多时相遥感提取水稻种植区研究进展

水稻是我国主要的粮食作物之一,对水稻面积和空间分布的调查是水稻监测的基础,关系国家粮食安全问题.利用卫星遥感技术进行水稻面积与空间分布调查具有大范围、实时性等传统调查不具备的优势,成为近年来的热点.本文对近年来基于多时相遥感进行水稻种植区提取的方法进行梳理与总结,按照传感器类型从3个方面进行阐述——利用不同时相分辨率的光学传感器的提取方法、利用不同极化方式的合成孔径雷达的提取方法以及两者的结合使用的方法,对基于不同数据源的提取方法进行了概括和举例.最后对目前的研究进行合理的评价,指出存在的不足之处并对进一步的研究做出展望.     

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。     

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。     

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.     

限制籼糯品种高产的内在因素分析

为了阐明超高产籼糯育种滞后原因，以超高产籼稻满仓515为对照，对鄂荆糯6号、闽岩糯、越糯3号3个国家审定的常规高产籼糯品种的生物学特性、干物质积累与转运、产量及其构成进行了比较研究。结果表明，虽然3个常规高产籼糯品种的穗数比满仓515多28．3％～56．3％．但总颖花数和库容量分别比满仓515少7．7％～20．6％和14．8％～30．4％，最终产量水平比满仓515低15．4％～22．1％。说明，常规高产籼糯每穗粒数少和生物产量低是其产量比高产籼稻低的主要原因。     

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...     

稻曲病田间分布特点及对水稻产量影响因子研究

通过田间调查,田边四周稻曲病发病严重,田中间发生较轻;一般仅发生于单个稻穗,很少有几个稻穗共同发病.稻曲病主要发生在稻穗的中、下部,少数发生在稻穗的上部.每穗稻曲病粒数以每穗1～6粒最多.在不同感病程度的品种中,单穗病粒数与千粒重、穗实粒数、穗重之间分别存在着极显著的负相关关系.单穗病粒数与秕粒率之间存在着极显著的正相关关系.权重系数表明稻曲病对水稻危害损失主要是通过减少千粒重来实现的.     

水稻遥感估产信息系统中地图数据库的建立

文中重点介绍了地图数据库的内容和数据结构,着重研究了地图数据库的建立及其应用。结果表明,地图数据库可为水稻遥感估产的各个环节提供各种专题信息,是水稻遥感估产信息系统不可缺少的组成部分。     

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.     

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     

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。     

雷达遥感在水稻生长监测应用中的研究进展

结合雷达遥感特点,分析了水稻后向散射特性的主要影响因素,总结了用于水稻生长监测的主要雷达遥感数据源和特点,在阐述水稻识别与估产、病虫害和水旱灾害监测的雷达遥感研究现状的基础上,初步探讨了未来水稻生长雷达监测研究的发展方向.     

Identifying important factors influencing corn yield and grain quality variability using artificial neural networks

Soil, landscape and hybrid factors are known to influence yield and quality of corn (Zea mays L.). This study employed artificial neural network (ANN) analysis to evaluate the relative importance of selected soil, landscape and seed hybrid factors on yield and grain quality in two Illinois, USA fields. About 7 to 13 important factors were identified that could explain from 61% to 99% of the observed yield or quality variability in the study site-years. Hybrid was found to be the most important factor overall for quality in both fields, and for yield as well in Field 1. The relative importance of soil and landscape factors for corn yield and quality and their relationships differed by hybrid and field. Cation exchange capacity (CEC) and relative elevation were consistently identified as among the top four most important soil and landscape factors for both corn yield and quality in both fields in 2000. Aspect and Zn were among the top five most important factors in Fields 1 and 2, respectively. Compound topographic index (CTI), profile curvature and tangential curvature were, in general, not important in the study site-years. The response curves generated by the ANN models were more informative than simple correlation coefficients or coefficients in multiple regression equations. We conclude that hybrid was more important than soil and landscape factors for consideration in precision crop management, especially when grain quality was a management objective.     

