Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable

Armonización de datos de satélite mediante zonas homogéneas

La calibración radiométrica de los sensores es un factor clave en la interoperabilidad de los datos, permitiendo asegurar su calidad científica y la comparación de los mismos entre sensores. Actualmente, la gran mayoría de satélites no poseen la instrumentación necesaria para realizar calibración on board por su elevado coste económico y por la simplicidad en el diseño del satélite lo que se une a la problemática de realizar medidas In-Situ en determinadas localizaciones de la Tierra. Por ello, la utilización de técnicas de calibración de manera indirecta tomando como referencia otros sensores permite una calibración radiométrica absoluta, de forma rápida, precisa y con un coste económico bajo. En esta tesis doctoral, se presenta una metodología de armonización de datos de satélite a partir de zonas homogéneas, aplicable a las dos metodologías fundamentales para la calibración radiométrica de manera indirecta, como son Simultaneous Nadir Overpass (SNOs) y Pseudo-Invariant Calibration Sites (PICS).Departamento de Física AplicadaDoctorado en Físic

Evaluation of Landsat 8 imagery capability to estimate chlorophyll-a concentrations using spatially and temporally different data

Water quality has been one of the major issues in water resources. A water quality monitoring program should be performed regularly. However, this program requires numerous resources and efforts, especially using a direct measurement method. An alternative should be carried out to minimise the issues. Landsat 8 (L8) can be the alternative. Water clarity is one of the essential water parameters affecting sunlight’s ability to penetrate water and engage photosynthesis. Algae is vital in photosynthesis and usually indicated as chlorophyll-a (chl-a). Several studies present that L8 is adequate to estimate chl-a concentrations as it provides high-accuracy results. This paper will generate a new model using data from different places and compare it with other chl-a models from previous studies by their capabilities to estimate chl-a concentrations. The results indicate that the generated model cannot provide consistent and precise estimations in different places and times, although it has a “good” R2 value at 0.7245 from the regression analysis for model generation. The same results arise from other models that cannot reasonably estimate chl-a concentrations

An Alternative Model to Estimate Total Suspended Solids Concentrations using Landsat 8 Imagery in Indonesia

A regular monitoring program of water quality is generally performed using a direct measurement method, which requires substantial efforts and resources. These issues can be minimised using several options, one of which is Landsat 8 (L8). This imagery has been broadly used to measure several water quality parameters, especially Total Suspended Solids (TSS) concentrations, even in Indonesia. This paper will compare several models from previous studies and a modified model generated using data from various sites. The comparison is based on their competencies to estimate TSS concentrations. The competencies are determined by the coefficient of correlation (r), correlation of determination (R2), and residual standard error (RSE) parameters as these three parameters are strongly correlated, generally applied, and provide distinctive determinations. The best model should have the highest r and R2 values, while the RSE value should be the lowest. The results imply that TSS model 4 generated in this study provides comparable results with TSS model 1, which has been generally used in Indonesia and provided favourable results. Thus, it can be an alternative model to estimate TSS concentrations in Indonesia

Use of Hyperspectral Remote Sensing to Estimate Water Quality

Approximating and forecasting water variables like phosphorus, nitrogen, chlorophyll, dissolved organic matter, and turbidity are of supreme importance due to their strong influence on water resource quality. This chapter is aimed at showing the practicability of merging water quality observations from remote sensing with water quality modeling for efficient and effective monitoring of water quality. We examine the spatial dynamics of water quality with hyperspectral remote sensing and present approaches that can be used to estimate water quality using hyperspectral images. The methods presented here have been embraced because the blue-green and green algae peak wavelengths reflectance are close together and make their distinction more challenging. It has also been established that hyperspectral imagers permit an improved recognition of chlorophyll and hereafter algae, due to acquired narrow spectral bands between 450 nm and 600 nm. We start by describing the practical application of hyperspectral remote sensing data in water quality modeling. The surface inherent optical properties of absorption and backscattering of chlorophyll a, colored dissolved organic matter (CDOM), and turbidity are estimated, and a detailed approach on analyzing ARCHER data for water quality estimation is presented

