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The NDVI3g 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 ± 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.

We review the literature and find 16 studies from across Africa's savannas and woodlands where woody encroachment dominates. These small-scale studies are supplemented by an analysis of long-term continent-wide satellite data, specifically the Normalized Difference Vegetation Index (NDVI) time series from the Global Inventory Modeling and Mapping Studies (GIMMS) dataset. Using dry-season data to separate the tree and grass signals, we find 4.0% of non-rainforest woody vegetation in sub-Saharan Africa (excluding West Africa) significantly increased in NDVI from 1982 to 2006, whereas 3.52% decreased. The increases in NDVI were found predominantly to the north of the Congo Basin, with decreases concentrated in the Miombo woodland belt. We hypothesize that areas of increasing dry-season NDVI are undergoing woody encroachment, but the coarse resolution of the study and uncertain relationship between NDVI and woody cover mean that the results should be interpreted with caution; certainly, these results do not contradict studies finding widespread deforestation throughout the continent. However, woody encroachment could be widespread, and warrants further investigation as it has important consequences for the global carbon cycle and land–climate interactions.

Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

Lake surface water temperature (LSWT) is an important parameter with which to assess aquatic ecosystems and to study the lake’s response to climate change. The AVHRR archive of the University of Bern offers great potential to derive consistent LSWT data suited for the study of climate change and lake dynamics. To derive such a dataset, challenges such as orbit drift correction, non-water pixel detection, and homogenization had to be solved. The result is a dataset covering over 3.5 decades of spatial LSWT data for 26 European lakes. The validation against in-situ temperature data at 19 locations showed an uncertainty between ±0.8 K and ±2.0 K (standard deviation), depending on locations of the lakes. The long-term robustness of the dataset was confirmed by comparing in-situ and satellite derived temperature trends, which showed no significant difference. The final trend analysis showed significant LSWT warming trends at all locations (0.2 K/decade to 0.8 K/decade). A gradient of increasing trends from south-west to north-east of Europe was revealed. The strong intra-annual variability of trends indicates that single seasonal trends do not well represent the response of a lake to climate change, e.g., autumn trends are dominant in the north of Europe, whereas winter trends are dominant in the south. Intra-lake variability of trends indicates that trends at single in-situ stations do not necessarily represent the lake’s response. The LSWT dataset generated for this study gives some new and interesting insights into the response of European lakes to climate change during the last 36 years (1981–2016).

The Baltic Sea is one of the fastest-warming marginal seas globally, and its temperature rise has adversely affected its physical and biochemical characteristics. In this study, forty years (1982–2021) of sea surface temperature (SST) data from the advanced very high resolution radiometer (AVHRR) were used to investigate spatial and temporal SST variability of the Baltic Sea. To this end, annual maximum and minimum SST stacked series, i.e., time series of stacked layers of satellite data, were generated using high-quality observations acquired at night and were fed to an automatic algorithm to detect linear and non-linear trend patterns. The linear trend pattern was the dominant trend type in both stacked series, while more pixels with non-linear trend patterns were detected when using the annual minimum SST. However, both stacked series showed increases in SST across the Baltic Sea. Annual maximum SST increased by an average of 0.062 ± 0.041 °C per year between 1982 and 2021, while annual minimum SST increased by an average of 0.035 ± 0.017 °C per year over the same period. Averaging annual maximum and minimum trends produces a spatial average of 0.048 ± 0.022 °C rise in SST per year over the last four decades.

Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. Since any satellite only operates over a relatively short period of time, creating a climate data record also requires the combination of space-borne measurements from a series of several (often similar) satellite sensors. Simply combining calibrated measurements from several sensors can, however, produce an inconsistent climate data record. This is particularly true of older, historic sensors whose behaviour in space was often different from their behaviour during pre-launch calibration and more scientific value can be derived from considering the series of historical and present satellites as a whole. Here, we consider harmonisation as a process that obtains new calibration coefficients for revised sensor calibration models by comparing calibrated measurements over appropriate satellite-to-satellite matchups, such as simultaneous nadir overpasses and which reconciles the calibration of different sensors given their estimated spectral response function differences. We present the concept of a framework that establishes calibration coefficients and their uncertainty and error covariance for an arbitrary number of sensors in a metrologically-rigorous manner. We describe harmonisation and its mathematical formulation as an inverse problem that is extremely challenging when some hundreds of millions of matchups are involved and the errors of fundamental sensor measurements are correlated. We solve the harmonisation problem as marginalised errors in variables regression. The algorithm involves computation of first and second-order partial derivatives using Algorithmic Differentiation. Finally, we present re-calibrated radiances from a series of nine Advanced Very High Resolution Radiometer sensors showing that the new time series has smaller matchup differences compared to the unharmonised case while being consistent with uncertainty statistics.