Identifying the limiting factors driving the winter wheat yield gap on smallholder farms by agronomic diagnosis in North China Plain

North China Plain(NCP) is the primary winter wheat production region in China, characterized by smallholder farming systems. Whereas the winter wheat average yield of smallholder farmers is currently low, the yield potential and limiting factors driving the current yield gap remain unclear. Therefore, increasing the wheat yield in NCP is essential for the national food security. This study monitored wheat yield, management practices and soil nutrient data in 132 farmers' fields of Xushui County, Baoding City, Hebei Province during 2014–2016. These data were analyzed using variance and path analysis to determine the yield gap and the contribution of yield components(i.e., spikes per hectare, grain number per spike and 1 000-grain weight) to wheat yield. Then, the limiting factors of yield components and the optimizing strategies were identified by a boundary line approach. The results showed that the attainable potential yield for winter wheat was 10 514 kg ha~(–1). The yield gaps varied strongly between three yield groups(i.e., high, middle and low), which were divided by yield level and contained 44 farmers in each group, and amounted to 2 493, 1 636 and 814 kg ha~(–1), respectively. For the three yield components, only spikes per hectare was significantly different(P0.01) among the three yield groups. For all 132 farmers' fields, correlation between yield and spikes per hectare(r=0.51, P0.01), was significantly positive, while correlations with grain number per spike(r=–0.16) and 1 000-grain weight(r=–0.10) were not significant. The path analysis also showed that the spikes per hectare of winter wheat were the most important component to the wheat yield. Boundary line analysis showed that seeding date was the most limiting factor of spikes per hectare with the highest contribution rate(26.7%), followed by basal N input(22.1%) and seeding rate(14.5%), which indicated that management factors in the seeding step were the most important for affecting spikes per hectare. For desired spikes per hectare(6.598×10~6 ha~(–1)),the seeding rate should range from 210–300 kg ha~(–1), seeding date should range from 3th to 8th October, and basal N input should range from 90~(–1)80 kg ha~(–1). Compared to these reasonable ranges of management measures, most of the farmers' practices were not suitable, and both lower and higher levels of management existed. It is concluded that the strategies for optimizing yield components could be achieved by improving wheat seeding quality and optimizing farmers' nutrient management practices in the NCP.     

稻田年内水旱轮作对晚稻产量及生长发育的影响

经５年的定位试验，结果表明：稻田年内水旱轮作（二水一早的麦＝玉米－稻）的晚稻产量比连作（一旱二水的麦－稻－稻）晚稻产量显著增加，增产幅度为６．５４％～１５．８７％，平均为１０．７５％，其增产的原因主要是稻田年内水旱轮作不仅可促进晚稻分蘖早、分蘖快、数量多，从而使有效穗明显增加；而且还促进叶面积的扩展并使叶片叶绿素含量增加，延缓后期衰老，使干物质积累明显增加，从而使每穗粒数明显增加，并能维持与连作晚稻相同的结实率和千粒重     

小麦遥感估产研究现状及趋势分析

在分析国内外小麦遥感估产研究进展的基础上,指出目前小麦遥感估产存在的主要问题,并就小麦遥感估产精度的提高、遥感和模型结合的估产研究与应用、极端气候条件下的遥感估产以及遥感估产技术的信息集成化等方面进行了思考。     

水稻中低产沙泥田氮磷钾肥推荐用量的研究

为了研究中低产沙泥田上氮磷钾不同施肥配方对杂交水稻产量的影响,进行“3414”小区试验,初步获得了土壤供肥能力、水稻的施肥效应和肥料利用率等基本参数：在一定范围内,氮磷钾肥对水稻的增产作用排序是：氮肥〉钾肥〉磷肥;氮、磷、钾肥配合施用增产效果最显著。建议同类中低产田推荐施肥量N、P2O5和K2O分别为：145.3kg/hm^2、53.7kg/hm^2和113.0kg/hm^2,比例为：1∶0.37∶0.78;在沙泥田上施用氮肥和钾肥要“少量多次”,以减少损失,提高肥效。     

