Special Issue "Monitoring Global Vegetation with AVHRR NDVI3g Data (1981-2011)"
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A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 30 June 2013
Special Issue Editors
Guest Editor
Prof. Ranga B. Myneni
Department of Geography & Environment, Boston University, Boston, MA 20115, USA
Website: http://cliveg.bu.edu/people/rmyneni.html
E-Mail: ranga.myneni@gmail.com
Interests: remote sensing of vegetation; satellite data analysis; radiative transfer in vegetative media; algorithms for biophysical variables from satellite data; climate/vegetation interactions; terrestrial carbon cycle
Guest Editor
Dr. Jorge E. Pinzón
Science Systems and Applications, Inc., Code 618, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
E-Mail: jorge.e.pinzon@nasa.gov
Phone: +301 614 6685
Fax: +301 614 6695
Interests: feature extraction from large geophysical temporal, multi- and hyper-spectral data; quality assurance and calibration of geophysical observations; image analysis, image compression, image classification; remote sensing applications for monitoring eco-climatic conditions associated with emerging infectious diseases
Special Issue Information
Dear Colleagues,
Vegetation indices are radiometric measures of photosynthetically active radiation absorbed by chlorophyll in the green leaves of vegetation canopies and are therefore good surrogate measures of the physiologically functioning surface greenness level of a region. In a series of articles during the early 1980s, Compton J. Tucker, demonstrated how the Normalized Difference Vegetation Index (NDVI) generated from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data can be used to map land cover and monitor vegetation changes and desertification at continental and global scales. These papers opened a whole new avenue of investigations regarding monitoring vegetation changes at a host of spatial resolutions and time scales. A simple search on the Web of Science reveals over 5000 articles containing NDVI either in the title or in the abstract. Compton J. Tucker continued to generate the NDVI time series over the past 30 years, in the framework of the Global Inventory Monitoring and Modeling System (GIMMS) project, carefully assembling it from different AVHRR sensors and accounting for various deleterious effects, such as calibration loss, orbital drift, volcanic eruptions, etc. The latest version of the GIMMS NDVI data set spans the period July 1981 to December 2011 and is termed NDVI3g (third generation GIMMS NDVI from AVHRR sensors). The goal of this special issue is to understand variability, long-term trends and changes in vegetation on our planet at a host of spatial scales over the past 30 years using this new, improved data set. Although the NDVI3g data set has not yet been released, scientists interested in contributing to this special issue are encouraged to contact the guest editors with a tentative title and two-line abstract to obtain access to the data set. The following is a tentative list of papers to appear in this special issue.
Prof. Ranga B. Myneni
Dr. Jorge E. Pinzón
Guest Editors
Submission
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed Open Access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 800 CHF (Swiss Francs).
Keywords
- NDVI
- AVHRR
- remote sensing
- vegetation trends
- phenology
- climate change
- drought
- arctic vegetation
- sahelian vegetation
- land degradation
- desertification
- carbon cycle
- dynamics vegetation models
Published Papers (11 papers)
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Received: 10 December 2012; in revised form: 24 January 2013 / Accepted: 28 January 2013 / Published: 4 February 2013
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Abstract: The ‘rain use efficiency’ (RUE) may be defined as the ratio of above-ground net primary productivity (ANPP) to annual precipitation, and it is claimed to be a conservative property of the vegetation cover in drylands, if the vegetation cover is not subject to non-precipitation related land degradation. Consequently, RUE may be regarded as means of normalizing ANPP for the impact of annual precipitation, and as an indicator of non-precipitation related land degradation. Large scale and long term identification and monitoring of land degradation in drylands, such as the Sahel, can only be achieved by use of Earth Observation (EO) data. This paper demonstrates that the use of the standard EO-based proxy for ANPP, summed normalized difference vegetation index (NDVI) (National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies 3rd generation (GIMMS3g)) over the year (ΣNDVI), and the blended EO/rain gauge based data-set for annual precipitation (Climate Prediction Center Merged Analysis of Precipitation, CMAP) results in RUE-estimates which are highly correlated with precipitation, rendering RUE useless as a means of normalizing for the impact of annual precipitation on ANPP. By replacing ΣNDVI by a ‘small NDVI integral’, covering only the rainy season and counting only the increase of NDVI relative to some reference level, this problem is solved. Using this approach, RUE is calculated for the period 1982–2010. The result is that positive RUE-trends dominate in most of the Sahel, indicating that non-precipitation related land degradation is not a widespread phenomenon. Furthermore, it is argued that two preconditions need to be fulfilled in order to obtain meaningful results from the RUE temporal trend analysis: First, there must be a significant positive linear correlation between annual precipitation and the ANPP proxy applied. Second, there must be a near-zero correlation between RUE and annual precipitation. Thirty-seven percent of the pixels in Sahel satisfy these requirements and the paper points to a range of different reasons why this may be the case.
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Received: 4 December 2012; in revised form: 4 February 2013 / Accepted: 5 February 2013 / Published: 18 February 2013
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Abstract: Using estimated leaf unfolding data and two types of Normalized Difference Vegetation Index (NDVI and NDVI3g) data generated from the Advanced Very High Resolution Radiometer (AVHRR) in the deciduous broadleaf forest of northern China during 1986 to 2006, we analyzed spatial, temporal and spatiotemporal relationships and differences between ground-based growing season beginning (BGS) and NDVI (NDVI3g)-retrieved start of season (SOS and SOS3g), and compared effectiveness of NDVI and NDVI3g in monitoring BGS. Results show that the spatial series of SOS (SOS3g) correlates positively with the spatial series of BGS at all pixels in each year (P < 0.001). Meanwhile, the time series of SOS (SOS3g) correlates positively with the time series of BGS at more than 65% of all pixels during the study period (P < 0.05). Furthermore, when pooling SOS (SOS3g) time series and BGS time series from all pixels, a significant positive correlation (P < 0.001) was also detectable between the spatiotemporal series of SOS (SOS3g) and BGS. In addition, the spatial, temporal and spatiotemporal differences between SOS (SOS3g) and BGS are at acceptable levels overall. Generally speaking, SOS3g is more consistent and accurate than SOS in capturing BGS, which suggests that NDVI3g data might be more sensitive than NDVI data in monitoring vegetation leaf unfolding.
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Received: 28 December 2012; in revised form: 7 February 2013 / Accepted: 16 February 2013 / Published: 22 February 2013
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Abstract: Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary layer. LAI and FPAR are also state variables in hydrological, ecological, biogeochemical and crop-yield models. The generation, evaluation and an example case study documenting the utility of 30-year long data sets of LAI and FPAR are described in this article. A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Niño-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website.
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Received: 20 December 2012; in revised form: 19 February 2013 / Accepted: 19 February 2013 / Published: 22 February 2013
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Abstract: The spatial distribution of crops and farming systems in Africa is determined by the duration of the period during which crop and livestock water requirements are met. The length of growing period (LGP) is normally assessed from weather station data—scarce in large parts of Africa—or coarse-resolution rainfall estimates derived from weather satellites. In this study, we analyzed LGP and its variability based on the 1981–2011 GIMMS NDVI3g dataset. We applied a variable threshold method in combination with a searching algorithm to determine start- and end-of-season. We obtained reliable LGP estimates for arid, semi-arid and sub-humid climates that are consistent in space and time. This approach effectively mapped bimodality for clearly separated wet seasons in the Horn of Africa. Due to cloud contamination, the identified bimodality along the Guinea coast was judged to be less certain. High LGP variability is dominant in arid and semi-arid areas, and is indicative of crop failure risk. Significant negative trends in LGP were found for the northern part of the Sahel, for parts of Tanzania and northern Mozambique, and for the short rains of eastern Kenya. Positive trends occurred across western Africa, in southern Africa, and in eastern Kenya for the long rains. Our LGP analysis provides useful information for the mapping of farming systems, and to study the effects of climate variability and other drivers of change on vegetation and crop suitability.
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Received: 20 December 2012; in revised form: 14 February 2013 / Accepted: 21 February 2013 / Published: 1 March 2013
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Abstract: Vegetation belongs to the components of the Earth surface, which are most extensively studied using historic and present satellite records. Recently, these records exceeded a 30-year time span composed of preprocessed fortnightly observations (1981–2011). The existence of monotonic changes and trend shifts present in such records has previously been demonstrated. However, information on timing and type of such trend shifts was lacking at global scale. In this work, we detected major shifts in vegetation activity trends and their associated type (either interruptions or reversals) and timing. It appeared that the biospheric trend shifts have, over time, increased in frequency, confirming recent findings of increased turnover rates in vegetated areas. Signs of greening-to-browning reversals around the millennium transition were found in many regions (Patagonia, the Sahel, northern Kazakhstan, among others), as well as negative interruptions—“setbacks”—in greening trends (southern Africa, India, Asia Minor, among others). A minority (26%) of all significant trends appeared monotonic.
