Ms. Yaling Lu Profile

Yaling Lu

at China Meteorological Administration

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Author

Publications (2)

Proceedings Article | 24 October 2018 Paper

Climate change and its impact on drought in Eastern Gansu rainfed agricultural area in Northwest China in the last thirty years

Ni Guo, Yaling Lu, Ying Cheng, Yiping Li, Sha Sha, Wang Wei, Jie Zheng

Proceedings Volume 10776, 107760X (2018) https://doi.org/10.1117/12.2324652

KEYWORDS: Soil science, Agriculture, Climate change, Climatology, Temperature metrology, Vegetation, Environmental sensing, Meteorology, Satellites, Remote sensing

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SPIE Journal Paper | 13 June 2018

Soil moisture estimation for spring wheat in a semiarid area based on low-altitude remote-sensing data collected by small-sized unmanned aerial vehicles

Wei Wang, Xiaoping Wang, Lijuan Wang, Yaling Lu, Yaohui Li, Xuying Sun

JARS, Vol. 12, Issue 02, 022207, (June 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.022207

KEYWORDS: Soil science, Unmanned aerial vehicles, Data modeling, Remote sensing, Vegetation, Agriculture, Infrared radiation, Thermography, Cameras, Error analysis

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As an important component of the Earth’s ecosystem, soil moisture plays a vital role in the global water cycle and serves as an important parameter in the study of hydrology, meteorology, and agroecology. Based on the energy balance theory of underlying surface, the atmospheric temperature data recorded by an automatic weather station as well as unmanned aerial vehicle (UAV)-borne thermal infrared and multispectral remote-sensing data were used to establish inversion models of relative soil moisture at different depths based on remote-sensing UAV data and date from near-ground quadrats, respectively. Spatial differences and data accuracy verification were then performed using the 2017 spring wheat moisture data as a control. The results showed that: (1) the relative moisture of farmland soil can be effectively estimated using the proposed soil moisture inversion model. In terrestrial ecosystems, the ratio of actual to potential evapotranspiration, which is often used to characterize potential drought levels, is linearly correlated to soil moisture at different depths; (2) during the inversion of farmland soil moisture, the UAV-based observation method is superior to the near-ground quadrat observation method in both efficiency and accuracy. In addition, the relative soil moisture estimation model based on UAV data has a high accuracy, with R2 reaching 0.629, and a root mean square error (RMSE) of <0.100; and (3) the number and size of quadrats are important factors affecting the inversion accuracy. The data collected by the UAVs covered a wide range and had high spatial matching degree at the field scale. Especially, during estimation of the relative moisture of surface soil (0 to 10 and 0 to 20 cm), the linear fitting between the inversion model based on UAV data and the measured value was optimal. The error was minimal (RMSE < 0.07) and R2 was >0.714, so this method is more suitable for estimating and dynamically monitoring relative soil moisture of farmland at the field scale.

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