冬绿肥联合稻秆还田对水稻产量及稻田土壤肥力的影响

【目的】研究不同品种冬绿肥联合稻秆还田对汉中盆地水稻产量和土壤肥力的影响,为探索适宜汉中水稻生产的绿色栽培技术提供科学依据。【方法】通过连续2年定位试验,设置水稻秸秆不还田（WSN）、水稻秸秆还田（WS）、紫云英和稻秆还田（CS）、油菜秸秆和稻秆还田（RS）、毛苕子和稻秆还田（HS） 5个处理,研究紫云英、毛苕子、油菜、冬闲联合稻秆还田对水稻产量及其构成要素、土壤养分含量、微生物数量和酶活性的影响,并对各因子与水稻产量的关系进行相关性分析。【结果】与WSN处理相比,冬绿肥联合稻秆还田显著提高了水稻产量,CS、HS和RS处理水稻产量2年平均增产14.51%,14.52%和8.49%。稻田土壤速效磷、有机质和全氮含量均以CS处理最高,且均与WSN处理达到差异显著水平。与WSN比较,CS、RS和HS处理稻田土壤微生物总量分别增加70.15%~336.19%,48.57%~183.11%和57.33%~293.55%,其中CS处理显著提高了土壤微生物数量,细菌数量比例上调,而真菌和放线菌数量比例下调,细菌与真菌数量的比值（B/F）升高,有效改善了稻田土壤微生物群落结构。CS处理土壤脲酶、过氧化氢酶、蔗糖酶、脱氢酶活性显著高于其他处理,较WSN处理分别增加17.69%,3.85%,35.36%和62.32%。相关性分析表明,水稻产量与土壤速效磷、全氮含量,土壤细菌、放线菌数量及土壤脲酶、过氧化氢酶活性之间呈显著（P<0.05）或极显著（P<0.01）正相关。【结论】冬季种植紫云英联合稻秆还田有利于提高水稻产量和土壤养分含量,改善土壤微生物群落结构,增强土壤酶活性,是适宜汉中水稻生产的绿色高效栽培种植模式。     

Integrating remote sensing and GIS for prediction of rice protein contents

In this study, protein content (PC) of brown rice before harvest was established by remote sensing (RS) and analyzed to select the key management factors that cause variation of PC using a GIS database. The possibility of finding out the key management factors using GreenNDVI was tested by combining RS and a GIS database. The study site was located at Yagi basin (Japan) and PC for seven districts (85 fields) in 2006 and nine districts (73 fields) in 2007 was investigated by a rice grain taste analyzer. There was spatial variability between districts and temporal variability within the same fields. PC was predicted by the average of GreenNDVI at sampling points (Point GreenNDVI) and in the field (Field GreenNDVI). The accuracy of the Point GreenNDVI model (r ² > 0.424, RMSE < 0.256%) was better than for the Field GreenNDVI model (r ² > 0.250, RMSE < 0.298%). A general-purpose model (r ² = 0.392, RMSE = 0.255%) was established using 2 years data. In the GIS database, PC was separated into two parts to compare the difference in PC between the upper (mean + 0.5SD) and lower (mean − 0.5SD) parts. Differences in PC were significant depending on the effective cumulative temperature (ECT) from transplanting to harvest (Factor 4) in 2007 but not in 2006. Because of the difference in ECT depending on vegetation term (from transplanting to sampling), PC was separated into two groups based on the mean value of ECT as the upper (UMECT) and lower (LMECT) groups. In 2007, there were significant differences in PC at LMECT group between upper and lower parts depending on the ECT from transplanting to last top-dressing (Factor 2), the amount of nitrogen fertilizer at top-dressing (Factor 3) and Factor 4. When the farmers would have changed their field management, it would have been possible to decrease protein contents. Using the combination of RS and GIS in 2006, it was possible to select the key management factor by the difference in the Field GreenNDVI.