Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTN₅₀) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTN₅₀ approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications

An ensemble neural network atmospheric correction for Sentinel-3 OLCI over coastal waters providing inherent model uncertainty estimation and sensor noise propagation

Accurate atmospheric correction (AC) is a prerequisite for quantitative ocean colour remote sensing and remains a challenge in particular over coastal waters. Commonly AC algorithms are validated by establishing a mean retrieval error from match-up analysis, which compares the satellite-derived surface reflectance with concurrent ground radiometric observations. Pixel-based reflectance uncertainties however, are rarely provided by AC algorithms and those for the operational Ocean and Land Colour Instrument (OLCI) marine reflectance product are not yet recommended for use. AC retrieval errors and uncertainties directly determine the quality with which ocean colour products can be estimated from the marine surface reflectance. Increasingly there is also the need for reflectance uncertainty products to be used as data assimilation inputs into biogeochemical models. This paper describes the development of a new coastal AC algorithm for Sentinel-3 OLCI that provides pixel-based estimation of the inherent model inversion uncertainty and sensor noise propagation. The algorithm is a full-spectral model-based inversion of radiative transfer (RT) simulations in a coupled atmosphere–ocean system using an ensemble of artificial neural networks (ANN) that were initialized differently during the training process, but composed of the same network architecture. The algorithm has been validated against in-situ radiometric observations across a wide range of optical water types, and has been compared with the latest EUMETSAT operational Level 2 processor IPF-OL-2 v7.01. In this analysis we found that the ensemble ANN showed improved performance over the operational Level 2 processor with a band-averaged (412–708 nm) mean absolute percentage error (MAPE) of 16% compared to 37% and a four-times lower band-averaged bias of -0.00045 sr-1. In the ensemble inversion process we account for three uncertainty components: (1) the total model variance that describes the variance of the data from the different ANNs, (2) the prediction variance of the mean, which is based on calculations of the RT simulations and (3) the instrument noise variance of the mean by propagating the OLCI spectral signal-to-noise ratios (SNR). To study algorithm performance and to quantify the contribution of the different uncertainty components to the total uncertainty, we applied the algorithm to an optically complex full resolution (FR) test scene covering coastal waters of the Great Barrier Reef, Australia. The uncertainties associated with the instrument noise variance were found to be two orders of magnitude lower than the uncertainty components of the prediction and total model variances. The overall largest uncertainty component in our uncertainty framework is attributed to the total model inversion error from averaging the responses of the slightly different adapted networks in the ensemble. The algorithm is made publicly available as a Python/C plugin for the Sentinel Application Platform (SNAP)

Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data using metrological principles

The Sentinel 3 mission is part of the Copernicus programme space segment and has the objective of making global operational observations of ocean and land parameters with its four onboard sensors. Two Sentinel 3 satellites are currently on orbit, providing near-daily global coverage. Sentinel 3A was launched on 16 February 2016 and Sentinel 3B on 25 April 2018. For the early part of its operation, Sentinel 3B flew in tandem with Sentinel 3A, flying 30 seconds ahead of its twin mission. This provided a unique opportunity to compare the instruments on the two satellites, and to test the per pixel uncertainty values in a metrologically-robust manner. In this work we consider the tandem-phase data from the infrared channels of one of the onboard instruments: the Sea and Land Surface Temperature Radiometer, SLSTR. A direct comparison was made of both the Level 1 radiance values and the Level 2 sea surface temperature values derived from those radiances. At Level 1 the distribution of differences between the sensor values were compared to the declared uncertainties for data gridded on to a regular latitude-longitude grid with propagated pixel uncertainties. The results showed good overall radiometric agreement between the two sensors, with mean differences of ∼0.06 K, although there was a scene-temperature dependent difference for the oblique view that was consistent with what was expected from a stray light effect observed pre-flight. We propose a means to correct for this effect based on the tandem data. Level 1 uncertainties were found to be representative of the variance of the data, expect in those channels affected by the stray light effect. The sea surface temperature results show a very small difference between the sensors that could be in part due to the fact that the Sentinel-3A retrieval coefficients were also applied to the Sentinel-3B retrieval because the Sentinel-3B coefficients are not currently available. This will lead to small errors between the S3A and S3B retrievals. The comparison also suggests that the retrieval uncertainties may need updating for two of the retrieval processes, that there are extra components of uncertainty related the quality level and the probability of cloud that should be included. Finally, a study of the quality flags assigned to sea surface temperature pixel values provided valuable insight into the origin of those quality levels and highlighted possible uncertainties in the defined quality level

The Development of Dark Hyperspectral Absolute Calibration Model Using Extended Pseudo Invariant Calibration Sites at a Global Scale: Dark EPICS-Global

