Inconsistencies of interannual variability and trends in long-term satellite leaf area index products

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products

QUANTIFICATION OF ERROR IN AVHRR NDVI DATA

Several influential Earth system science studies in the last three decades were based on Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) series of instruments. Although AVHRR NDVI data are known to have significant uncertainties resulting from incomplete atmospheric correction, orbital drift, sensor degradation, etc., none of these studies account for them. This is primarily because of unavailability of comprehensive and location-specific quantitative uncertainty estimates. The first part of this dissertation investigated the extent of uncertainty due to inadequate atmospheric correction in the widely used AVHRR NDVI datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AErosol RObotic NETwork (AERONET) sunphotometer sites in 1999. Of the datasets included in this study, Long Term Data Record (LTDR) was found to have least errors (precision=0.02 to 0.037 for clear and average atmospheric conditions) followed by Pathfinder AVHRR Land (PAL) (precision=0.0606 to 0.0418), and Top of Atmosphere (TOA) (precision=0.0613 to 0.0684). ` Although the use of field data is the most direct type of validation and is used extensively by the remote sensing community, it results in a single uncertainty estimate and does not account for spatial heterogeneity and the impact of spatial and temporal aggregation. These shortcomings were addressed by using Moderate Resolution Imaging Spectrometer (MODIS) data to estimate uncertainty in AVHRR NDVI data. However, before AVHRR data could be compared with MODIS data, the nonstationarity introduced by inter-annual variations in AVHRR NDVI data due to orbital drift had to be removed. This was accomplished by using a Bidirectional Reflectance Distribution Function (BRDF) correction technique originally developed for MODIS data. The results from the evaluation of AVHRR data using MODIS showed that in many regions minimal spatial aggregation will improve the precision of AVHRR NDVI data significantly. However temporal aggregation improved the precision of the data to a limited extent only. The research presented in this dissertation indicated that the NDVI change of ~0.03 to ~0.08 NDVI units in 10 to 20 years, frequently reported in recent literature, can be significant in some cases. However, unless spatially explicit uncertainty metrics are quantified for the specific spatiotemporal aggregation schemes used by these studies, the significance of observed differences between sites and temporal trends in NDVI will remain unknown

Reconstruction of a Long-term spatially Contiguous Solar-Induced Fluorescence (LCSIF) over 1982-2022

Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for diagnosing the photosynthetic characteristics of terrestrial ecosystems. Despite the increasing spatial and temporal resolutions of these satellite retrievals, records of SIF are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function and structure. In this study, we leverage the two surface reflectance bands (red and near-infrared) available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2022) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2022). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data, a neural network is then built to reproduce the Orbiting Carbon Observatory-2 SIF using AVHRR and MODIS, and used to map SIF globally over the entire 1982-2022 period. Compared with the previous MODIS-based CSIF product relying on four reflectance bands, our two-band-based product has similar skill but can be advantageously extended to the bias-corrected AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv; all based empirically on red and near-infrared bands) shows a higher or comparable correlation of LCSIF with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity of LCSIF for representing terrestrial photosynthesis. Globally, LCSIF-AVHRR shows an accelerating upward trend since 1982, with an average rate of 0.0025 mW m-2 nm-1 sr-1 per decade during 1982-2000 and 0.0038 mW m-2 nm-1 sr-1 per decade during 2001-2022. Our LCSIF data provide opportunities to better understand the long-term dynamics of ecosystem photosynthesis and their underlying driving processes

A Non-Stationary 1981-2012 AVHRR NDVI(sub 3g) Time Series

The NDVI(sub 3g) time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of plus or minus 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set

Aqua: AIRS, AMSU, HSB, AMSR-E, CERES, MODIS

This brochure provides an overview of the Aqua spacecraft, instruments, science, and data products Aqua, Latin for water, is a NASA Earth Science satellite mission named for the large amount of information that the mission is collecting about the Earth's water cycle, including evaporation from the oceans, water vapor in the atmosphere, clouds, precipitation, soil moisture, sea ice, land ice, and snow cover on the land and ice. Additional variables also measured by Aqua include radiative energy fluxes, aerosols, vegetation cover on the land, phytoplankton and dissolved organic matter in the oceans, and air, land, and water temperatures. Note: this guide was produced before Aqua was launched; for the most recent information on Aqua, go to http://aqua.nasa.gov. Educational levels: Undergraduate lower division, Undergraduate upper division, Graduate or professional, Informal education

Historical Perspectives on AVHRR NDVI and Vegetation Drought Monitoring

No abstract availabl

Land and cryosphere products from Suomi NPP VIIRS: overview and status

[1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team's evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS

Development and analysis of global long-term burned area based on avhrr-ltdr data