New Approach for Temporal Stability Evaluation of Pseudo-Invariant Calibration Sites (PICS)

Pseudo-Invariant Calibration Sites (PICS) are one of the most popular methods for in-flight vicarious radiometric calibration of Earth remote sensing satellites. The fundamental question of PICS temporal stability has not been adequately addressed. However, the main purpose of this work is to evaluate the temporal stability of a few PICS using a new approach. The analysis was performed over six PICS (Libya 1, Libya 4, Niger 1, Niger 2, Egypt 1 and Sudan 1). The concept of a Virtual Constellation was developed to provide greater temporal coverage and also to overcome the dependence limitation of any specific characteristic derived from one particular sensor. TOA reflectance data from four sensors consistently demonstrating stable calibration to within 5%the Landsat 7 ETM+ (Enhanced Thematic Mapper Plus), Landsat 8 OLI (Operational Land Imager), Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and Sentinel-2A MSI (Multispectral Instrument)were merged into a seamless dataset. Instead of using the traditional method of trend analysis (Students T test), a nonparametric Seasonal Mann-Kendall test was used for determining the PICS stability. The analysis results indicate that Libya 4 and Egypt 1 do not exhibit any monotonic trend in six reflective solar bands common to all of the studied sensors, indicating temporal stability. A decreasing monotonic trend was statistically detected in all bands, except SWIR 2, for Sudan 1 and the Green and Red bands for Niger 1. An increasing trend was detected in the Blue band for Niger 2 and the NIR band for Libya 1. These results do not suggest abandoning PICS as a viable calibration source. Rather, they indicate that PICS temporal stability cannot be assumed and should be regularly monitored as part of the sensor calibration process

Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm

This is the final version. Available from Elsevier via the DOI in this record. Mangroves play an important role in protecting coasts against wave energy and storms. Mangrove ecosystems provide important habitats for fauna and flora and are an important carbon sink. Loss of mangroves forest may lead to enhanced coastal erosion. Mangroves are complex ecosystems and processes of settling and development are not fully understood. Characterizing the rates and patterns of mangrove gains and losses is needed to better understand the functioning of mangrove ecosystems, how mangrove dynamics are linked to coastal morphological behaviour and how human interference with the coastal system impacts mangroves. Here we present a study of the mangrove ecosystems at the Suriname coast, which are relatively pristine and characterized by strong dynamics due to migrating mudbanks along the coast. Satellite images between 2000 and 2018, available in the historic satellite image archives, were analysed using the LandTrendr (Landsat-based detection of trends in disturbance and recovery) algorithm to identify locations of mangrove erosion, mangrove colonization, surface areas of change and patterns of settlement, as indicated by (sudden) changes in NDVI. The algorithm requires careful setting of various parameters for successful detection of (abrupt) temporal changes in mangrove coverage. The algorithm was evaluated on its robustness using various parameter settings. Results show the value of the timeseries of Landsat imagery to detect locations of coastal erosion of up to 50 m/yr and accretion where loss or settlement of mangroves is prevailing between 2000 and 2018. Locally differences are very large. An overall westward mangrove progression along the coast is apparent from the images and probably linked to mud bank migration. Various patterns of mangrove colonization and development such as arc-, zonal- and patch- arrangements were identified, although at some locations the Landsat resolution of 30 m is somewhat coarse to allow detailed analysis. The success and robustness of the LandTrendr algorithm are controlled by NDVI threshold values, number of allowed breakpoints in the timeseries and fitting parameters. The presented method requires further testing and evaluation but is a promising tool for semi-automatic detection of coastal mangrove erosion and colonization that can be applied to other mangrove ecosystems in the world. The satellite timeseries analyses generate valuable information on coastal dynamics, which is helpful to identify coastal areas prone to erosion and mangrove retreat and provide as such a valuable tool for coastal management and protection.NWO WOTRO Joint Sustainability Development Goal Research ProgramUtrecht University Bright Minds projec

Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable

Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3

Trabajo realizado por otros veinte autores.Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (acdom(440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and acdom(440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and acdom(440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI