Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery

The wide field of view (WFV) imaging system onboard the Chinese GaoFen-1 (GF-1) optical satellite has a 16-m resolution and four-day revisit cycle for large-scale Earth observation. The advantages of the high temporal-spatial resolution and the wide field of view make the GF-1 WFV imagery very popular. However, cloud cover is an inevitable problem in GF-1 WFV imagery, which influences its precise application. Accurate cloud and cloud shadow detection in GF-1 WFV imagery is quite difficult due to the fact that there are only three visible bands and one near-infrared band. In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery. The MFC algorithm first implements threshold segmentation based on the spectral features and mask refinement based on guided filtering to generate a preliminary cloud mask. The geometric features are then used in combination with the texture features to improve the cloud detection results and produce the final cloud mask. Finally, the cloud shadow mask can be acquired by means of the cloud and shadow matching and follow-up correction process. The method was validated using 108 globally distributed scenes. The results indicate that MFC performs well under most conditions, and the average overall accuracy of MFC cloud detection is as high as 96.8%. In the contrastive analysis with the official provided cloud fractions, MFC shows a significant improvement in cloud fraction estimation, and achieves a high accuracy for the cloud and cloud shadow detection in the GF-1 WFV imagery with fewer spectral bands. The proposed method could be used as a preprocessing step in the future to monitor land-cover change, and it could also be easily extended to other optical satellite imagery which has a similar spectral setting.Comment: This manuscript has been accepted for publication in Remote Sensing of Environment, vol. 191, pp.342-358, 2017. (http://www.sciencedirect.com/science/article/pii/S003442571730038X

Estimating daily evapotranspiration based on a model of evaporative fraction (EF) for mixed pixels

Currently, applications of remote sensing evapotranspiration (ET) products are limited by the coarse resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal-scale extrapolation of the instantaneous latent heat flux (LE) based on satellite overpass time. This study proposes a simple but efficient model (EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evaporative fraction (EF) and area fraction (AF) to increase the accuracy of ET estimate over heterogeneous land surfaces. To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis 1 states that the available energy (AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of error and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis 2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial-scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300&thinsp;m resolution. The results generated before and after making corrections were compared and validated using site data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination (R2) increased to 0.82 from 0.62, the root mean square error (RMSE) decreased to 1.60 from 2.47&thinsp;MJ&thinsp;m−2(decreased approximately to 0.64 from 0.99&thinsp;mm) and the mean bias error (MBE) decreased from 1.92 to 1.18&thinsp;MJ&thinsp;m−2 (decreased from approximately 0.77 to 0.47&thinsp;mm). It is concluded that EFAF can reproduce daily ET with reasonable accuracy; can be used to produce the ET product; and can be applied to hydrology research, precision agricultural management and monitoring natural ecosystems in the future.</p

Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status

Comparison of machine learning algorithms for retrieval of water quality indicators in case-II waters: a case study of Hong Kong

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelf-sea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable

Design of a generic end-to-end mission performance simulator and application to the performance analysis of the FLEX/Sentinel-3 mission

