基于无人机光谱遥感的田块尺度蒸散发空间分布估算

针对农田中高分辨率空间模式蒸散量(ET)缺少有效量化的问题,提出一种基于无人机(UAV)估算农田蒸散量的方法。构建了M100型多旋翼无人机搭载FLIR Vue ProR热像仪和Micasense Red Edge多光谱成像仪的采集数据平台;将无人机数据匹配卫星遥感蒸散模型,比较典型单层模型METRIC(Mapping evapotranspiration at high resolution with internalized calibration)模型和典型双层模型RSEB(Remote sensing energy balance)模型在农田中的适用程度;针对RSEB模型的土壤热通量计算方式不适用于农田环境的问题,对模型进行基于多光谱数据的改进;针对模型中温度参数易产生较大误差的问题,基于无人机热像仪数据与实际温度间的关系,对获取的热像仪数据进行校正;将模型计算值与涡度相关系统(OPEC)测量值进行对比。结果表明,结合无人机多光谱数据的RSEB模型经过温度校正可得到结果较为准确的通量数据,显热通量均方根误差为20. 013 W/m~2,平均绝对误差为15. 835 W/m~2,潜热通量均方根误差为40. 202 W/m~2,平均绝对误差为26. 017 W/m~2,进而得到分米级分辨率的农田蒸散量空间分布图。本文估算方法可以有效获取高分辨率空间模式的田间蒸散量,为精准农业灌溉提供技术支持。     

Empirical Modeling of Relationships Between Sorghum Yield and Soil Properties

A crucial part of any site-specific management is the identification of causes of yield variability and assessment of crop requirements. Therefore, relationships between yield and soil properties must be identified. In this study, relationships between sorghum yield and soil properties on a verbosols within a field located in Moree, in northern NSW, Australia, were examined. Measured soil properties included pH; available phosphorus; percent clay, silt and sand; gravimetric moisture content of air-dry soil and at matric potentials corresponding to –1 500 kPa and –33 kPa; percent organic carbon; CEC and exchangeable calcium, magnesium, sodium and potassium and copper, zinc, manganese and iron contents. The exchangeable sodium percentage (ESP) and the Ca/Mg ratio were calculated. We used a number of empirical methods and found that neural networks, projection pursuit regression, generalized additive models and regression trees are good techniques for modeling yield response. However, further comparison of these techniques is needed. By modeling yield response to individual soil properties and using kriging to map yields predicted from these models, it was possible to identify which soil properties limited production in different areas of the field.     

Field-Scale Experiments for Site-Specific Crop Management. Part II: A Geostatistical Analysis

Part II analyses approach C experiments. Field-scale experiments were applied to four wheat fields in the Western Australian wheat belt. Different experimental designs were used two two-dimensional sine-waves, a chequerboard, and a two-factor strip arrangement. In each experiment, the yield associated with a particular treatment was predicted by kriging to where the other treatments were located. Different forms of kriging were investigated. Co-located cokriging, using the previous-season yield map as a covariate, was the most promising. The kriged data were then modelled with polynomial yield response functions. The outcome was a map for each field that described the optimum application of experimental input. The requirements varied continuously across the field, and could justify future site-specific crop management. The two-factor strip experiment was the most successful of those presented; the field on which it was used showed relatively strong responses to the applied inputs. The other sites were affected by lack of rain and/or design flaws. The underlying philosophy is sound, but the method proposed is time-consuming and inefficient. We hope that this paper can stimulate further research on the subject.     

确定田块尺度土壤水力参数不同方法的比较研究

结合不同类型的大田试验数据,分别利用瞬时剖面法、HYDRUS反演方法、直接方法反求土壤水力参数。将所得参数代入HYDRUS正演程序,分别预测试验后期剖面含水率,并与实测值对比。结果表明,瞬时剖面法与HYDRUS反演方法预测结果相近且优于直接方法。在实测范围内,前2种方法所得参数虽略有差异,但预测结果和表征的水力性质基本一致。直接方法由于试验尺度小、对土壤扰动大,结果并不理想。对比求参工作量,2种反演方法小于直接方法。2种反演方法计算成本对比,瞬时剖面法比HYDRUS反演方法更有优势。     

Field-Scale Experiments for Site-Specific Crop Management. Part I: Design Considerations

This is a two-part paper concerned with the role of field-scale experiments in site-specific crop management (SSCM). Part I is a general introduction to experimental design for SSCM, while Part II focuses on applied analysis. All references are listed at the end of Part II. In Part I, we list the goals of SSCM-experimentation, in addition to the classes of valid experimental designs. Three general approaches are proposed for choosing experimental designs: approach A is for experiments concerning management classes, while approaches B and C are alternatives for continuous management. Approaches A and B are comparative and can be analysed with an appropriately modified ANOVA.     

北京郊区田块尺度土壤质量评价

该研究以北京郊区3个典型田块为研究对象,T1与一山地接壤,T2紧邻京承高速公路,T3位于人为活动密集的村落附近。为了评价该区土壤质量以及初步探讨周边环境对其影响,选取土壤肥力因子pH值、有机质、有效磷、速效钾和硝态氮作为土壤肥力质量的评价指标,采用模糊隶属度函数模型分别对研究区T1、T2和T3进行定量评价;选取重金属Cu,Zn,Cr,Pb,Cd,As,Hg作为土壤环境质量评价指标,采用单因子污染指数和内梅罗综合污染指数相结合的方法进行评价。结果表明,T1、T2和T3土壤肥力质量综合指数分别为：0.494、0.624和0.630,土壤养分含量相对均一。T1、T2和T3土壤环境质量内梅罗指数分别为0.876、0.972和0.903,均处于土壤环境质量第Ⅱ级;各田块尤其是T2田块存在重金属污染的潜在风险。综合两方面的评价结果认为,研究区肥力质量良好,土壤环境质量尚清洁,适合作为设施农产品生产基地。T1田块受山地影响,肥力质量较差;T2田块环境质量最差;人为活动密集的村落对T3土壤质量尚未见影响。     

基于时空滤波Sentinel-1时序数据的田块尺度岭南作物分布提取

为解决岭南地区作物制图中光学影像时空覆盖缺失以及作物种植结构复杂导致不确定性高等问题,以满足对高精度作物制图日益迫切的应用需求。该研究提出了一种基于Sentinel-1的双极化时间序列和作物物候信息建立时空维度典型物候特征的作物分类方法,选择广州市南沙区为试验区,通过XGBoost机器学习算法,实现了田块尺度下的不同作物类型的准确识别及种植面积的精细提取。结果表明：1）相比像素时序特征分类结果,经过雷达时空滤波后的田块时序特征分类方法不仅能有效抑制合成孔径雷达（Synthetic Aperture Radar,SAR）影像斑点噪声产生,而且总体分类精度和Kappa系数分别提高了12.5个百分点、0.19;2）与仅基于Sentinel-1(VV+VH)时空滤波后的全年时序特征分类方法相比,在分类过程中添加物候特征变量的方法表现出更高的精度,Kappa系数可达0.91,甘蔗和香蕉播种面积提取精度分别达到82.04%和71.01%。研究结果表明中高分辨率SAR影像（10 m×10 m）时间序列结合XGBoost算法和雷达数据时空滤波策略可实现区域作物准确识别及种植面积提取,同时,可从数据源与分类方法上为岭南地区农业遥感应用提供思路,对该地区农业灾害管理和灾后救助具有重要参考价值。