Antonio Ruiz-Verdú

HICO L1 and L2 data processing: Radiometric recalibration, atmospheric correction and retrieval of water quality parameters

The Hyperspectral Imager for the Coastal Ocean (HICO) is an imaging spectrometer designed with a very high signal-to-noise ratio to monitor coastal ocean and inland waters. The processing of Top-Of-Atmosphere radiance data down to surface reflectance is fundamental for the retrieval of water quality products. However, the current HICO processing chain does not provide atmospheric corrected data nor higher-level water quality products. This paper describes the algorithms implemented within an HICO data processing chain that includes image pre-processing, atmospheric correction and the retrieval of water quality parameters. The implemented algorithms have been validated over a set of HICO images showing a good match with in-situ surface reflectance data and correlation (R2 = 0.95) between in-situ measured and retrieved Chl-a over a water body. It is expected that the presented algorithms will ease the processing of HICO data down to surface reflectance allowing to derive water quality parameters.

Comparison of MODIS and Landsat-8 retrievals of Chlorophyll-a and water temperature over Lake Titicaca

Chlorophyll-a concentration ([Chl-a]) and Lake Surface Temperature (LST) were retrieved in Lake Titicaca (Peru-Bolivia) using MODIS and Landsat-8 images. The lake was chosen as a case-study for evaluating the feasibility of Landsat-8 images for [Chl-a] and LST monitoring in oligotrophic and mesotrophic water bodies. The big size of the lake and its spatial and temporal variability, allowed the comparison of MODIS and Landsat-8 products for a wide range of [Chl-a] and LST. The atmospheric correction of the images was facilitated by the very high altitude of the lake. MODIS images were processed with standard ocean color algorithms whereas for Landsat-8, specific algorithms were tested and validated The results show that Landsat-8 is capable of retrieving [Chl-a] and LST with an accuracy comparable to that of MODIS and with a finer spatial resolution, revealing surface patterns in greater detail. The combined use of both sensors allows monitoring the eutrophication and temperature trends of Lake Titicaca, which is a water body of the highest ecological interest, increasingly affected by human activities in its watershed and very sensitive to climate changes.

HICO level-2 data processing toolbox for the atmospheric correction and the retrieval of water quality parameters

The Hyperspectral Imager for the Coastal Ocean (HICO) is an imaging spectrometer specifically designed to monitor the coastal ocean. The processing of Top-Of-Atmosphere (TOA) radiance data down to surface reflectance is fundamental for the retrieval of water quality products. However, the current HICO processing chain does not provide atmospheric corrected data nor higher-level water quality products. This work describes a toolbox for the atmospheric correction of HICO data and the retrieval of water quality products. The HICO toolbox, consisting on three main modules (image pre-processing, atmospheric correction and retrieval of water quality products), has been used over a set of HICO images showing a good linear correlation (R2 = 0.95) between in-situ measured and retrieved Chl-a over a water body. The presented toolbox will ease the processing of HICO data down to surface reflectance that will allow to derive water quality parameters.

Operational cloud screening service for Sentinel-2 image time series

This paper deals with the development and implementation of a cloud screening algorithm for image time series, with the focus on the forthcoming Sentinel-2 satellites to be launched under the ESA Copernicus Programme. The proposed methodology is based on kernel ridge regression and exploits the temporal information to detect anomalous changes that correspond to cloud covers. The huge data volumes to be processed when dealing with high temporal, spatial, and spectral resolution datasets motivate the implementation of the algorithm within distributed computer resources. In consequence, an operational cloud screening service has been specifically designed and implemented in the frame of the Sentinels Synergy Framework (SenSyF). The effectiveness of the proposed method is successfully illustrated using a time series dataset with a 5-day revisit derived from SPOT-4 at high resolution, which has been collected by ESA in preparation for the exploitation of the Sentinel-2 mission.