Measuring and modeling the effect of surface moisture on the spectral reflectance of coastal beach sand

Land Surface Parameterization at Exposed Playa and Desert Region to Support Dust Emissions Estimates in Southern California, United States

Remote sensing technologies provide a unique opportunity to identify ground surfaces that are more susceptible to dust emissions at a large scale. As part of the Salton Sea Air Quality Mitigation Program (SSAQMP) of the Imperial Irrigation District (IID), efforts have been made to improve our understanding of fugitive, wind-blown dust emissions around the Salton Sea region in Southern California, United States. Field campaigns were conducted for multiple years to evaluate surface conditions and measure the dust emissions potential in the area. Data collected during the field work were coupled with remote sensing imagery and data mining techniques to map surface characteristics that are important in identifying dust emissions potential. Around the playa domain, surface crust type, sand presence, and soil moisture were estimated. Geomorphic surface types were mapped in the desert domain. Overall accuracy ranged from 91.7% to 99.4% for the crust type mapping. Sand presence mapping showed consistent and slightly better accuracy, ranging from 96.2% to 99.7%. Soil moisture assessment agreed with precipitation records. Geomorphic mapping in the desert domain achieved accuracy above 93.5%, and the spatial pattern was consistent with previous studies. These land surface condition assessments provide important information to support dust emissions estimates in the region.

A Numerical Study on the Impact of Building Dimensions on Airflow Patterns and Bed Morphology around Buildings at the Beach

The attractiveness of beaches to people has led, in many places, to the construction of buildings at the beach–dune interface. Buildings change the local airflow patterns which, in turn, alter the sediment transport pathways and magnitudes. This induces erosion and deposition patterns around the structures. In this study, a numerical model is developed using the open-source computational fluid dynamics solver OpenFOAM. First, the model is used to predict the airflow patterns around a single rectangular building. The model predictions are validated with wind-tunnel data, which show good agreements. Second, a reference beach building is introduced and then the building dimensions are increased in length, width and height, each up to three times the reference building dimension. The impact of each dimensional extent on the near-surface airflow patterns is investigated. The results show that the near-surface airflow patterns are least dependent on the length of the building in the wind direction and they depend most on the width of the building perpendicular to the wind direction. Third, the convergence of the third-order horizontal near-surface velocity field is calculated to interpret the impact of changes in airflow patterns on potential erosion and deposition patterns around the building. The numerical predictions are compared with the observed erosion and sedimentation patterns around scale models in the field. The comparisons show satisfactory agreements between numerical results and field measurements.

Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data

Beach Surface Moisture (BSM) is a key attribute in the coastal investigations of land-atmospheric water and energy fluxes, groundwater resource budgets and coastal beach/dune development. In this study, an attempt has been made for the first time to estimate BSM from terrestrial LiDAR intensity data based on the Support Vector Regression (SVR). A long-range static terrestrial LiDAR (Riegl VZ-2000) was adopted to collect point cloud data of high spatiotemporal resolution on the Ostend-Mariakerke beach, Belgium. Based on the field moisture samples, SVR models were developed to retrieve BSM, using the backscattered intensity, scanning ranges and incidence angles as input features. The impacts of the training samples' size and density on the predictive accuracy and generalization capability of the SVR models were fully investigated based on simulated BSM-intensity samples. Additionally, we compared the performance of the SVR models for BSM estimation with the traditional Stepwise Regression (SR) method and the Artificial Neural Network (ANN). Results show that SVR could accurately retrieve the BSM from the backscattered intensity with high reproducibility (average test RMSE of 0.71% ± 0.02% and R² of 0.98% ± 0.002%). The Radial Basis Function (RBF) was the most suitable kernel for SVR model development in this study. The impacts of scanning geometry on the intensity could also be accurately corrected in the process of estimating BSM by the SVR models. However, compared to the SR method, the predictive accuracy and generalization performance of SVR models were significantly dependent on the training samples' coverage, size and distribution, suggesting the need for the training samples of uniform distribution and representativeness. The minimum size of training samples required for SVR model development was 54. Under this condition, SVR performed similarly to ANN with a test RMSE of 1.06%, but SVR still performed acceptably (with an RMSE of 1.83%) even using extremely few training samples (only 16 field samples of uniform distribution), far better than the ANN (with an RMSE of 4.02%).