La Observación de la Tierra mediante técnicas de teledetección con instrumentos ópticos en satélite tiene como objetivo monitorizar los procesos bio-geofísicos en la superficie y atmósfera terrestre, adquiriendo datos a diferentes longitudes de onda del espectro electromagnético. Con el fin de asegurar el mantenimiento de las observaciones y las capacidades para entender el sistema Tierra, nuevas misiones satelitales están siendo desarrolladas por agencias espaciales nacionales e internacionales así como organizaciones de investigación. En este contexto, los simuladores de misiones espaciales (E2ES por sus siglas en inglés, End-to-End Mission Performance Simulator) ofrecen a los científicos e ingenieros un marco único para entender el impacto de la configuración del instrumento en los productos finales de la misión y, por tanto, acelerar el desarrollo de una misión desde la fase conceptual hasta el lanzamiento. Al mismo tiempo, estas herramientas permiten definir una metodología para la consolidación de los requisitos y la evaluación de la actuación de estas misiones satelitales, estableciendo criterios para la selección de una misión por las diferentes agencias espaciales. Mientras que el concepto de un E2ES es simple, el diseño de nuevos E2ES y la evolución de los ya existentes tienen una falta de guias y metodología estandarizadas, lo cual se traduce en un caro y complejo proceso de re-ingeniería. Esta tesis cubre dos objetivos principales. Por un lado, se pretende armonizar el trabajo hecho en el campo de los E2ES durante las últimas décadas y proponer una serie de guias y metodologías para desarrollas E2ES para misiones satelitales futuras con instrumentos ópticos pasivos. El primer objetivo es por tanto "Diseñar un simulador de misión genérico que pueda ser fácilmente adaptado para reproducir la mayoría de misiones satelitales, presentes y futuras, con sensores ópticos pasivos". Por otro lado, la misión FLEX/Sentinel-3 de la ESA se usa para validar, a través de la implementación de su propio E2ES, el diseño de la arquitectura genérica tratada en el punto anterior. De este modo, el E2ES para la misión FLEX permite evaluar la actuación de la misión para la obtención de la fluorescencia inducida por radiación solar emitida por la vegetación terrestre. La misión FLEX/Sentinel-3 es una candidata óptima para esta tarea de validación dada la complejidad de la misión (p.ej. vuelo en tandem, multi-plataforma/-instrumento, múltiples rangos y resoluciones espectrales, observaciones multi-angulares, sinergia de productos). El segundo objetivo de esta tesis es por tanto "Evaluar la misión FLEX para la la observación de la emisión de fluorescencia emitida por la vegetación usando un E2ES desarrollado de acuerdo con una arquitectura genérica". La razón fundamental tras esta Tesis es promocionar el uso de una arquitectura genérica común para los E2ES que permita comparar misiones satelitales en procesos de selección competitiva como los Earth Explorer de la ESA así como acelerar el análisis de los requisitos técnicos y el rendimiento de la misión a nivel científico. Particularmente, esto se muestra mediante la implementación de esta arquitectura genérica para el caso específico de la misión FLEX/Sentinel-3 demostrando que: (1) la misión es capaz de obtener con la precisión requerida la emisión de fluorescencia por la vegetación ; y (2) el concepto de esta arquitectura genérica es apto para reproducir la complejidad de la misión FLEX/Sentinel-3 y por tanto se espera que esta metodología pueda ser también aplicable para un gran abanico de misiones ópticas pasivas. Esta base lógica se consigue a partir de una categorización de varias misiones satelitales y la identificación y análisis de los elementos principales que afectan en el rendimiento de la misión e impactan en la arquitectura de un simulador de misión. La arquitectura genérica para E2ES propuesta se valida mediante la implementación del E2ES de la misión FLEX/Sentinel-3 de la ESA teniendo en cuenta ambos satélites, sus instrumentos, y evaluando con este E2ES el rendimiento de la misión FLEX. En esta Tesis, los capítulos 1 y 2 introducen los principales temas de esta Tesis y definen los conceptos básicos. Los capítulos 3 al 5 describe el diseño de la arquitectura genérica para los E2ES en misiones ópticas pasivas. Finalmente, el capítulo 6 resume los principales resultados y las conclusiones derivadas de esta Tesis.Earth observation by satellite optical remote sensing aims to monitor bio-geophysical processes happening in the Earth surface and the atmosphere by acquiring data at different wavelengths of the electromagnetic spectrum. In order to ensure sustained observations and capabilities to fill scientific gaps in our current understanding of the Earth system, new satellite missions are being developed by national and international space agencies and research organisations. In this context, End-to-End Mission Performance Simulator (E2ES) tools offer scientists and engineers a unique framework to understand the impact of instrument configuration in the final mission products and to accelerate the mission development from concept to deployment. At the same time, these cost-effective and flexible tools are capable of defining a methodology for the consolidation of requirements and performance assessment of these new satellite missions, setting the criteria for mission selection by the various space agencies’ programme boards. While the concept of an E2ES is simple, the design of new E2ES and the evolution of existing ones lack from a standard methodology and guidelines, which translates into a complex and costly re-engineering process. This Thesis covers two main objectives. On the one hand, it aims to harmonize the work done in the field of E2ES during the last decades and to propose a set of guidelines or methodology to develop E2ES for future remote sensing satellite passive optical missions. The first main objective, therefore, is: ’To design a generic end-to-end mission performance simulator that can be easily adapted to reproduce most present or future passive optical spaceborne instruments’. On the other hand, the ESA’s FLEX/Sentinel-3 tandem mission is used to validate, through the implementation of its E2ES, the designed generic E2ES architecture and to evaluate the performance of the FLEX mission for the retrieval of Sun-induced fluorescence. The FLEX/Sentinel- 3 mission is optimally suitable for this validation task due to the complexity of the mission (e.g. tandem flight, multi-platform/-instrument mission, multiple spectral ranges and resolutions, multi-angular observations, synergy of products). The second main objective, therefore, is: ’To evaluate the FLEX mission for Sun-induced fluorescence retrievals using a newly developed E2ES in agreement with the designed generic E2ES architecture.’. The rationale behind this Thesis is promoting the use of a common generic E2ES architecture that allows comparing missions in competitive selection process (e.g., ESA’s Earth Explorers) and speeding-up the analysis of the mission technical requirements and scientific performances. Particularly, this is shown by implementing this generic E2ES architecture for the specific case of FLEX/Sentinel-3 mission demonstrating that: (1) the mission is capable of retrieving Sun-induced fluorescence within the required accuracy; and (2) the conceptual generic E2ES architecture is suitable toreproduce the complexity of the FLEX/Sentinel-3 tandem mission and thus it is expected to be also applicable for a wide range of passive optical missions. This rationale is achieved by categorising several satellite missions to identify and analyse the main elements that affect the mission performance and impact the simulator architecture. The proposed generic E2ES architecture is validated by implementing the ESA’s FLEX/Sentinel-3 E2ES, both satellites and their instruments, and testing it through the performance assessment of the FLEX mission products. In this Thesis, Chapters 1 and 2 introduce the main research questions and sets the background concepts. Then Chapters 3–5 describe the design of a generic E2ES architecture for passive optical missions. Finally, Chapter 6 summarizes the main results and conclusions derived in this Thesis

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET)

Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies