Carolina Doña

Integrated satellite data fusion and mining for monitoring lake water quality status of the Albufera de Valencia in Spain

Lake eutrophication is a critical issue in the interplay of water supply, environmental management, and ecosystem conservation. Integrated sensing, monitoring, and modeling for a holistic lake water quality assessment with respect to multiple constituents is in acute need. The aim of this paper is to develop an integrated algorithm for data fusion and mining of satellite remote sensing images to generate daily estimates of some water quality parameters of interest, such as chlorophyll a concentrations and water transparency, to be applied for the assessment of the hypertrophic Albufera de Valencia. The Albufera de Valencia is the largest freshwater lake in Spain, which can often present values of chlorophyll a concentration over 200 mg m(-3) and values of transparency (Secchi Disk, SD) as low as 20 cm. Remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) and Enhance Thematic Mapper (ETM+) images were fused to carry out an integrative near-real time water quality assessment on a daily basis. Landsat images are useful to study the spatial variability of the water quality parameters, due to its spatial resolution of 30 m, in comparison to the low spatial resolution (250/500 m) of MODIS. While Landsat offers a high spatial resolution, the low temporal resolution of 16 days is a significant drawback to achieve a near real-time monitoring system. This gap may be bridged by using MODIS images that have a high temporal resolution of 1 day, in spite of its low spatial resolution. Synthetic Landsat images were fused for dates with no Landsat overpass over the study area. Finally, with a suite of ground truth data, a few genetic programming (GP) models were derived to estimate the water quality using the fused surface reflectance data as inputs. The GP model for chlorophyll a estimation yielded a R(2) of 0.94, with a Root Mean Square Error (RMSE) = 8 mg m(-3), and the GP model for water transparency estimation using Secchi disk showed a R(2) of 0.89, with an RMSE = 4 cm. With this effort, the spatiotemporal variations of water transparency and chlorophyll a concentrations may be assessed simultaneously on a daily basis throughout the lake for environmental management.

Empirical Relationships for Monitoring Water Quality of Lakes and Reservoirs Through Multispectral Images

Remote sensing techniques can be used to estimate water quality variables such as chlorophyll\mbi a, total suspended particles, and water transparency. This paper describes empirical algorithms for the estimation of these variables using Landsat Thematic Mapper (TM) data. Ground data were taken from several Spanish lakes covering a variety of trophic statuses, ranging from oligotrophic to hypereutrophic. The studied lakes were the Albufera de Valencia and lakes and ponds of the Southeast Regional Park in Madrid. Empirical equations were obtained to estimate chlorophyll\mbi a from the ratio in reflectance values between bands 2 and 4 of TM ( R2\mmb = 0.66, p\lt 0.001), transparency [Secchi disk (SD)] from reflectance in band 2 ( R2\mmb = 0.80, pbf \lt 0.001), and total suspended particles from reflectance in band 4 ( R2 \mmb = 0.92, p\lt 0.001). The spectral equivalence between TM and the recent satellite Deimos-1 was also tested. By applying the proposed algorithms to this new sensor, the temporal resolution is improved by up to 3 days, which increases spatial resolution to 22 m. The algorithms were validated using three Deimos-1 scenes of the Albufera de Valencia together with ground measurements. Results of this validation showed root-mean-square errors (RMSEs) of 40\nbspmg·m\mmb-3 for Chl-\mbi a (data range: 32\mmb - 238\nbspmg·m-3), 10\nbspmg·L\mmb -1 for total suspended solid (TSS) (data range: 25\mmb -89\nbspmg·L\mmb -1), and 0.10 m for SD (data range: 0.17-0.40 m). In any case, these results show the potential of Deimos-1 as a substitute of TM in water quality monitoring in small/medium water bodies, providing continuity to three decades of TM imagery.

Thermal-Infrared Spectral and Angular Characterization of Crude Oil and Seawater Emissivities for Oil Slick Identification

Previous work has shown that the emissivity of crude oil is lower than that of the seawater in the thermal-infrared (TIR) spectrum. Thus, oil slicks cause an emissivity decrease relative to the seawater in that region. The aim of this paper was to carry out experimental measurements to characterize the spectral and angular variations of crude oil and seawater emissivities. The results showed that the crude oil emissivity is lower than the seawater emissivity and that it is essentially flat in the atmospheric window of 8-13 μm. The crude oil emissivity has a marked emissivity decrease with the angle (from 0.956 ± 0.005 at 15 ° to 0.873 ± 0.007 at 65 °), which is even higher than that of the seawater, and thus, the seawater-crude emissivity difference increases with the angle (from +0.030 ±0.007 at close-to-nadir angles up to +0.068 ±0.010 in average at 65 °). In addition, the experimental results were checked by using the dual-angle viewing capability of the Environmental Satellite Advanced Along-Track Scanning Radiometer (ENVISAT-AATSR) images (i.e., 0 °-22 ° and 53 °-55 ° for the nadir and forward views, respectively), with the data acquired during the BP Deepwater Horizon oil slick in 2010. The objective was to explore its applicability to satellite observations. Nadir-forward emissivity differences of +0.028 and +0.017 were obtained for the oil slick and the surrounding clean seawater, respectively. The emissivity differences between the seawater and the oil slick were +0.035 and +0.046 for the nadir and forward views, respectively, which was in agreement with the experimental data. The increase in the seawater-crude emissivity difference with the angle gives significant differences for off-nadir observation angles, showing a new chance of crude oil slick identification from satellite TIR data.