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Received: 31 December 2012; in revised form: 27 February 2013 / Accepted: 27 February 2013 / Published: 6 March 2013
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Abstract: Increasing water use and droughts, along with climate variability and land use change, have seriously altered vegetation growth patterns and ecosystem response in several regions alongside the Andes Mountains. Thirty years of the new generation biweekly normalized difference vegetation index (NDVI3g) time series data show significant land cover specific trends and variability in annual productivity and land surface phenological response. Productivity is represented by the growing season mean NDVI values (July to June). Arid and semi-arid and sub humid vegetation types (Atacama desert, Chaco and Patagonia) across Argentina, northern Chile, northwest Uruguay and southeast Bolivia show negative trends in productivity, while some temperate forest and agricultural areas in Chile and sub humid and humid areas in Brazil, Bolivia and Peru show positive trends in productivity. The start (SOS) and length (LOS) of the growing season results show large variability and regional hot spots where later SOS often coincides with reduced productivity. A longer growing season is generally found for some locations in the south of Chile (sub-antarctic forest) and Argentina (Patagonia steppe), while central Argentina (Pampa-mixed grasslands and agriculture) has a shorter LOS. Some of the areas have significant shifts in SOS and LOS of one to several months. The seasonal Multivariate ENSO Indicator (MEI) and the Antarctic Oscillation (AAO) index have a significant impact on vegetation productivity and phenology in southeastern and northeastern Argentina (Patagonia and Pampa), central and southern Chile (mixed shrubland, temperate and sub-antarctic forest), and Paraguay (Chaco).
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Received: 19 January 2013; in revised form: 28 January 2013 / Accepted: 5 March 2013 / Published: 12 March 2013
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Abstract: Projected changes in the frequency and severity of droughts as a result of increase in greenhouse gases have a significant impact on the role of vegetation in regulating the global carbon cycle. Drought effect on vegetation Gross Primary Production (GPP) is usually modeled as a function of Vapor Pressure Deficit (VPD) and/or soil moisture. Climate projections suggest a strong likelihood of increasing trend in VPD, while regional changes in precipitation are less certain. This difference in projections between VPD and precipitation can cause considerable discrepancies in the predictions of vegetation behavior depending on how ecosystem models represent the drought effect. In this study, we scrutinized the model responses to drought using the 30-year record of Global Inventory Modeling and Mapping Studies (GIMMS) 3g Normalized Difference Vegetation Index (NDVI) dataset. A diagnostic ecosystem model, Terrestrial Observation and Prediction System (TOPS), was used to estimate global GPP from 1982 to 2009 under nine different experimental simulations. The control run of global GPP increased until 2000, but stayed constant after 2000. Among the simulations with single climate constraint (temperature, VPD, rainfall and solar radiation), only the VPD-driven simulation showed a decrease in 2000s, while the other scenarios simulated an increase in GPP. The diverging responses in 2000s can be attributed to the difference in the representation of the impact of water stress on vegetation in models, i.e., using VPD and/or precipitation. Spatial map of trend in simulated GPP using GIMMS 3g data is consistent with the GPP driven by soil moisture than the GPP driven by VPD, confirming the need for a soil moisture constraint in modeling global GPP.
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Received: 1 February 2013; in revised form: 13 March 2013 / Accepted: 18 March 2013 / Published: 21 March 2013
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Abstract: Using a recent Leaf Area Index (LAI) dataset and the Community Land Model version 4 (CLM4), we investigated percent changes and controlling factors of global vegetation growth for the period 1982 to 2009. Over that 28-year period, both the remote-sensing estimate and model simulation show a significant increasing trend in annual vegetation growth. Latitudinal asymmetry appeared in both products, with small increases in the Southern Hemisphere (SH) and larger increases at high latitudes in the Northern Hemisphere (NH). The south-to-north asymmetric land surface warming was assessed to be the principal driver of this latitudinal asymmetry of LAI trend. Heterogeneous precipitation functioned to decrease this latitudinal LAI gradient, and considerably regulated the local LAI change. A series of factorial experiments were specially-designed to isolate and quantify contributions to LAI trend from different external forcings such as climate variation, CO2, nitrogen deposition and land use and land cover change. The climate-only simulation confirms that climate change, particularly the asymmetry of land temperature variation, can explain the latitudinal pattern of LAI change. CO2 fertilization during the last three decades was simulated to be the dominant cause for the enhanced vegetation growth. Our study, though limited by observational and modeling uncertainties, adds further insight into vegetation growth trends and environmental correlations. These validation exercises also provide new quantitative and objective metrics for evaluation of land ecosystem process models at multiple spatio-temporal scales.
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Received: 1 March 2013; in revised form: 24 April 2013 / Accepted: 24 April 2013 / Published: 2 May 2013
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Abstract: Arctic-Boreal region—mainly consisting of tundra, shrub lands, and boreal forests—has been experiencing an amplified warming over the past 30 years. As the main driving force of vegetation growth in the north, temperature exhibits tight coupling with the Normalized Difference Vegetation Index (NDVI)—a proxy to photosynthetic activity. However, the comparison between North America (NA) and northern Eurasia (EA) shows a weakened spatial dependency of vegetation growth on temperature changes in NA during the past decade. If this relationship holds over time, it suggests a 2/3 decrease in vegetation growth under the same rate of warming in NA, while the vegetation response in EA stays the same. This divergence accompanies a circumpolar widespread greening trend, but 20 times more browning in the Boreal NA compared to EA, and comparative greening and browning trends in the Arctic. These observed spatial patterns of NDVI are consistent with the temperature record, except in the Arctic NA, where vegetation exhibits a similar long-term trend of greening to EA under less warming. This unusual growth pattern in Arctic NA could be due to a lack of precipitation velocity compared to the temperature velocity, when taking velocity as a measure of northward migration of climatic conditions.
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Received: 27 February 2013; in revised form: 17 April 2013 / Accepted: 25 April 2013 / Published: 3 May 2013
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Abstract: Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.
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Received: 25 March 2013; in revised form: 2 May 2013 / Accepted: 7 May 2013 / Published: 17 May 2013
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Abstract: Pasture ecosystems may be particularly vulnerable to land degradation due to the high risk of human disturbance (e.g., overgrazing, burning, etc.), especially when compared with natural ecosystems (non-pasture, non-cultivated) where direct human impacts are minimal. Using maximum annual leaf area index (LAImax) as a proxy for standing biomass and peak annual aboveground productivity, we analyze greening and browning trends in pasture areas from 1982–2008. Inter-annual variability in pasture productivity is strongly controlled by precipitation (positive correlation) and, to a lesser extent, temperature (negative correlation). Linear temporal trends are significant in 23% of pasture cells, with the vast majority of these areas showing positive LAImax trends. Spatially extensive productivity declines are only found in a few regions, most notably central Asia, southwest North America, and southeast Australia. Statistically removing the influence of precipitation reduces LAImax trends by only 13%, suggesting that precipitation trends are only a minor contributor to long-term greening and browning of pasture lands. No significant global relationship was found between LAImax and pasture intensity, although the magnitude of trends did vary between cells classified as natural versus pasture. In the tropics and Southern Hemisphere, the median rate of greening in pasture cells is significantly higher than for cells dominated by natural vegetation. In the Northern Hemisphere extra-tropics, conversely, greening of natural areas is 2–4 times the magnitude of greening in pasture areas. This analysis presents one of the first global assessments of greening and browning trends in global pasture lands, including a comparison with vegetation trends in regions dominated by natural ecosystems. Our results suggest that degradation of pasture lands is not a globally widespread phenomenon and, consistent with much of the terrestrial biosphere, there have been widespread increases in pasture productivity over the last 30 years.
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Planned Papers
The below list represents only planned manuscripts. Some of these
manuscripts have not been received by the Editorial Office yet. Papers
submitted to MDPI journals are subject to peer-review.