This research aimed to develop a novel dark hyperspectral absolute calibration (DAHAC) model using stable dark targets of Global Cluster - 36 (GC-36), one of the clusters from 300 Class Global Classification. The stable dark sites were identified from GC-36 called Dark EPICS-Global covering the surface types viz; dark rock, volcanic area, and dark sand. The Dark EPICS-Global shows a temporal variation of 0.02 unit reflectance. This work uses the Landsat-8 (L8) Operational Land Imager (OLI) , Sentinel-2A (S2A) Multispectral Instrument (MSI) , and Earth Observing One (EO-1) Hyperion data for the DAHAC model development, where well-calibrated L8 and S2A are used as the reference sensors while EO-1 Hyperion with 10 nm spectral resolution is used as a hyperspectral library. The dark hyperspectral dataset (DaHD) is generated by combining the normalized hyperspectral profile of L8 and S2A for the DAHAC model development. The DAHAC model developed in this study takes into account the solar zenith and azimuth angles as well as the view zenith and azimuth angles in Cartesian coordinates form. This model is capable of predicting TOA reflectance in all existing spectral bands of any sensor. The DAHAC model was then validated with Landsat-7 (L7) , Landsat-9 (L9) , and Sentinel-2B (S2B) satellites from their launch dates to March 2022. These satellite sensors vary in terms of their spectral resolution, equatorial crossing time, spatial resolution, etc. The comparison between the DAHAC model and satellite measurements shows accuracy within 0.01 unit reflectance across overall spectral bands. The proposed DAHAC model uncertainty level is determined using Monte Carlo Simulation and found to be 0.04 and 0.05 unit reflectance for VNIR and SWIR channels, respectively. The DAHAC model double ratio is used as a tool to perform the inter-comparison between two satellites. The sensor inter-comparison results for L8 and L9 shows a 2% difference and 1% for S2A and S2B across all spectral bands

New sensors benchmark report on Sentinel-2A

Geometric benchmarking for Sentinel-A2 sensor over Maussane test site for CAP purposesJRC.H.6-Digital Earth and Reference Dat

Classification of North Africa for Use as an Extended Pseudo Invariant Calibration Sites (Epics) for Radiometric Calibration and Stability Monitoring of Optical Satellite Sensors

An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to help the understanding of the Earth’s complex systems and to monitor significant changes to them. The quality of data recorded by these sensors is a primary concern, as it critically depends on accurate radiometric calibration for each sensor. Pseudo Invariant Calibration Sites (PICS) have been extensively used for radiometric calibration and temporal stability monitoring of optical satellite sensors. Due to limited knowledge about the radiometric stability of North Africa, only a limited number of sites in the region are used for this purpose. This work presents an automated approach to classify North Africa for its potential use as an extended PICS (EPICS) covering vast portions of the continent. An unsupervised classification algorithm identified 19 “clusters” representing distinct land surface types; three clusters were identified with spatial uncertainties within approximately 5% in the shorter wavelength bands and 3% in the longer wavelength bands. A key advantage of the cluster approach is that large numbers of pixels are aggregated into contiguous homogeneous regions sufficiently distributed across the continent to allow multiple imaging opportunities per day, as opposed to imaging a typical PICS once during the sensor’s revisit period. In addition, this work proposes a technique to generate a representative hyperspectral profile for these clusters, as the hyperspectral profile of these identified clusters are mandatory in order to utilize them for performing cross-calibration of optical satellite sensors. The technique was used to generate the profile for the cluster containing the largest number of aggregated pixels. The resulting profile was found to have temporal uncertainties within 5% across all the spectral regions. Overall, this technique shows great potential for generation of representative hyperspectral profiles for any North African cluster, which could allow the use of the entire North Africa Saharan region as an extended PICS (EPICS) dataset for sensor cross-calibration. Furthermore, this work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor crosscalibration. Eventually, North Africa can be used a continental scale PICS

Benefits and lessons learned from the Sentinel-3 tandem phase

During its commissioning phase, the Copernicus Sentinel-3B satellite has been placed in a tandem formation with Sentinel-3A for a period of 6 months. This configuration allowed a direct comparison of measurements obtained by the two satellites. The purpose of this paper was to present the range of analyses that can be performed from this dataset, highlighting methodology aspects and the main outcomes for each instrument. We examined, in turn, the benefit of the tandem in understanding instrument operational modes differences, in assessing inter-satellite differences, and in validating measurement uncertainties. The results highlighted the very good consistency of the Sentinel-3A and B instruments, ensuring the complete inter-operability of the constellation. Tandem comparisons also pave the way for further improvements through harmonization of the sensors (OLCI), correction of internal stray-light sources (SLSTR), or high-frequency processing of SRAL SARM data. This paper provided a comprehensive overview of the main results obtained, as well as insights into some of the results. Finally, we drew the main lessons learned from the Sentinel-3 tandem phase and provided recommendations for future missions

Comparison of ratioing and RCNA methods in the detection of flooded areas using Sentinel 2 Imagery (case study: Tulun, Russia)

Climate change and natural disasters caused by hydrological, meteorological, and climatic phenomena have a significant impact on cities. Russia, a continental country with a vast territory of complex geographic–ecological environments and highly variable climatic conditions, is subject to substantial and frequent natural disasters. On 29 June 2019, an extreme precipitation event occurred in the city of Tulun in the Irkutsk oblast, Russian Federation, which caused flooding due to the increase in the water level of the Iya River that passes through the city, leaving many infrastructures destroyed and thousands of people affected. This study aims to determine the flooded areas in the city of Tulun based on two change detection methods: Radiometric Rotation Controlled by No-change Axis (RCNA) and Ratioing, using Sentinel 2 images obtained before the event (19 June 2019) and during the flood peak (29 June 2019). The results obtained by the two methodologies were compared through cross-classification, and a 98% similarity was found in the classification of the areas. The study was validated based on photointerpretation of Google Earth images. The methodology presented proved to be useful for the automatic precession of flooded areas in a straightforward, but rigorous, manner. This allows stakeholders to efficiently manage areas that are buffeted by flooding episodes.LA/P/0069/2020info:eu-repo/semantics/publishedVersio

Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions