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[学术文献] 基于无人机多光谱影像的冬小麦返青期变量施氮决策模型研究 进入全文

光谱学与光谱分析

氮素是影响冬小麦生长的重要元素,如何根据冬小麦需求适时变量施用氮肥是现代农业精准施肥研究需要解决的关键问题之一。无人机遥感技术在冬小麦生长情况监测中具有高分辨率、高时效性、低成本等优势,为解决施肥需求监测问题提供了重要数据源。因此研究无人机多光谱影像数据,构建其与冬小麦产量与施肥量之间的关系模型对于精准施肥研究十分重要。选择冬小麦典型生产区山东省桓台县为实验区,布置4种不同施氮水平的田间实验。利用无人机搭载Sequoia多光谱传感器,采集实验区不同氮素施肥水平的冬小麦返青初期多光谱影像,同时测得冬小麦冠层叶绿素含量(soil and plant analyzer development, SPAD)数据及产量数据。通过多光谱影像数据计算获得归一化植被指数(normalized difference vegetation index, NDVI)、叶绿素吸收指数(modified chlorophyll absorption ratio index, MCARI2)等6种形式植被指数,建立无人机多光谱影像植被指数与小麦冠层SPAD值的线性、二阶多项式、对数、指数和幂函数模型,优选地面氮素状况最优植被指数模型,反演冬小麦不同施氮水平的状况,进而根据不同施氮水平与敏感植被指数和冬小麦产量的关系,构建了基于植被指数指标的氮肥变量施肥模型,并将模型应用于同时期小麦多光谱影像。结果如下:(1)地面实测的SPAD值能较好的反映冬小麦施氮水平及生长状况。无人机多光谱数据分区统计结果表明不同施氮水平冬小麦冠层反射率有较大差异性。(2)结构性植被指数与SPAD拟合效果优于其他类型指数。MCARI2的二阶多项式模型精度最优(R2=0.790, RMSE=0.22),其能较好的移除冬小麦返青初期土壤背景等因素的影响,为氮肥敏感植被指数。(3)基于产量-施氮量模型和产量-敏感植被指数模型,构建敏感植被指数的氮肥变量施肥模型为Nr=10 707.63×MCARI22-5 992.36×MCARI2+715.27。通过模型应用生成了实验区冬小麦氮肥变量施肥图,与实际情况具有较高一致性。该研究提出了利用无人机多光谱数据进行冬小麦施氮决策的模型及方法,为冬小麦精准施肥的进一步研究提供了依据。

[学术文献] 基于作物空间物候差异提取黄淮海夏玉米种植面积 进入全文

中国农业气象

考虑大区域内不同纬度间玉米物候差异,利用MODISEVI时序曲线提取黄淮海夏玉米种植面积。基于Landsat影像和MOD13Q1数据集,提取参考区夏玉米MODIS EVI时间序列曲线;根据研究区内农业气象站夏玉米生育期观测数据,构建夏玉米各物候期与纬度的关系,以纬度作为参数修正参考区夏玉米MODIS EVI时序曲线,获取研究区夏玉米EVI标准时序曲线,结合平均绝对距离(MAD)和p-分位数法提取黄淮海平原夏玉米面积。结果表明,利用遥感影像提取的北京、天津、河北、河南以及山东夏玉米面积分别为125.3×103、162.6×103、2231.8×103、2963.6×103和2731.9×103hm2,各省提取精度均达到80%以上。在市级尺度上,决定系数R2为0.82,均方根误差RMSE为147.8×103hm2;在县级尺度上,决定系数R2为0.62,均方根误差RMSE为17.7×103hm2。说明利用本方法能够准确有效地提取大区域内夏玉米种植面积,为其它农作物在大范围内估计种植面积提供新思路。 

[学术文献] 基于Landsat 8和ZY3数据融合的西北干旱区河西走 廊荒漠绿洲土地覆盖类型与蒸散关系研究 进入全文

生态学报

蒸散是地表水热平衡的重要分量,也是陆地生态过程与水文过程之间的重要纽带,尤其在干旱区地-气相互作用、碳循环、水循环等过程所包含的物质与能量交换中占有极其重要的地位。基于Landsat 8遥感影像和资源三号影像(ZY3)的高分辨率植被信息,利用SEBS模型对西北干旱区河西走廊中段临泽绿洲北部区域地表蒸散量进行了估算,并用绿洲内部和绿洲-荒漠过渡带两个通量塔涡动相关数据对模型进行评估,分析了不同土地覆盖类型对蒸散量空间分布的影响。结果表明:(1)SEBS模型模拟值与实测日蒸散值之间拟合效果较好,且在均一地表时(绿洲农田区)估算精度更高(R2=0.96,P<0.001),RMSE、MAE分别为0.84 mm/d、0.56 mm/d;(2)从季节变化来看蒸散量与作物生长密切相关,夏季灌溉和降雨使得研究区水分充足,植被覆盖度高,蒸散量相应增加,在绿洲地区可达5.95 mm/d,而冬季最小仅为0.52 mm/d;(3)从蒸散量的空间变化来看,水体蒸散值最大,其余依次为农田、防护林、裸地和灌木丛,说明除水体外,随着植被覆盖的增大,蒸散量也逐渐增加。通过ZY3影像的高分辨率植被信息与Landsat 8影像热红外数据融合,提高了SEBS模型对该区域蒸散量的模拟效果,增进了我们对绿洲下垫面与大气间水热交换规律、水文过程、生态-水文相互作用的深入理解。