Actual Evapotranspiration from UAV Images: A Multi-Sensor Data Fusion Approach

Multispectral imaging using Unmanned Aerial Vehicles (UAVs) has changed the pace of precision agriculture. Actual evapotranspiration (ETa) from the very high spatial resolution of UAV images over agricultural fields can help farmers increase their production at the lowest possible cost. ETa estimation using UAVs requires a full package of sensors capturing the visible/infrared and thermal portions of the spectrum. Therefore, this study focused on a multi-sensor data fusion approach for ETa estimation (MSDF-ET) independent of thermal sensors. The method was based on sharpening the Landsat 8 pixels to UAV spatial resolution by considering the relationship between reference ETa fraction (ETrf) and a Vegetation Index (VI). Four Landsat 8 images were processed to calculate ETa of three UAV images over three almond fields. Two flights coincided with the overpasses and one was in between two consecutive Landsat 8 images. ETrf was chosen instead of ETa to interpolate the Landsat 8-derived ETrf images to obtain an ETrf image on the UAV flight. ETrf was defined as the ratio of ETa to grass reference evapotranspiration (ETr), and the VIs tested in this study included the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Land Surface Water Index (LSWI). NDVI performed better under the study conditions. The MSDF-ET-derived ETa showed strong correlations against measured ETa, UAV- and Landsat 8-based METRIC ETa. Also, visual comparison of the MSDF-ET ETa maps was indicative of a promising performance of the method. In sum, the resulting ETa had a higher spatial resolution compared with thermal-based ETa without the need for the Albedo and hot/cold pixels selection procedure. However, wet soils were poorly detected, and in cases of continuous cloudy Landsat pixels the long interval between the images may cause biases in ETa estimation from the MSDF-ET method. Generally, the MSDF-ET method reduces the need for very high resolution thermal information from the ground, and the calculations can be conducted on a moderate-performance computer system because the main image processing is applied on Landsat images with coarser spatial resolutions.

Remote Sensing-Based Automatic Detection of Shoreline Position: A Case Study in Apulia Region

Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

Sensitivity of Near-Infrared Permanent Laser Scanning Intensity for Retrieving Soil Moisture on a Coastal Beach: Calibration Procedure Using In Situ Data

Anthropogenic activities and climate change in coastal areas require continuous monitoring for a better understanding of environmental evolution and for the implementation of protection strategies. Surface moisture is one of the important drivers of coastal variability because it highly affects shoreward sand transport via aeolian processes. Several methods have been explored for measuring surface moisture at different spatiotemporal resolutions, and in recent years, light detection and ranging (LiDAR) technology has been investigated as a remote sensing tool for high-spatiotemporal-resolution moisture detection. The aim of the present study is the assessment of the performance of a permanent terrestrial laser scanner (TLS) with an original setting located on a high position and hourly scanning of a wide beach area stretching from a swash zone to the base of a dune in order to evaluate the soil moisture at a high spatiotemporal resolution. The reflectance of a Riegl-VZ2000 located in Noordwijk on the Dutch coast was used to assess a new calibration curve that allows the estimation of soil moisture. Three days of surveys were conducted to collect ground-truth soil moisture measurements with a time-domain reflectometry (TDR) sensor at 4 cm depth. Each in situ measurement was matched with the closest reflectance measurement provided by the TLS; the data were interpolated using a non-linear least squares method. A calibration curve that allowed the estimation of the soil moisture in the range of 0–30% was assessed; it presented a root-mean-square error (RMSE) of 4.3% and a coefficient of determination (R-square) of 0.86. As an innovative aspect, the calibration curve was tested under different circumstances, including weather conditions and tidal levels. Moreover, the TDR data collected during an independent survey were used to validate the assessed curve. The results show that the permanent TLS is a highly suitable technique for accurately evaluating the surface moisture variations on a wide sandy beach area with a high spatiotemporal resolution.

Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

Measuring Surface Moisture on a Sandy Beach based on Corrected Intensity Data of a Mobile Terrestrial LiDAR

Surface moisture plays a key role in limiting the aeolian transport on sandy beaches. However, the existing measurement techniques cannot adequately characterize the spatial and temporal distribution of the beach surface moisture. In this study, a mobile terrestrial LiDAR (MTL) is demonstrated as a promising method to detect the beach surface moisture using a phase-based Z&F/Leica HDS6100 laser scanner mounted on an all-terrain vehicle. Firstly, two sets of indoor calibration experiments were conducted so as to comprehensively investigate the effect of distance, incidence angle and sand moisture contents on the backscattered intensity by means of sand samples with an average grain diameter of 0.12 mm. A moisture estimation model was developed which eliminated the effects of the incidence angle and distance (it only relates to the target surface reflectance). The experimental results reveal both the distance and incidence angle influencing the backscattered intensity of the sand samples. The standard error of the moisture model amounts to 2.0% moisture, which is considerably lower than the results of the photographic method. Moreover, a field measurement was conducted using the MTL system on a sandy beach in Belgium. The accuracy and robustness of the beach surface moisture derived from the MTL data was evaluated. The results show that the MTL is a highly suitable technique to accurately and robustly measure the surface moisture variations on a sandy beach with an ultra-high spatial resolution (centimeter-level) in a short time span (12 × 200 m per minute).

Development of a Multimode Field Deployable Lidar Instrument for Topographic Measurements of Unsaturated Soil Properties: Instrument Description

The hydrological and mechanical behavior of soil is determined by the moisture content, soil water (matric) potential, fines content, and plasticity. However, these parameters are often difficult or impractical to determine in the field. Remote characterization of soil parameters is a non-destructive data collection process well suited to large or otherwise inaccessible areas. A ground-based, field-deployable remote sensor, called the soil observation laser absorption spectrometer (SOLAS), was developed to collect measurements from the surface of bare soils and to assess the in-situ condition and essential parameters of the soil. The SOLAS instrument transmits coherent light at two wavelengths using two, continuous-wave, near-infrared diode lasers and the instrument receives backscattered light through a co-axial 203-mm diameter telescope aperture. The received light is split into a hyperspectral sensing channel and a laser absorption spectrometry (LAS) channel via a multi-channel optical receiver. The hyperspectral channel detects light in the visible to shortwave infrared wavelengths, while the LAS channel filters and directs near-infrared light into a pair of photodetectors. Atmospheric water vapor is inferred using the differential absorption of the on- and off-line laser wavelengths (823.20 nm and 847.00 nm, respectively). Range measurement is determined using a frequency-modulated, self-chirped, coherent, homodyne detection scheme. The development of the instrument (transmitter, receiver, data acquisition components) is described herein. The potential for rapid characterization of physical and hydro-mechanical soil properties, including volumetric water content, matric potential, fines content, and plasticity, using the SOLAS remote sensor is discussed. The envisioned applications for the instrument include assessing soils on unstable slopes, such as wildfire burn sites, or stacked mine tailings. Through the combination of spectroradiometry, differential absorption, and range altimetry methodologies, the SOLAS instrument is a novel approach to ground-based remote sensing of the natural environment.

Soil directioal (biconical) reflectance in the principal plane with varied illumination angle under dry and saturated conditions

Change in directional (biconical) spectral reflectance was monitored for three soil samples under air dry and saturated conditions in the laboratory. The illumination angle was set consecutively at θ_i  = -10°, -40°, and -70° (left side of the sample on the principal plane), while the observation angle ranged from θ_o  = -60° to + 60° (both sides of the sample in the principal plane) in 5° increments. The soil samples were chosen to represent a variety of soil properties. Emphasis is on observations that illustrate the changes in the directional and spectral distribution of soil reflectance when the soil is dry or saturated.