杂交水稻制种最佳期的确定          下载免费PDF全文

周义明  张翊 《气象》1997,23(11):52-54

针对杂交水稻制种对晴，雨天气敏感反应，根据建阳市降水资料，分析降水分布规律，采用历年６－９月逐日，各候连阴雨天气概率分布规律了连续１０天内遇阴雨天概率分布谷期，确定闽北地区杂交水稻制种的最佳期。  相似文献   

基于灾害风险理论的湖北水稻高温热害风险分析及区划     

李世刚  王东仙  刘可群  刘志雄 《气象科技》2024,52(2):288-296

高温热害是长江流域最主要的气象灾害之一,科学评估热害风险是防灾减灾的基础。本文利用近60年气象观测资料,对湖北高温热害的时空分布特征进行了分析;基于自然灾害风险基本理论,建立了包括影响水稻结实率关键期的热害强度、灾害发生时承灾体实际暴露度、灾害脆弱性等因素的高温热害风险评价模型,并进行了风险分析与区划。结果表明：高温天气出现概率高的时段是7月下旬,其中7月第6候为最高。从高温热害风险指数上来看,7月第3候抽穗开花水稻的热害风险最高,此后随时间的推移,热害风险降低;湖北现行的一季中稻抽穗开花期处于风险较高的时段,推迟5天其热害风险指数可下降20%左右;推迟15天以上热害风险指数将降低50%以上。江汉平原稻区是湖北高温热害风险低发地区,鄂东南及鄂西北地区是热害风险高发地区;针对各区热害特点提出了风险应对措施。  相似文献   

南方水稻复种指数变化对国家粮食产能的影响及其政策启示   总被引：8，自引：1，他引：7  

蒋敏  李秀彬  辛良杰  谈明洪 《地理学报》2019,74(1):32-43

中国南方地区水稻生产的变化对国家粮食安全具有重要影响。本文利用Landsat数据提取1990-2015年南方地区水稻种植制度分布及变化,并分析其对粮食产能的影响。结果表明：① 1990-2015年,水稻复种指数从148.3%下降到129.3%,双季稻改种单季稻（“双改单”）损失的播种面积为253.16万hm²,区域上以长江中下游地区变化最为突出。南方地区水稻种植制度整体呈现由北向南“双退单进”的变化格局;② 1990-2015年,“双改单”导致全国水稻产量减少6.1%,粮食产量减少2.6%。水稻主产区湖南省和江西省以及经济发展较好的浙江省因“双改单”水稻减产幅度较大,均超过13%;③ 充分利用“双改单”稻田的粮食产能相当于新增耕地223.3万hm²,为2001-2015年通过土地整治项目新增耕地总量的54%,是2016-2020年全国新增耕地规划目标的1.7倍,可节省约1674.4亿元新增耕地开垦费用。因此,与其追求低质量的“新”耕地,不如充分利用已有的高质量“旧”耕地,政府应转变耕地占补平衡的考核方式,将因提高复种指数增加的播种面积纳入补充指标。  相似文献   

Optimal MODIS data processing for accurate multi-year paddy rice area mapping in China     

Li Liu  Qinxue Xiong  Huijuan Zhang  Peiling Song  Yanhong Huang 《地理信息系统科学与遥感》2020,57(5):687-703

ABSTRACT

Researchers, policy makers, and farmers currently rely on remote sensing technology to monitor crops. Although data processing methods can be different among different remote sensing methods, little work has been done on studying these differences. In order for potential users to have confidence in remote sensing products, an analysis of mapping accuracies and their associated uncertainties with different data processing methods is required. This study used the MOD09A1 and MYD09A1 products of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, from which the Enhanced Vegetation Index (EVI) and the two-band EVI (EVI2) images were obtained. The objective of this study was to analyze the accuracy of different data processing combinations for multi-year rice area mapping. Sixteen combinations of EVI and EVI2 with two cloudy pixel removal methods (QA/BLUE) and four pixel replacement methods (MO/MY/MOY/MYO) were investigated over the Jiangsu Province of southeast China from 2006 to 2016. Different accuracy results were obtained with different data processing combinations for multi-year rice field mapping. Based on a comparison of the relative performance of different MODIS products and processing method combinations, EVI2_BLUE_MYO was proposed to be the optimal processing method, and was applied to forecasting the rice-planted area of 2017. Study results from 2006 to 2017 were validated against reference data and showed accuracies of rice area extraction of greater than 95%. The mean absolute error of transplanting, heading, and maturity dates were 11.55, 8.10, and 7.78 days, respectively. In 2017, two sample regions (A and B) were selected from places where rice fractional cover was greater than 75%. Rice area extraction accuracies of 85.0% (A) and 92.3% (B) were obtained. These results demonstrated the complementarity of MOD09A1 and MYD09A1 datasets in enhancing pixel spatial coverage and improving rice area mapping when atmospheric influences are significant. The optimal data processing combination indentified in this study is promising for accurate multi-year and large-area paddy rice information extraction and forecasting.  相似文献   