SMOS Level-2 Soil Moisture Product Evaluation in Rain-Fed Croplands of the Pampean Region of Argentina

A field campaign was carried out to evaluate the Soil Moisture (SM) MIR_SMUDP2 product (v5.51) generated from the data of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) aboard the Soil Moisture and Ocean Salinity (SMOS) mission. The study area was the Pampean Region of Argentina, which was selected because it is a vast area of flatlands containing quite homogeneous rain-fed croplands, which are considered SMOS nominal land uses and hardly affected by radio-frequency interference contamination. Transects of ground handheld SM measurements were performed using ThetaProbe ML2x probes within four Icosahedral Snyder Equal Area Earth (ISEA) grid nodes, where permanent SM stations are located. The campaign results showed a negative bias of -0.02 m3m-3 between concurrent SMOS data and ground SM measurements, which means a slight SMOS underestimation, and a standard deviation of ±0.06 m3m-3. Additionally, a good correlation was obtained between the handheld SM measurements taken during the campaign and the permanent SM station data within a node, which pointed out that the station data could be used as reference data to evaluate the SMOS product over a longer temporal period. SMOS-retrieved data were also compared with station mean SM values from 2012 to 2014. A general SMOS underestimation of -0.05 m3m-3 was observed, with a standard deviation of ±0.04 m3m-3, which yields an uncertainty of ±0.07 m3m-3 for the SMOS product. Although the random error meets the SMOS missions goal of ±0.04 m3m-3, the product overall uncertainty is higher than that due to the significant dry bias, which is also found in other regions of the world.

Single band atmospheric correction tool for thermal infrared data: application to Landsat 7 ETM+

Atmospheric correction of Thermal Infrared (TIR) remote sensing data is a key process in order to obtain accurate land surface temperatures (LST). Single band atmospheric correction methods are used for sensors provided with a single TIR band. Which employs a radiative transfer model using atmospheric profiles over the study area as inputs to estimate the atmospheric transmittances and emitted radiances. Currently, TIR data from Landsat 5-TM, Landsat 7-ETM+ and Landsat 8-TIRS can be atmospherically corrected using the on-line Atmospheric Correction Parameter Calculator (ACPC, http://atmcorr.gsfc.nasa.gov). For specific geographical coordinates and observation time, the ACPC provides the atmospheric transmittance, and both upwelling and downwelling radiances, which are calculated from MODTRAN4 radiative transfer simulations with NCEP atmospheric profiles as inputs. Since the ACPC provides the atmospheric parameters for a single location, it does not account for their eventual variability within the full Landsat scene. The new Single Band Atmospheric Correction (SBAC) tool provides the geolocated atmospheric parameters for every pixel taking into account their altitude. SBAC defines a three-dimensional grid with 1°×1° latitude/longitude spatial resolution, corresponding to the location of NCEP profiles, and 13 altitudes from sea level to 5000 meters. These profiles are entered in MODTRAN5 to calculate the atmospheric parameters corresponding to a given pixel are obtained by weighted spatial interpolation in the horizontal dimensions and linear interpolation in the vertical dimension. In order to compare both SBAC and ACPC tools, we have compared with ground measurements the Landsat-7/ETM+ LST obtained using both tools over the Valencia ground validation site.

Discrepancies between eddy covariance and lysimeter measurements in the assessment of energy balance modeling in vineyards

Remote sensing-based models are a potential technique when evapotranspiration (ET) estimates are needed on a regional scale. These remote sensing methods are typically validated and calibrated using in situ measurements. Eddy covariance (EC) and lysimetry are two of the most prevalent techniques for measuring ET. Some discrepancies arise between these two techniques consequence of the measurement footprint or the spatial variability in atmospheric and surface conditions. An experiment was carried out in the growing season of 2015 in a ~4 ha row-crop vineyard in a semi-arid advective location in Central Spain, encouraged by the necessity to assess the feasibility of EC measurements in this area and under these conditions. A 9-m2 monolithic weighting lysimeter was available. An EC system was deployed together with a net radiometer and a set of soil heat flux plates. Data of the different terms of the energy balance equation were stored every 15 min, and then averaged at an hourly and daily scales. In this work we focus on the comparison between ET measurements from the two methods, EC and lysimetry. The imbalance in the surface energy budget was first analyzed. A lack of closure around 20% was observed. After forcing the closure, discrepancies between EC and lysimeter measurements still remained. Average estimation errors of ±0.09 mm h-1 and ±0.5 mm d-1 were obtained at hourly and daily scales, respectively, whereas a deviation of only 2% was observed in the accumulated ET for the entire experiment. These results support the use of adjusted EC technique to monitor accurate ET in vineyards.