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- 摘要
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- 实验方案
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The Use of Multisource Optical Sensors to Study Phytoplankton Spatio-Temporal Variation in a Shallow Turbid Lake
摘要： Lake water quality monitoring has the potential to be improved through integrating detailed spatial information from new generation remote sensing satellites with high frequency observations from in situ optical sensors (WISPstation). We applied this approach for Lake Trasimeno with the aim of increasing knowledge of phytoplankton dynamics at di?erent temporal and spatial scales. High frequency chlorophyll-a data from the WISPstation was modeled using non-parametric multiplicative regression. The ‘day of year’ was the most important factor, re?ecting the seasonal progression of a phytoplankton bloom from July to September. In addition, weather factors such as the east–west wind component were also signi?cant in predicting phytoplankton seasonal and diurnal patterns. Sentinel 3-OLCI and Sentinel 2-MSI satellites delivered 42 images in 2018 that successfully mapped the spatial and seasonal change in chlorophyll-a. The potential in?uence of localized in?ows in contributing to increased chlorophyll-a in mid-summer was visualized. The satellite data also allowed an estimation of quality status at a much ?ner scale than traditional manual methods. Good correspondence was found with manually collected ?eld data but more signi?cantly, the greatly increased spatial and temporal resolution provided by satellite and WISPstation sensors clearly o?ers an unprecedented resource in the research and management of aquatic resources.
关键词： remote sensing,WISPstation,water monitoring,chlorophyll-a,Sentinel-2 MSI,Sentinel-3 OLCI
更新于2025-09-23 15:19:57
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Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States
摘要： Remote sensing landscape monitoring approaches frequently benefit from a dense time series of observations. To enhance these time series, data from multiple satellite systems need to be integrated. Landsat image data is a valuable 30-meter resolution source of spatial information to assess forest conditions over time. Together both operational Landsat satellites—7 and 8—provide a revisit frequency of 8 days at the equator. This moderate temporal frequency provides essential information to detect annual large area abrupt land cover changes. However, the ability to measure subtle and short lived intraseasonal changes is challenged by gaps in Landsat imagery at key points in time. The first Sentinel-2 satellite mission was launched by the European Space Agency in 2015. This moderate resolution data stream provides an opportunity to supplement the Landsat data record. The objective of this study is to assess the potential for integrating top of atmosphere Landsat and Sentinel 2 image data archived in the Google Earth Engine compute environment. In this paper we assess absolute and proportional differences in near-contemporaneous observations for six bands with comparable spectral response functions and spatial resolution between the Sentinel-2 Multi Spectral Instrument and Landsat Operational Land Imager and Enhanced Thematic Mapper Plus imagery. We assessed differences using absolute difference metrics and major axis linear regression between over 10,000 image pairs across the conterminous United States and present cross sensor transformation models. Major axis linear regression results indicate that Sentinel MSI data are as spectrally comparable to the two types of Landsat image data as the Landsat sensors are with each other. Root-mean-square deviation (RMSD) values ranging from 0.0121 to 0.0398 were obtained between MSI and Landsat spectral values, and RMSD values ranging from 0.0124 and 0.0372 were obtained between OLI and ETM+. Despite differences in their spatial, spectral, and temporal characteristics, integration of these datasets appears to be feasible through the application of bandwise linear regression corrections.
关键词： Sensor integration,ETM+,Sentinel-2,MSI,OLI,Time series,Change detection,Landsat
更新于2025-09-09 09:28:46
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Using new remote sensing satellites for assessing water quality in a reservoir
摘要： Water quality monitoring could benefit from information derived from the newest generation of medium resolution Earth observation satellites. The main objective of our study was to assess the suitability of both Landsat 8 and Sentinel-2A satellites for estimating and mapping Secchi disk transparency (SDT), a common measurement of water clarity, in Cassaffousth Reservoir (Córdoba, Argentina). Ground observations and a dataset of four Landsat 8 and four Sentinel-2A images were used to create and validate models to estimate SDT in the reservoir. The selected algorithms were used to obtain graphic representations of water clarity. Slight differences were found between Landsat 8 and Sentinel-2 estimations. Thus, we demonstrated the suitability of both satellites for estimating and mapping water quality. This study highlights the importance of free and readily-available satellite datasets in monitoring water quality especially in countries where conventional monitoring programs are either lacking or unsatisfactory.
关键词： water clarity,Secchi disk depth,monitoring,Sentinel-2 MSI,remote sensing,Landsat 8 OLI
更新于2025-09-09 09:28:46