有包括地理坐标数据、各种光谱数据在内的各种数据,作者提出了对二维地理空间进行编码,由一个编码代替两个地理坐标,从而实现对地理坐标数据的压缩,其压缩率为1:2。文中还针对土壤光谱特点,利用实验室标准土壤光谱与实际土壤光谱之间的关系,提出了土壤光谱回归压缩方法,将每条土壤光谱曲线,压缩为2个回归系数。     

Mapping within-field variability in wheat yield and biomass using remote sensing vegetation indices

Precision Agriculture - This paper explored the ability of remote sensing (RS) and meteorological data to map the variability of yield/biomass in cultivated wheat (Triticum aestivum). The...     

基于多源遥感数据和EC-LUE模型的冬小麦产量估算

【目的 】及时、准确、无损地估算冬小麦产量有助于粮食生产管理和粮食安全。【方法 】文章使用Sentinel-2的红光波段和短波红外数据及MOD09Q1数据,使用ESTARFM融合方法,生成冬小麦生长期（3～6月）内8 d的NDVI高空间分辨率时间序列数据。结合MERRA-2气象同化数据,使用EC-LUE模型进行农作物总初级生产力（GPP）的模拟估算,并使用收割指数方法将之转化为冬小麦产量,将估算结果与美国农业部门公布的县级产量数据进行比较验证。【结果 】实验表明,Sentinel-2与MOD09Q1融合NDVI具有良好的融合精度,相关系数在0.60～0.87之间。基于融合NDVI估算的GPP相比MOD17A2H具有更好的空间细节和纹理。2017—2020年估算产量平均绝对误差MAE为8.41 bu/acre,平均相对误差为18.4%,均方根RMSE为9.7 bu/acre。【结论 】基准影像数量及其与预测日期的时间差会影响融合的精度,总体上能用于后续GPP模拟;EC-LUE模型较好地模拟了农作物的GPP水平和产量,在土地覆盖类型复杂的区域,可以提供更好的GPP空间变异信息,具有可移植性...     

Active remote sensing and grain yield in irrigated maize

Advances in agricultural technology have led to the development of active remote sensing equipment that can potentially optimize N fertilizer inputs. The objective of this study was to evaluate a hand-held active remote sensing instrument to estimate yield potential in irrigated maize. This study was done over two consecutive years on two irrigated maize fields in eastern Colorado. At the six- to eight-leaf crop growth stage, the GreenSeeker? active remote sensing unit was used to measure red and NIR reflectance of the crop canopy. Soil samples were taken before side-dressing from the plots at the time of sensing to determine nitrate concentration. Normalized difference vegetation index (NDVI) was calculated from the reflectance data and then divided by the number of days from planting to sensing, where growing degrees were greater than zero. An NDVI-ratio was calculated as the ratio of the reflectance of an area of interest to that of an N-rich portion of the field. Regression analysis was used to model grain yield. Grain yields ranged from 5 to 24 Mg ha^?1. The coefficient of determination ranged from 0.10 to 0.76. The data for both fields in year 1 were modeled and cross-validated using data from both fields for year 2. The coefficient of determination of the best fitting model for year 1 was 0.54. The NDVI-ratio had a significant relationship with observed grain yield (r ² = 0.65). This study shows that the GreenSeeker? active sensor has the potential to estimate grain yield in irrigated maize; however, improvements need to be made.     

粮食单产遥感预测方法

本文介绍了通过时序遥感植被指数进行粮食单产预测的方法。以NOAA-AVHRR数据为基础数据源，提取区域作物生长过程曲线，并对曲线进行时序分析，提取曲线特征参数，采用参数-产量拟合法、产量分解法和产量差值法三种建模方法，分别对各参数与粮食单产、波动单产和差值单产进行相关分析，得到区域粮食单产敏感因子，并采用线性拟合的方法建立各地区不同类型的单产预测模型，从三种模型中选取置信度最高的模型作为最终单产预测模型。依据此方法对全国26个秋粮主产省份2004年单产进行预测，并将最终的预测结果与国家统计局数据进行对比分析．两者之间的误差在-4．9至11．59之间．相关系数为0．947。     

Multi-temporal wheat disease detection by multi-spectral remote sensing

For the implementation of site-specific fungicide applications, the spatio-temporal dynamics of crop diseases must be well known. Remote sensing can be a useful tool to monitor the heterogeneity of crop vitality within agricultural sites. However, the identification of fungal infections at an early growth stage is essential. This study examines the potential of multi-spectral remote sensing for a multi-temporal analysis of crop diseases. Within an experimental field, a 6 ha plot of winter wheat was grown, containing all possible infective stages of the powdery mildew (Blumeria graminis) and leaf rust (Puccinia recondita) pathogens. Three high-resolution remote sensing images were used to execute a spatio-temporal analysis of the infection dynamics. A decision tree, using mixture tuned matched filtering (MTMF) results and the Normalized Difference Vegetation Index (NDVI), was applied to classify the data into areas showing different levels of disease severity. Classification results were compared to ground truth data. The classification accuracy of the first scene was only 56.8%, whereas the scenes from May 28^th and June 20^th achieved considerably higher accuracies of 65.9% and 88.6% respectively. The results showed that high-resolution multi-spectral data are generally suitable to detect in-field heterogeneities of crop vigour but are only moderately suitable for early detection of crop infections.

Identifying and characterizing yield limiting factors in paddy rice using remote sensing yield maps

Identification and characterization of yield limiting factors based on multi-year yield maps is important for delineating field management zones. Multi-year yield maps were derived from satellite images of a paddy-rice (Oryza sativa L.) study site with a conventional two-cropping system in central Taiwan. Spatiotemporal yield-trend maps with consistently high, average and low yields, and inconsistent yield areas were delineated based on temporal variation and the means of the normalized yields on a per pixel basis. Soil and plant samples were collected and grouped for statistical analysis based on the derived yield-trend maps. Comparison of soil properties and rice yield components among yield classes indicated that differences in leaching loss of basal and top-dressed N fertilizers were the likely limiting factor affecting the spatial variation of yield within the study site.