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Effect of soil property change on soil moisture profile estimation through data fusion

机译:数据融合对土壤性质变化对土壤水分剖面估算的影响

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Crop yield is strongly correlated to soil moisture and its variation with respect to phenological stage of the plant. For this reason spatial and temporal distribution of soil moisture is substantial for agricultural planning, irrigation and water resource management. A soil profile estimation model where agro-meteorological and remote sensing data sets are fused by wavelet neural network is developed for large scale multi sensor agricultural monitoring network in Turkey (TARBIL). Temporal rain, parcel based evapotranspiration, amount of irrigation together with fractional vegetation cover have been used as inputs of multiple-input time-delay wavelet neural network for spatiotemporal multi-depth soil moisture profile estimation. An error source in soil moisture estimation is discontinuity in soil property change. Meanwhile additional estimation on z-axis for profile data is also effective in complexity of the problem. In this study, we have investigated effect of some physical and chemical property changes and proposed possible improvements on the soil profile estimation model.
机译:作物产量与土壤水分及其相对于植物物候阶段的变化密切相关。因此,土壤水分的时空分布对于农业计划,灌溉和水资源管理而言是至关重要的。针对土耳其的大型多传感器农业监测网络,开发了一种通过小波神经网络融合农业气象和遥感数据集的土壤剖面估计模型。时空降雨,基于包裹的蒸散量,灌溉量以及植被覆盖率已被用作多输入时延小波神经网络的输入,用于时空多深度土壤湿度剖面估算。土壤水分估算的一个误差源是土壤性质变化的不连续性。同时,在z轴上进行轮廓数据的附加估计在问题的复杂性方面也是有效的。在这项研究中,我们研究了一些物理和化学性质变化的影响,并提出了对土壤剖面估算模型的可能改进。

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