Inter-provincial Differences in Rice Multi-cropping Changes in Main Double-cropping Rice Area in China: Evidence from Provinces and Households   总被引：1，自引：0，他引：1  

WANG Renjing  LI Xiubin  TAN Minghong  XIN Liangjie  WANG Xue  WANG Yahui  JIANG Min 《中国地理科学(英文版)》2019,(1):127-138

Since the early 1980 s, the multi-cropping index for rice has decreased significantly in main double-cropping rice area in China, which is the primary double-cropping rice(DCR) production area. This decline may bring challenges to food security in China because rice is the staple food for more than 60% of the Chinese population. It has been generally recognized that rapidly rising labor costs due to economic growth and urbanization in China is the key driving force of the ‘double-to-single' rice cropping system adaption. However, not all provinces have shown a dramatic decline in DCR area, and labor costs alone cannot explain this difference. To elucidate the reasons for these inter-provincial distinctions and the dynamics of rice cropping system adaption, we evaluated the influencing factors using provincial panel data from 1980 to 2015. We also used household survey data for empirical analysis to explore the mechanisms driving differences in rice multi-cropping changes. Our results indicated that the eight provinces in the study can be divided into three spatial groups based on the extent of DCR area decline, the rapidly-declining marginal, core, and stable zones. Increasing labor cost due to rapid urbanization was the key driving force of rice cropping system adaption, but the land use dynamic vary hugely among different provinces. These differences between zones were due to the interaction between labor price and accumulated temperature conditions. Therefore, increasing labor costs had the greatest impact in Zhejiang, Anhui, and Hubei, where the accumulated temperature is relatively low and rice multi-cropping index declined dramaticly. However, labor costs had little impact in Guangdong and Guangxi. Differences in accumulated temperature conditions resulted in spatially different labor demands and pressure on households during the busy season. As a result, there have been different profits and rice multi-cropping changes between provinces and zones. Because of these spatial differences, regionally appropriate policies that provide appropriate subsidies for early rice in rapidly-declining marginal zone such as Zhejiang and Hubei should be implemented. In addition, agricultural mechanization and the number of agricultural workers have facilitated double-cropping; therefore, small machinery and agricultural infrastructure construction should be further supported.  相似文献   

无人机影像的面向对象水稻种植面积快速提取     

吴方明  张淼  吴炳方 《地球信息科学学报》2019,21(5):789-798

基于抽样技术的地面调查与遥感影像分类相结合的方法在大范围作物种植面积提取中得到广泛使用。无人机影像具有低成本、高时效、高分辨率的一系列优点,可以快速实现特定区域范围内的农情采样任务。本文以水稻样地为研究对象,采用便携式无人机Mavic Pro进行航拍。对所获取无人机影像进行预处理生成分辨率为3.95cm/pix的正射影像,采用面向对象的思想,目视评价和ESP工具相结合快速选择了最优分割尺度为300,应用了支持向量机、随机森林和最邻近监督分类方法对影像进行了地物分类和水稻面积快速提取。采用目视解译分类结果进行分类结果和面积精度评价,总体精度最高的方法为最邻近分类法,此时水稻分类用户精度为95%,面积一致性精度为99%。研究结果说明了无人机遥感和自动分类能够在平原水稻种植区快速获取样方内高分辨率影像并提取水稻种植面积,弥补了农田被遮挡时地面调查数据的缺失,为大范围水稻种植面积、产量等信息的计算提供样本和验证依据。  相似文献   

基于不同空间尺度的作物产量集成预报——以江苏一季稻为例          下载免费PDF全文

刘维  宋迎波 《气象科学》2021,41(6):828-834

基于1981-2016年江苏省不同区域一季稻产量序列,计算站点尺度的气温、降水、日照适宜度以及综合气候适宜度,在此基础上构建基于气候适宜指数的作物产量预报模型,开展不同空间尺度的一季稻产量精细化预报。同时,以各主产地市、县一季稻种植面积百分比为权重,加权集成省级产量,开展基于不同空间尺度一季稻产量序列的大区域尺度产量预测。结果表明:(1)江苏省不同区域一季稻气象产量与不同时段气候适宜指数之间存在较高的相关性,基于气候适宜指数的预报方法适用于江苏省不同区域一季稻单产预报。(2)2012-2016年省级尺度模型预报平均准确率高于97.5%,主产地市、县模型平均预报准确率低于省级尺度预报模型,主产县预报准确率年际间波动较大,表明预报区域越小,预报的难度提升。(3)基于气候适宜指数模型的江苏省级、主产地市集成,主产县集成模型预报准确率大部在95%以上,整体上看主产县集成优于主产地市集成,主产地市集成优于省级尺度模型。由此,开展地市级和县级尺度的精细化产量预报可提升省级尺度预报准确率,同时提高县级作物产量预报能力。  相似文献