当前位置: X-MOL 学术IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Soil Moisture Monitoring at Plant Root Zone by using Phenology as Context in Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2020-01-01 , DOI: 10.1109/jstars.2020.3021990
Ayda Aktas , Burak Berk Ustundag

In this study, the phenological behavior and energy balance of plants are used as a sensory mechanism for root-zone soil moisture monitoring using both in-situ and satellite remote sensing data. The commonly used in-situ measurements are not feasible for mapping soil moisture at large-scale agricultural areas. Local direct root-zone soil moisture measurements cannot be reliably interpolated owing to the high spatial variability of soil structure and the vegetative content. Remote sensing methods are negatively affected by vegetation coverage and density regarding penetration and backscattering characteristics. In order to overcome these limitations, we propose a root-zone soil moisture estimation method utilizing a context-aware data clustering process, which can be applied prior to any statistical analysis, for empirical evaluation of data. In this aspect, the crops’ phenological stages and soil–air temperature differences are defined as the two contexts for data clustering. Parameters such as canopy–air temperature difference, land surface temperature, and solar radiation with respect to plant energy and water processes are used for the analysis. The proposed model is utilized using piecewise linear regression of data obtained from 16 rainfed wheat parcels distributed across Turkey, under different climatic and topographic conditions. It is shown that the proposed context-aware data clustering process enables the nonlinear plant behavior to be analyzed linearly. The correlation value of the whole season increased from 21% to a range between 78% and 95% for different clusters. The outliers became relevant and the parameters became significant after the proposed context-aware data clustering.

中文翻译:

以物候为背景的遥感植物根区土壤水分监测

在这项研究中,植物的物候行为和能量平衡被用作使用原位和卫星遥感数据进行根区土壤水分监测的感官机制。通常使用的原位测量对于绘制大规模农业区的土壤湿度图是不可行的。由于土壤结构和植被含量的高空间变异性,不能可靠地内插局部直接根区土壤湿度测量值。遥感方法受到植被覆盖率和密度的负面影响,涉及穿透和反向散射特性。为了克服这些限制,我们提出了一种利用上下文感知数据聚类过程的根区土壤水分估计方法,该方法可以在任何统计分析之前应用于数据的实证评估。在这方面,作物的物候阶段和土壤-空气温度差异被定义为数据聚类的两个背景。与植物能量和水过程相关的诸如冠层-空气温度差、地表温度和太阳辐射等参数用于分析。在不同的气候和地形条件下,使用从分布在土耳其各地的 16 个雨育小麦地块获得的数据的分段线性回归来利用所提出的模型。结果表明,所提出的上下文感知数据聚类过程能够对非线性设备行为进行线性分析。对于不同的集群,整个季节的相关值从 21% 增加到 78% 到 95% 之间的范围。
更新日期:2020-01-01
down
wechat
bug