Search

We developed a set of two precision, small-scale, water balance lysimeters to provide accurate measurements of bare soil evaporation. Each lysimeter comprises a soil tank, a balance assembly with load cell, a wicking drainage system, and a stilling well to measure drained water. Fiberglass wicks installed at the bottom of the soil tanks provide −60 cm of tension to the base of the soil column, and soil water drainage is quantified to close the water balance within the lysimeter. The calibrated lysimeters return mass changes with uncertainties ranging from 3 to 8 g, corresponding to uncertainties of 0.02–0.05 mm of water. Installed at a semi-arid site in northern Nevada, the two lysimeters are filled with uniform construction sand and silt loam. Over a six-month pilot observation period, bare soil evaporation rates of 0.19 and 0.40 mm/day were measured for the construction sand and silt loam, respectively, which is consistent with meteorological data and models of potential evapotranspiration at the site. The design of the lysimeter can be adapted to specific research goals or site restrictions, and these instruments can contribute significantly to our ability to close the soil water balance. [ABSTRACT FROM AUTHOR]

Deep learning models are widely employed in hyperspectral image processing to integrate both spatial features and spectral features, but the correlations between them are rarely taken into consideration. However, in hyperspectral mineral identification, not only the spectral and spatial features of minerals need to be considered, but also the correlations between them are crucial to further promote identification accuracy. In this paper, we propose hierarchical spatial-spectral feature extraction with long short term memory (HSS-LSTM) to explore correlations between spatial features and spectral features and obtain hierarchical intrinsic features for mineral identification. In the proposed model, the fusion spatial-spectral feature is primarily extracted by stacking local spatial features obtained by a convolution neural network (CNN)-based model and spectral information together. To better exploit spatial features and spectral features, an LSTM-based model is proposed to capture correlations and obtain hierarchical features for accurate mineral identification. Specifically, the proposed model shares a uniform objective function, so that all the parameters in the network can be optimized in the meantime. Experimental results on the hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) in the Nevada mining area show that HSS-LSTM achieves an overall accuracy of 94.70% and outperforms other commonly used identification methods. [ABSTRACT FROM AUTHOR]

Bright surfaces across the western U.S. lead to uncertainties in satellite derived aerosol optical depth (AOD) where AOD is typically overestimated. With this in mind, a compact and portable instrument was developed to measure surface albedo on an unmanned aircraft system (UAS). This spectral albedometer uses two Hamamatsu micro-spectrometers (range: 340–780 nm) for measuring incident and reflected solar radiation at the surface. The instrument was deployed on 5 October 2017 in Nevada's Black Rock Desert (BRD) to investigate a region of known high surface reflectance for comparison with albedo products from satellites. It was found that satellite retrievals underestimate surface reflectance compared to the UAS mounted albedometer. To highlight the importance of surface reflectance on the AOD from satellite retrieval algorithms, a 1-D radiative transfer model was used. The simple model was used to determine the sensitivity of AOD with respect to the change in albedo and indicates a large sensitivity of AOD retrievals to surface reflectance for certain combinations of surface albedo and aerosol optical properties. This demonstrates the need to increase the number of surface albedo measurements and an intensive evaluation of albedo satellite retrievals to improve satellite-derived AOD. The portable instrument is suitable for other applications as well. [ABSTRACT FROM AUTHOR]