Patterns of recruitment in marine ecosystems can reflect the distribution of adults, dispersal by ocean currents, or patterns of mortality after settlement. In turn, patterns of recruitment can play an important role in determining patterns of adult abundance and community dynamics. Here we examine the biogeographic structure of recruitment variability along the U.S. West Coast and examine its association with temperature variability. From 1997 to 2004 we monitored monthly recruitment rates of dominant intertidal invertebrates, mussels and barnacles, at 26 rocky shore sites on the West Coast of the United States, from northern Oregon to southern California, a span of 1750 km of coastline. We examined spatial variation in the dynamics of recruitment rates and their relationship to coastal oceanography using satellite-derived time series of monthly sea surface temperature (SST). Recruitment rates showed a biogeographic structure with large regions under similar dynamics delimited by abrupt transitions. The seasonal peak in recruitment rates for both mussels and barnacles changed from a late summer—early fall peak in Oregon to winter or early spring in northern California, and then back toward summer in southern California. Recruitment rates varied greatly in magnitude across the latitudinal range. The barnacle Balanus glandula and mussels (Mytilus spp.) showed a decline of two orders of magnitude south of Oregon. In contrast, recruitment rates of barnacles of the genus Chthamalus showed a variable pattern across the region examined. The spatial distribution of associations between raw SST and recruitment rates for all species showed positive associations, indicating recruitment during warm months, for all species in Oregon, northern California, and several sites in south-central California. By considerably extending the spatial and temporal scales beyond that of previous studies on larval recruitment rates in this system, our study has identified major biogeographic breaks around Cape Blanco and Point Conception despite considerable spatial and temporal variation within each region and among species. These large differences in recruitment rates across biogeographic scales highlight the need for a better understanding of larval responses to ocean circulation patterns in the conservation and management of coastal ecosystems.

Spatial and temporal variations in net primary production (NPP) are of great importance to ecological studies, natural resource management, and terrestrial carbon sink estimates. However, most of the existing estimates of interannual variation in NPP at regional and global scales were made at coarse resolutions with climate-driven process models. In this study, we quantified global NPP variation at an 8 km and 10-day resolution from 1981 to 2000 based on satellite observations. The high resolution was achieved using the GLObal Production Efficiency Model (GLO-PEM), which was driven with variables derived almost entirely from satellite remote sensing. The results show that there was an increasing trend toward enhanced terrestrial NPP that was superimposed on high seasonal and interannual variations associated with climate variability and that the increase was occurring in both northern and tropical latitudes. NPP generally decreased in El Niño season and increased in La Niña seasons, but the magnitude and spatial pattern of the response varied widely between individual events. Our estimates also indicate that the increases in NPP during the period were caused mainly by increases in atmospheric carbon dioxide and precipitation. The enhancement of NPP by warming was limited to northern high latitudes (above 50°N); in other regions, the interannual variations in NPP were correlated negatively with temperature and positively with precipitation.

Aboveground net primary production (ANPP) of grasslands varies spatially and temporally. Spectral information provided by remote sensors is a promising new tool that may be able to estimate ANPP in real time and at low cost. The objectives of this study were (a) to evaluate at a seasonal scale the relationship between ANPP and the normalized difference vegetation index (NDVI), (b) to estimate seasonal variations in the coefficient of conversion of absorbed radiation into aboveground biomass (εₐ), and (c) to identify the environmental controls on such temporal changes. We used biomass-based field determinations of ANPP for two grassland sites in the Flooding Pampa, Argentina, and related them with NDVI data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR) satellites using three different models. Results were compared with data obtained from the new Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at an additional site. The first model was based solely on NDVI; the second was based on the amount of photosynthetically active radiation absorbed by the green vegetation$({\\rm APAR}_{{\\rm g}})$, which was derived from NDVI and incoming photosynthetically active radiation (PAR); the third was based on${\\rm APAR}_{{\\rm g}}$and εₐ, which was in turn estimated from climatic variables. NDVI explained between 63 and 93% of ANPP variation, depending on the site considered. Estimates of ANPP were not improved by considering the variation in incoming PAR. At both sites, εₐ varied seasonally (from 0.2 to 1.2 g DM/MJ) and was significantly associated with combinations of precipitation and temperature. Combining εₐ variations with${\\rm APAR}_{{\\rm g}}$increased our ability to account for seasonal ANPP variations at both sites. Our results indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and εₐ are correlated throughout the seasons. Thus, in most cases, seasonal variations of εₐ associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.

This study explores the relationship between the Agulhas Current system and El Niño Southern Oscillation (ENSO) events. Specifically, it addresses monthly to yearly variations in Agulhas leakage where the Agulhas Current sheds waters into the Atlantic Ocean, in turn affecting meridional overturning circulation (MOC). Sea surface temperature (SST) data from the National Oceanic and Atmospheric Administration’s (NOAA) Advanced Very High Resolution Radiometer (AVHRR) combined with sea surface salinity (SSS) from Soil Moisture Ocean Salinity (SMOS) and Simple Ocean Data Assimilation (SODA) reanalysis are used to explore changes in Agulhas leakage dynamics. Agulhas leakage is anomalously warm in response to El Niño and anomalously cool in response to La Niña. The corresponding SSS signal shows both a primary and secondary signal response. At first, the SSS signal of Agulhas leakage is anomalously fresh in response to El Niño, but this primary signal is replaced by a secondary anomalously saline signal. In response to La Niña, the primary SSS signal of Agulhas leakage is anomalously saline, while the secondary SSS signal is anomalously fresh. The lag between the peak of ENSO and the response in SST and the corresponding primary SSS signal of Agulhas leakage is about 20 months, followed by the secondary SSS signal at a lag of about 26 months. In general, increasing ENSO strength increases the extremes of the resulting anomalous SST and SSS signal and impacts the Agulhas leakage region earlier during El Niño and slightly later during La Niña.