基于光谱反射信息的作物单产估测模型研究进展

及时准确地估测区域作物单产信息,对于粮食安全预警、粮食贸易流通,以及农业可持续发展都具有非常重要的意义。基于光谱反射信息的遥感技术,能够实时获取作物和土壤在不同时间和空间尺度下的分布信息,为区域作物单产估测研究提供了新的机遇和挑战。在简单介绍作物反射光谱特性和作物单产影响因素的基础上,分经验模型、半经验半机理模型和机理模型三部分,详细论述了基于光谱反射信息的作物单产遥感估测模型的国内外研究进展,并指出基于作物生长机理模型与多时相遥感信息同化技术的研究,应该是未来区域作物单产估测的重要发展方向之一。今后应该重点加强作物冠层关键参数(如叶面积指数、叶绿素浓度、作物吸收光合有效辐射系数、植被覆盖率等)的定量反演研究,同时加强多源遥感数据替代和整合技术研究,以及作物模型与遥感信息同化关键技术研究,以进一步改善单产估测精度和提高系统可运行性。     

基于遥感数据与作物生长模型同化的作物长势监测

遥感观测和作物生长模型模拟是进行农作物长势监测的2种有效手段,并具有较好的互补性,构建二者的同化系统是目前农业遥感研究领域的热点。同化涉及多学科的交叉集成,十分有必要将同化方法中的模型、算法、数据进行集成,构建基础作物模型同化系统平台,降低科学研究和应用的难度。采用模块化结构设计,将同化系统构建所需的主要模型、算法、数据等进行有机结合和无缝集成,实现基于极快速模拟退火算法的遥感数据与CERES-Wheat作物生长模型的同化原型系统构建。此外,通过所开发的系统利用地面高光谱作为遥感数据,通过同化小麦叶面积指数对同化系统进行了检验和初步应用。同化LAI与实测结果较好的拟合度,表明所开发的同化系统基本可行,能为遥感技术与作物模型的基础研究和应用提供一个平台。     

应用CropSyst模型模拟东北黑土区春小麦生长

Available water and fertilizer have been the main limiting factors for yields of spring wheat, which occupies a large area of the black soil zone in northeast China; thus, the need to set up appropriate models for scenario analysis of cropping system models has been increasing. The capability of CropSyst, a cropping system simulation model, to simulate spring wheat growth of a widely grown spring cultivar, ‘Longmai 19＇, in the black soil zone in northeast China under different water and nitrogen regimes was evaluated. Field data collected from a rotation experiment of three growing seasons （1992- 1994） were used to calibrate and validate the model. The model was run for 3 years by providing initial conditions at the beginning of the rotation without reinitializing the model in later years in the rotation sequence. Crop input parameters were set based on measured data or taken from CropSyst manual. A few cultivar-specific parameters were adjusted within a reasonable range of fluctuation. The results demonstrated the robustness of CropSyst for simulating evapotranspiration, aboveground biomass, and grain yield of ＇Longmai 19＇ spring wheat with the root mean square errors being 7%, 13% and 13% of the observed means for evapotranspiration （ET）, grain yield and aboveground biomass, respectively. Although CropSyst was able to simulate spring production reasonably well, further evaluation and improvement of the model with a more detailed field database was desirable for agricultural systems in northeast China.     

区域水土流失动态监测评价的大数据分析基础与对策

区域水土流失动态监测评价的目的是掌握水土流失状况及其防治成效,建成并运用大数据进行分析,可充分利用数据资源,提高监测成果的科学性、完整性和时效性.通过大数据采集与知识挖掘,构建区域水土流失动态监测评价数据集(或集合),再利用云网络计算平台,实现基于大数据的动态监测与分析评价.文章针对区域水土流失动态监测与评价的方法和数据基础,从完善顶层设计入手,实现大数据融合与共享,推进数据标准化建设,创新大数据和云网络服务方式,完善监测站网信息采集和大数据网络基础设施建设,探索基于大数据的区域水土流失动态监测评价方法和实现途径,并提出其大数据构成与分析流程,以及应用大数据分析的对策与建议.     

The development of a methodology using fuzzy logic to assess the performance of cropping systems based on a case study of maize in the Po Valley

The development of tools for evaluating the sustainability of cropping systems is fundamental to providing reliable information on crop performance. In this study, an integrated index [Performance Index (PI)] was developed based on environmental, production and cost variables. In this approach, data are aggregated using a fuzzy logic‐based procedure to deal with the inherent subjectivity at each aggregation step. The PI was tested for three cropping systems in the Po Valley (northern Italy) where maize was grown using minimum tillage, sod seeding and conventional tillage. The methodology proved to be effective for evaluating the three systems. In contrast to other methods for evaluating cropping systems such as those based on agro‐ecological indicators (AEIs), direct aggregation of measured variables reduces the risk of losing information because the level of abstraction in the mathematical formalization of AEIs is usually higher. This advantage is counterbalanced by an increase in the data needed for the evaluation process. The use of domain experts and the mathematical management of subjective decisions should encourage the use of the methodology in other regions and cropping systems.     

遥感订正作物种植结构数据对提高灌区SWAT模型精度的影响

为确保灌区水文过程与营养物流失过程模拟更接近于真实过程,进一步提高模拟精度,该研究综合考虑作物种植结构空间位置的准确性与作物种植结构数据的精度2个因素,利用GF-1 16 m遥感影像对耕地作物进行分类提取,并对土地利用类型图进行修正,从而分析比较作物种植结构空间位置的订正与作物种植结构数据精度的提高分别对SWAT（Soil and Water Assessment Tool）模型模拟精度的影响。结果表明：作物种植结构空间位置的订正或作物种植结构数据精度的提高均可提高径流和硝态氮模拟效率。经作物种植结构空间位置的订正和数据精度的提高可使得模型在径流模拟中,率定期和验证期决定系数R2分别达到了0.76和0.82,效率系数分别达到了0.69和0.79,相对误差分别降低至3.50%和-0.30%;在硝态氮模拟中,率定期和验证期决定系数R2分别达到了0.70和0.63,效率系数分别达到了0.55和0.53,相对误差分别降低至10.06%和6.42%。综合订正作物种植结构空间位置和提高作物种植结构数据精度可有效提高SWAT模型在灌区的模拟精度。