[学术文献] Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery 进入全文

Computers and Electronics in Agriculture

This work is focused on the spatio-temporal monitoring of winter wheat inoculated with various levels of yellow rust inoculum during the entire growth season. A dedicated workflow is devised to obtain time-series five-bands (visible-infrared) aerial imageries with a multispectral camera and an Unmanned Aerial Vehicle. A number of spectral indices are drawn so that the sensitive ones can be identified by statistical dependency analysis; particularly, their discriminating capabilities are evaluated at different stages for both wheat pixel segmentation and yellow rust severity. Then the spatial-temporal changes of sensitive bands/indices are evaluated and analysed quantitatively. A validation field experiment was designed in 2017–2018 by inoculating wheat with one of the six levels of yellow rust inoculum. Five-bands RedEdge camera on-board DJI S1000 was used to capture aerial images at eight time points covering the entire growth season at an altitude of about 20 meters with a ground resolution of 1–1.5 cm/pixel. Experimental results via spatio-temporal analysis show that: (1) various bands/indices should be used for wheat segmentation at different stages; (2) no bands/indices differences are observed for yellow rust inoculated wheat plots in both incubation stage (9 days after inoculation) and early onset stage (25 days after inoculation); (3) NIR and Red are the sensitive bands for wheat yellow rust in disease stages (45 days after inoculation); and their normalized difference NDVI index provides an even higher statistical dependency; (4) bands/indices’ sensitivity to yellow rust changes over time and decreases in later Heading stage until being very low in Ripening stage (61 days after inoculation). This experimental study provides a crucial guidance for future early spatio-temporal yellow rust monitoring at farmland scales.

[科技图书] The Climate-Smart Agriculture Papers 进入全文

Springer Link

This book is open access under a CC BY 4.0 license. This volume shares new data relating to Climate-Smart Agriculture (CSA), with emphasis on experiences in Eastern and Southern Africa. The book is a collection of research by authors from over 30 institutions, spanning the public and private sectors, with specific knowledge on agricultural development in the region discussed. The material is assembled to answer key questions on the following five topic areas: (1) Climate impacts: What are the most significant current and near future climate risks undermining smallholder livelihoods? (2) Varieties: How can climate-smart varieties be delivered quickly and cost-effectively to smallholders? (3) Farm management: What are key lessons on the contributions from soil and water management to climate risk reduction and how should interventions be prioritized?  (4) Value chains: How can climate risks to supply and value chains be reduced? and (5) Scaling up: How can most promising climate risks reduction strategies be quickly scaled up and what are critical success factors? Readers who will be interested in this book include students, policy makers, and researchers studying climate change impacts on agriculture and agricultural sustainability. 

[科技报告] 《中国数字乡村发展报告(2019)》发布 进入全文

农业农村部

11月15日,《中国数字乡村发展报告(2019)》在2019年数字农业农村发展论坛上发布。报告在中央网信办信息化发展局、农业农村部市场与信息化司指导下,由农业农村信息化专家咨询委员会编制。当前,新一代信息技术创新空前活跃,不断催生新产品、新模式、新业态,推动全球经济格局和产业形态深度变革,为数字乡村发展创造了前所未有的重大机遇。党的十八大以来,以习近平同志为核心的党中央作出一系列重要战略部署。《中共中央国务院关于实施乡村振兴战略的意见》和《乡村振兴战略规划(2018—2022年)》提出,要实施数字乡村战略,大力发展数字农业。2019年5月,中共中央办公厅、国务院办公厅印发《数字乡村发展战略纲要》,明确将数字乡村作为乡村振兴的战略方向,加快信息化发展,整体带动和提升农业农村现代化发展。各地各部门坚决贯彻落实党中央、国务院部署要求,扎实推动数字乡村建设,取得了阶段性成效。《中国数字乡村发展报告(2019)》全面总结了我国数字乡村建设的阶段性进展和经验探索,分析了当前面临的形势,展望了未来发展前景,是对当前数字乡村发展情况的集中呈现。

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