Cross-Calibration of S-NPP VIIRS Moderate Resolution Reflective Solar Bands Against MODIS Aqua over Dark Water Scenes

The Visible Infrared Imaging Radiometer Suite (VIIRS) is being used to continue the record of Earth Science observations and data products produced routinely from National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. However, the absolute calibration of VIIRS's reflected solar bands is thought to be biased, leading to offsets in derived data products such as aerosol optical depth (AOD) as compared to when similar algorithms are applied to different sensors. This study presents a cross-calibration of these VIIRS bands against MODIS Aqua over dark water scenes, finding corrections to the NASA VIIRS Level 1 (version 2) reflectances between approximately +1 and 7 % (dependent on band) are needed to bring the two into alignment (after accounting for expected differences resulting from different band spectral response functions), and indications of relative trending of up to 0.35 % per year in some bands. The derived calibration gain corrections are also applied to the VIIRS reflectance and then used in an AOD retrieval, and they are shown to decrease the bias and total error in AOD across the mid-visible spectral region compared to the standard VIIRS NASA reflectance calibration. The resulting AOD bias characteristics are similar to those of NASA MODIS AOD data products, which is encouraging in terms of multi-sensor data continuity

Using New and Long-Term Multi-Scale Remotely Sensed Data to Detect Recurrent Fires and Quantify Their Relationship to Land Cover/Use in Indonesian Peatlands

Indonesia has committed to reducing its greenhouse gases emissions by 29% (potentially up to 41% with international assistance) by 2030. Achieving those targets requires many efforts but, in particular, controlling the fire problem in Indonesia’s peatlands is paramount, since it is unlikely to diminish on its own in the coming decades. This study was conducted in Sumatra and Kalimantan peatlands in Indonesia. Four MODIS-derived products (MCD45A1 collection 5.1, MCD64A1 (collection 5.1 and 6), FireCCI51) were initially assessed to explore long-term fire frequency and land use/cover change relationships. The results indicated the product(s) could only detect half of the fires accurately. A further study was conducted using additional moderate spatial resolution data to compare two years of different severity (2014 and 2015) (Landsat, Sentinel 2, Sentinel 1, VIIRS 375 m). The results showed that MODIS BA products poorly discriminated small fires and failed to detect many burned areas due to persistent interference from clouds and smoke that often worsens as fire seasons progress. Although there are unique fire detection capabilities associated with each sensor (MODIS, VIIRS, Landsat, Sentinel 2, Sentinel 1), no single sensor was ideal for accurate detection of peatland fires under all conditions. Multisensor approaches could advance biomass-burning detection in peatlands, improving the accuracy and comprehensive coverage of burned area maps, thereby enabling better estimation of associated fire emissions. Despite missing many burned areas, MODIS BA (MCD64A1 C6) provides the best available data for evaluating longer term (2001-2018) associations between the frequency of fire occurrence and land use/cover change across large areas. Results showed that Sumatra and Kalimantan have both experienced frequent fires since 2001. Although extensive burning was present across the entire landscape, burning in peatlands was ~5- times more frequent and strongly associated with changes of forest to other land use/cover classes. If fire frequencies since 2001 remain unchanged, remnant peat swamp forests of Sumatra and Kalimantan will likely disappear over the next few decades. The findings reported in this dissertation provide critical insights for Indonesian stakeholders that can help them to minimize impacts of environmental change, manage ecological restoration efforts, and improve fire monitoring systems within Indonesia

A Feedforward Neural Network Approach for the Detection of Optically Thin Cirrus From IASI-NG