1 Anav et al. Evaluation of DGVMs in reproducing satellite derived LAI, Part 1 Coupled ESMs
2 Anyamba et al. 30 years of growing season NDVI patterns and trends over the Sahel
3 Alessandri et al.
4 Atzberger Phenological metrics derived over the European continent from NDVI3g data and MODIS time series
5 Barichivich et al. Drought and growing season photosynthetic activity in northern terrestrial ecosystems
6 Beck et al. Interpretation of cross-sensor remotely sensed boreal vegetation productivity pattens
7 Bhatt et al. Pan-Arctic tundra vegetation change and variability
8 Bi et al. Divergence in trends of vegetation greenness between North America and Eurasia
9 Brown et al. Relating observed trends in nutritional status from 1985-2011 using demographic and health survey data to variations in environmental conditions as measured by the GIMMS NDVI3g dataset
10 Ciais et al. Comparison of phenology and soil moisture simulated by a process based model with 30 years of microwave and visible remote sensing observations over Africa
11 Cook et al. Long–term greening and browning trends in global pasture lands (RS-29129; Jan-09-2013)
12 Cécile Dardel et al. 28 years of rangeland monitoring in Sahel from the sky and on the ground (Gourma, Mali)
13 Dong et al. Climatic warming affecting cropping system and pattern in Northern Temperate regions
14 Dye et al. Seasonality and trends in snow-cover, vegetation index, and temperature in northern Eurasia: 1982 to 2011.
15 Eastman et al. Global trends in seasonality of NDVI, 1982-2011
16 Erasmi et al. Vegetation greenness and dynamics over Northeast Brazil and its relation to ENSO
17 Fensholt et al. Assessment of land degradation/recovery in the African Sahel from long-term Earth Observation based vegetation and precipitation trends: EO-based land degradation assessment in Sahel (RS-27454)
18 Forkel et al. Inter-annual variability controls the reliability of greening and browning trend change estimates in NDVI time series.
18 Guerschman et al. Assimilating observations of FPAR in a hydrological model for Australia
19 Hashimoto et al. Structural uncertainty in model-simulated trends of global Gross Primary Production (RS-27795; Dec-12-2012)
20 Høgda et al Vegetation dynamics in northern Fennoscandia with focus on the start of growing season
21 Ichii et al. Evaluation of temporal variations in modeled terrestrial carbon cycle in Asia using long-term record of satellite based vegetation indices.
22 Ivits et al. Characterization of phenological change varieties of global ecosystems
23 de Jong et al. Shifts in global vegetation-activity trends
24 Van Leeuwen et al. Trends and ENSO/AAO driven variability in productivity and phenology alongside the Andes Mountains (RS-27271; Nov-30-2012)
25 Luo et. al. Validating satellite-derived start of season with estimated leaf unfolding data in the deciduous broadleaf forest of northern China (RS-27421; Dec-04-2012)
26 Mao et al. Global latitudinal-asymmetric vegetation growth trends and their driving mechanisms: 1982-2009
27 Mao et al. Global estimation of CMIP5 Earth System Models in simulating Leaf Area Index against remote-sensing products
28 Murray et al. Evaluation of DGVMs in reproducing satellite derived LAI, Part 2 Uncoupled DGVMs
29 Mathukumalli Long-term variability in vegetation dynamics in the Indo-Bangladesh Sundarban mangrove ecosystem
30 Nan et al. Detecting turning points in seasonal vegetation greenness in the Northern Hemisphere over the last 30 years
31 Pettorelli et al. NDVI in ecology: where and when does it work best?
32 Piao et al. Vegetation greeness changes in SouthEast Asia over the last three decades
33 Pinzon et al. Revisiting error, precision and uncertainty in NDVI AVHRR data: development of a consistent NDVI3g time series
34 Saatchi et al. Coupled impact of climate and land use change on Caspian Sea basin hydrology
35 Scheftic & Zeng et al. Comparison of the interannual variability of Fractional Vegetation Cover and the seasonal variability of Green Vegetation Fraction over the North American Monsoon Region using four different NDVI products
36 Vrieling et al. Length of growing period over Africa: variability and trends from 30 years of NDVI time series (RS-27228; Nov-30-2012)
37 Wang Kai et al. Evaluation of a land surface solar radiation partitioning scheme using remote sensing and FLUXNET FPAR datasets
38 Williams et al.
39 Xu et al. Analysis of vegetation dynamics and its relationship with climate in China from 1982 to 2011
40 Zhou et al. Interannual variability of vegetation greenness and its linkage with hydroclimate variables over the last 30 years
41 Zhu et al. Global data sets of vegetation LAI3g and FPAR3g derived from GIMMS NDVI3g for the period 1981 to 2011
42 Zhu et al. Trends in global vegetation greenness 1982 to 2011
Last update: 22 January 2013