La tesis doctoral titulada “Development and analysis of global Long-Term Burned Area based on AVHRR-LTDR data” propone la extensión temporal de la información global de área quemada obtenida a partir de imágenes de satélite. Un nuevo y consistente producto global de área quemada fue desarrollado ofreciendo datos por casi cuarenta años (1982-2018). El producto fue generado, analizado y validado, además de aplicado en el estudio global de tendencias espaciales y temporales. El trabajo fue financiado y desarrollado bajo el proyecto Fire Disturbance (Fire_cci) perteneciente al programa Climate Change initiative (CCI) de la Agencia Espacial Europea (ESA). Un producto global en una escala temporal larga contribuye a rellenar un vacío en la información necesaria para los modelos del clima y el estudio del cambio climático. Para llevar a cabo este objetivo fue preciso utilizar la base de datos de imágenes globales de satélite más extensa disponible, los datos pertenecientes al sensor Advanced Very High Resolution Radiometer (AVHRR) y de los satélites National Oceanic and Atmospheric Administration (NOAA). Por ello, se hizo uso de un algoritmo novedoso que introdujo una visión renovada para afrontar estas limitaciones y detectar área quemada. Modelos mensuales de Random Forest fueron desarrollados. Un innovador índice sintético y la obtención de proporciones de área quemada por cada pixel, hizo de este algoritmo y producto, únicos. Además, una validación y un estudio espacio-temporal fue realizado por primera vez en una serie temporal larga de casi cuarenta años. Los resultados de la inter-comparación con otros productos globales de área quemada, ofreció correlaciones altas, mostrando relaciones mensuales con los productos MCD64A1 (r = 0.89, %MAE = 21%) y FireCCI51 (r = 0.95, %MAE = 10%) durante las series temporales comunes. También se obtuvieron altas correlaciones con los perímetros oficiales que se extendían a la época pre-MODIS, como (Australia: r = 0.89, %MAE = 26%; Canadá: r = 0.81, %MAE = 33%; Alaska: r = 0.96, %MAE = 42%). La degradación de los satélites no influyó a los patrones de área quemada en la serie temporal. La validación fue novedosa al realizar a una serie temporal de casi 30 años, con un buen comportamiento del producto, y el uso de proporciones fue capaz de reducir errores. Los datos del periodo AVHRR2 del producto tienen mayor incertidumbre que AVHRR3 debido a la calidad de los sensores, aunque ambos periodos son consistentes. El producto desarrollado en esta tesis reveló tendencias de disminución de área quemada en África oriental, regiones boreales, Asia central y el sur de Australia, y tendencias de aumento de área quemada en África occidental y central, Sudamérica, USA y el norte de Australia

Evaluation of five satellite top-of-atmosphere albedo products over land

Five satellite top-of-atmosphere (TOA) albedo products over land were evaluated in this study including global products from the Advanced Very High Resolution Radiometer (AVHRR) (TAL-AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) (TAL-MODIS), and Clouds and the Earth’s Radiant Energy System (CERES); one regional product from the Climate Monitoring Satellite Application Facility (CM SAF); and one harmonized product termed Diagnosing Earth’s Energy Pathways in the Climate system (DEEP-C). Results showed that overall, there is good consistency among these five products, particularly after the year 2000. The differences among these products in the high-latitude regions were relatively larger. The percentage differences among TAL-AVHRR, TAL-MODIS, and CERES were generally less than 20%, while the differences between TAL-AVHRR and DEEP-C before 2000 were much larger. Except for the obvious decrease in the differences after 2000, the differences did not show significant changes over time, but varied among different regions. The differences between TAL-AVHRR and the other products were relatively large in the high-latitude regions of North America, Asia, and the Maritime Continent, while the differences between DEEP-C and CM SAF in Europe and Africa were smaller. Interannual variability was consistent between products after 2000, before which the differences among the three products were much larger

Using Long Time Series of Satellite Remote Sensing Data to Assess the Impact of Climate and Anthropogenic Changes in the Mesopotamian Marshes, Iraq

In the recent past, the Mesopotamia region has been rich in all forms of biological diversity, characterized by a fertile living environment and natural habitats full of rare birds, wild animals, aquatic animals, and diverse plants. Its natural abundance and geographical location have allowed it to be break or transit point for millions of migratory birds from Russia to South Africa. It is a breeding ground for many species of Persian Gulf fish. Despite all this historical, environmental and economic richness, they have been neglected as a result of the combination of a number of human and climatic factors, which in 16 years (1988-2003) has modified them to a land where vegetation, water, and biodiversity have been clearly reduced. This is a great environmental loss, not only for West Asia but for the whole world. This dissertation explores the changes in the vegetation coverage and water bodies in the Mesopotamian marshes, Iraq over more than three decades (36 years) using different sources of satellite remote sensing datasets. Firstly, we utilized Normalized Difference Vegetation Index (NDVI) from the Land Long Term Data Record (LTDR) Version 5 which has a 0.05o x 0.05o in spatial resolution and daily temporal repeat to monitor the fluctuations of vegetation together with hydrological variables such precipitation, surface temperature, and evapotranspiration. In this research, we studied the impact of climate change and anthropogenic activities on vegetation and water coverage changes. Secondly, we compared Normalized Difference Vegetation Index from various satellite sensors - Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High-Resolution Radiometer (AVHRR), and Landsat over the Mesopotamian marshlands for 17 years. We selected this time series (2002-2018) to monitor the changes in vegetation area. The time series (2002-2018) is considered as a period of rehabilitation for the Mesopotamian marshes. Thirdly, as a result of human factors and local and regional climate changes, the marshes and Iraq are in general vulnerable to face a large number of dust storms annually. According to local sources (Iraq news) and National Aeronautics and Space Administration, the time period from June 29 to July 8, 2009, is considered the longest dust storm period in Iraq during last decade. In this research, we utilized the Moderate Resolution Imagining Spectroradiometer, surface reflectance daily data to calculate the Normalized Difference Dust Index. Additionally, brightness temperature data from Aqua thermal band 31 were used to separate sand on the ground from atmospheric dust. The main reasons for the degradation of the Mesopotamian marshes were due to anthropogenic activities. In the comparison research, we found that the NDVI derived from MODIS, AVHRR and Landsat sensors are correlated with high precision. This paper investigates the utility of combining low spatial resolution with frequent temporal repeat and long-term coverage and a high spatial resolution with infrequent temporal repeat and similar long-term coverage. This study also proves that we can use the low-resolution Advance Very High- resolution Radiometer data for studies on land cover change