The identification of optically thin cirrus is crucial for their accurate parameterization in climate and Earth's system models. This study exploits the characteristics of the infrared atmospheric sounding interferometer-new generation (IASI-NG) to develop an algorithm for the detection of optically thin cirrus. IASI-NG has been designed for the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar system second-generation program to continue the service of its predecessor IASI from 2024 onward. A thin-cirrus detection algorithm (TCDA) is presented here, as developed for IASI-NG, but also in parallel for IASI to evaluate its performance on currently available real observations. TCDA uses a feedforward neural network (NN) approach to detect thin cirrus eventually misidentified as clear sky by a previously applied cloud detection algorithm. TCDA also estimates the uncertainty of "clear-sky" or "thin-cirrus" detection. NN is trained and tested on a dataset of IASI-NG (or IASI) simulations obtained by processing ECMWF 5-generation reanalysis (ERA5) data with the s-IASI radiative transfer model. TCDA validation against an independent simulated dataset provides a quantitative statistical assessment of the improvements brought by IASI-NG with respect to IASI. In fact, IASI-NG TCDA outperforms IASI TCDA by 3% in probability of detection (POD), 1% in bias, and 2% in accuracy, and the false alarm ratio (FAR) passes from 0.02 to 0.01. Moreover, IASI TCDA validation against state-of-the-art cloud products from Cloudsat/CPR and CALIPSO/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) real observations reveals a tendency for IASI TCDA to underestimate the presence of thin cirrus (POD = 0.47) but with a low FAR (0.07), which drops to 0.0 for very thin cirrus

Development and verification of the NASA Multi-Angle Imager for Aerosols operational cloud mask

The National Aeronautics and Space Administration (NASA) Multi-Angle Imager for Aerosols (MAIA) instrument is set to launch in 2022 with the mission of quantifying the epidemiological relationships between aerosols and human health. The MAIA instrument's primary product is a level 2 aerosol particulate matter concentration measurement collected over cloud-free pixels. The quality of this product heavily depends on the validity of the cloud mask. In this project, we present a cloud masking algorithm for MAIA constrained to its hardware. It consists of 7 observables that are tested against predetermined static thresholds. Both observables and thresholds are a function of scene type, which is a unique combination of sun-view geometry, day of year and surface type, including a novel surface classification scheme derived from the Multi-Angle Implementation of Atmospheric Correction Bi-Directional Reflectance Distribution Function (MAIAC BRDF) data set. The cloud mask algorithm works by checking if an observation exceeds or falls short of a threshold for any of the 7 observables, resulting in a cloudy or clear classification. The thresholds are derived to match the performance of the Terra Moderate Resolution Imaging Spectro-Radiometer (MODIS) high-confidence-cloud cloud mask to achieve cloud conservative behavior. The algorithm allows tuning of the conservativeness by introducing the quantities of Distance-to-Threshold, Activation Value and number of tests to activate. These user-specified parameters determine how much confidence is needed for a cloudy or clear classification. The results are presented for the Los Angeles primary target area. The overall agreement between the MODIS cloud mask and the MAIA cloud mask (MCM) is 92.9%. Of the 7.1% disagreement, 60% of it was due to false positives by the MCM, considering MODIS as the truth. The MCM is more than 90% in agreement with MODIS for deep non-sun-glint water and the first 11 of the 16 snow-free land surface types. It differs from the MODIS cloud mask the most over bright desert, mountains and coastlines due to false cloudy flags. It agrees well with the MODIS cloud mask for cumulus, stratus and high cirrus, with greater disagreements over cloud edges, smoke plumes from wildfires, and very thin cirrus. The MCM agrees well with the MODIS cloud mask (>85%) for most solar zenith angles between 25 and 53 degrees, viewing zenith angles less than 60 degrees, and relative azimuth angles between 105 and 135 degrees. Several recommendations for improving the MCM are discussed, and its advantages over the MODIS cloud mask

Development of a global burned area mapping algorithm for moderate spatial resolution optical sensors

La tesis doctoral titulada “Development of a global burned area mapping algorithm for moderate spatial resolution optical sensors” propone el desarrollo de un algoritmo de detección de área quemada global para sensores ópticos de resolución espacial moderada. El trabajo ha sido financiado y desarrollado bajo los proyectos Fire Disturbance (FireCCI) del programa Climate Change Initiative (CCI) de la European Space Agency (ESA) y el Copernicus Climate Change Service (C3S) de la European Commission (EC). El autor de este trabajo también ha recibido financiación del Ministerio de Ciencia, Innovación y Universidades, a través de una beca FPU. Cuando se propuso esta tesis solo había un único producto global de área quemada que ofrecía una serie temporal larga y consistente. Se trataba del producto MCD64A1 de la National Aeronautics and Space Administration (NASA) que se generaba operacionalmente y que proveía información de área quemada a nivel global a 500 m desde noviembre del 2000. Por la parte europea solo había dos productos, el FireCCI41 y el GIO_GL1_BA, pero se trataba de productos que o bien ofrecían una serie temporal demasiado reducida (FireCCI41) o bien una serie con baja fiabilidad. En cualquier caso, los tres productos, incluido el MCD64A1, presentaban limitaciones que les hacían estar lejos de cumplir los requerimientos establecidos por los usuarios en términos de errores de comisión y omisión. Es en este contexto donde se plantea esta tesis que pretende avanzar en el conocimiento de los algoritmos de área quemada globales y la generación de productos globales que cumplan o se acerquen de forma más significativa a las expectativas de los usuarios. Para este propósito, se ha utilizado información proveniente de sensores que no se habían utilizado hasta el momento para generar productos de área quemada globales. Esta información incluye las bandas de alta resolución a 250 m del Moderate Resolution Imaging Spectroradiometer (MODIS), las bandas del Ocean and Land Colour Instrument (OLCI) y del SYNERGY, así como fuegos activos de MODIS y del Visible Infrared Imaging Radiometer Suite (VIIRS). En este último caso, ha sido la primera vez que se utilizan globalmente para generar este tipo de productos. Así, se han desarrollado cuatro algoritmos y se han generado sus respectivos productos de área quemada a escala global. Cada uno de ellos ha jugado un papel complementario al resto, ya sea a modo de versión mejorada o como adaptación de un mismo algoritmo a distintos sensores. Todos los productos derivados han sido validados globalmente y se han llevado a cabo comparaciones exhaustivas con otros productos existentes. Además, para confirmar la estabilidad de los patrones espacio temporales, los productos se han aplicado para dar respuesta a distintas preguntas científicas relacionadas con las anomalías en las tendencias del área quemada en distintas partes del mundo. Para explicar todo este proceso la tesis se ha estructurado en ocho capítulos: introducción, seis publicaciones en revistas internacionales y unas conclusiones

Pre-Aerosol, Clouds, and Ocean Ecosystem (PACE) Mission Science Definition Team Report

We live in an era in which increasing climate variability is having measurable impact on marine ecosystems within our own lifespans. At the same time, an ever-growing human population requires increased access to and use of marine resources. To understand and be better prepared to respond to these challenges, we must expand our capabilities to investigate and monitor ecological and bio geo chemical processes in the oceans. In response to this imperative, the National Aeronautics and Space Administration (NASA) conceived the Pre-Aerosol, Clouds, and ocean Ecosystem (PACE) mission to provide new information for understanding the living ocean and for improving forecasts of Earth System variability. The PACE mission will achieve these objectives by making global ocean color measurements that are essential for understanding the carbon cycle and its inter-relationship with climate change, and by expanding our understanding about ocean ecology and biogeochemistry. PACE measurements will also extend ocean climate data records collected since the 1990s to document changes in the function of aquatic ecosystems as they respond to human activities and natural processes over short and long periods of time. These measurements are pivotal for differentiating natural variability from anthropogenic climate change effects and for understanding the interactions between these processes and various human uses of the ocean. PACE ocean science goals and measurement capabilities greatly exceed those of our heritage ocean color sensors, and are needed to address the many outstanding science questions developed by the oceanographic community over the past 40 years

CIRA annual report FY 2015/2016

Reporting period April 1, 2015-March 31, 2016

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017