| 1. |
- Cai, Zhanzhang, et al.
(författare)
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Modelling Daily Gross Primary Productivity with Sentinel-2 Data in the Nordic Region-Comparison with Data from MODIS
- 2021
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 13:3
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Tidskriftsartikel (refereegranskat)abstract
- The high-resolution Sentinel-2 data potentially enable the estimation of gross primary productivity (GPP) at finer spatial resolution by better capturing the spatial variation in a heterogeneous landscapes. This study investigates the potential of 10 m resolution reflectance from the Sentinel-2 Multispectral Instrument to improve the accuracy of GPP estimation across Nordic vegetation types, compared with the 250 m and 500 m resolution reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS). We applied linear regression models with inputs of two-band enhanced vegetation index (EVI2) derived from Sentinel-2 and MODIS reflectance, respectively, together with various environmental drivers to estimate daily GPP at eight Nordic eddy covariance (EC) flux tower sites. Compared with the GPP from EC measurements, the accuracies of modelled GPP were generally high (R-2 = 0.84 for Sentinel-2; R-2 = 0.83 for MODIS), and the differences between Sentinel-2 and MODIS were minimal. This demonstrates the general consistency in GPP estimates based on the two satellite sensor systems at the Nordic regional scale. On the other hand, the model accuracy did not improve by using the higher spatial-resolution Sentinel-2 data. More analyses of different model formulations, more tests of remotely sensed indices and biophysical parameters, and analyses across a wider range of geographical locations and times will be required to achieve improved GPP estimations from Sentinel-2 satellite data.
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| 2. |
- Cai, Zhanzhang, et al.
(författare)
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Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data
- 2017
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 9:12
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Tidskriftsartikel (refereegranskat)abstract
- Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics.
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| 3. |
- Conradt, Tobias, et al.
(författare)
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Cross-sectoral impacts of the 2018–2019 Central European drought and climate resilience in the German part of the Elbe River basin
- 2023
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Ingår i: Regional Environmental Change. - : Springer Science and Business Media LLC. - 1436-378X .- 1436-3798. ; 23, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- The 2018–2019 Central European drought was probably the most extreme in Germany since the early sixteenth century. We assess the multiple consequences of the drought for natural systems, the economy and human health in the German part of the Elbe River basin, an area of 97,175 km2 including the cities of Berlin and Hamburg and contributing about 18% to the German GDP. We employ meteorological, hydrological and socio-economic data to build a comprehensive picture of the drought severity, its multiple effects and cross-sectoral consequences in the basin. Time series of different drought indices illustrate the severity of the 2018–2019 drought and how it progressed from meteorological water deficits via soil water depletion towards low groundwater levels and river runoff, and losses in vegetation productivity. The event resulted in severe production losses in agriculture (minus 20–40% for staple crops) and forestry (especially through forced logging of damaged wood: 25.1 million tons in 2018–2020 compared to only 3.4 million tons in 2015–2017), while other economic sectors remained largely unaffected. However, there is no guarantee that this socio-economic stability will be sustained in future drought events; this is discussed in the light of 2022, another dry year holding the potential for a compound crisis. Given the increased probability for more intense and long-lasting droughts in most parts of Europe, this example of actual cross-sectoral drought impacts will be relevant for drought awareness and preparation planning in other regions.
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| 4. |
- Eklundh, Lars, et al.
(författare)
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An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration
- 2011
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 11:8, s. 7678-7709
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Tidskriftsartikel (refereegranskat)abstract
- We present a network of sites across Fennoscandia for optical sampling of vegetation properties relevant for phenology monitoring and satellite data calibration. The network currently consists of five sites, distributed along an N-S gradient through Sweden and Finland. Two sites are located in coniferous forests, one in a deciduous forest, and two on peatland. The instrumentation consists of dual-beam sensors measuring incoming and reflected red, green, NIR, and PAR fluxes at 10-min intervals, year-round. The sensors are mounted on separate masts or in flux towers in order to capture radiation reflected from within the flux footprint of current eddy covariance measurements. Our computations and model simulations demonstrate the validity of using off-nadir sampling, and we show the results from the first year of measurement. NDVI is computed and compared to that of the MODIS instrument on-board Aqua and Terra satellite platforms. PAR fluxes are partitioned into reflected and absorbed components for the ground and canopy. The measurements demonstrate that the instrumentation provides detailed information about the vegetation phenology and variations in reflectance due to snow cover variations and vegetation development. Valuable information about PAR absorption of ground and canopy is obtained that may be linked to vegetation productivity.
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| 5. |
- Huang, Ke, et al.
(författare)
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The confounding effect of snow cover on assessing spring phenology from space : A new look at trends on the Tibetan Plateau
- 2021
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697. ; 756
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Tidskriftsartikel (refereegranskat)abstract
- The Tibetan Plateau is the highest and largest plateau in the world, hosting unique alpine grassland and having a much higher snow cover than any other region at the same latitude, thus representing a “climate change hot-spot”. Land surface phenology characterizes the timing of vegetation seasonality at the per-pixel level using remote sensing systems. The impact of seasonal snow cover variations on land surface phenology has drawn much attention; however, there is still no consensus on how the remote sensing estimated start of season (SOS) is biased by the presence of preseason snow cover. Here, we analyzed SOS assessments from time series of satellite derived vegetation indices and solar-induced chlorophyll fluorescence (SIF) during 2003–2016 for the Tibetan Plateau. We evaluated satellite-based SOS with field observations and gross primary production (GPP) from eddy covariance for both snow-free and snow covered sites. SOS derived from SIF was highly correlated with field data (R2 = 0.83) and also the normalized difference phenology index (NDPI) performed well for both snow free (R2 = 0.77) and snow covered sites (R2 = 0.73). On the contrary, normalized difference vegetation index (NDVI) correlates only weakly with field data (R2 = 0.35 for snow free and R2 = 0.15 for snow covered sites). We further found that an earlier end of the snow season caused an earlier estimate of SOS for the Tibetan Plateau from NDVI as compared to NDPI. Our research therefore adds new evidence to the ongoing debate supporting the view that the claimed advance in land surface SOS over the Tibetan Plateau is an artifact from snow cover changes. These findings improve our understanding of the impact of snow on land surface phenology in alpine ecosystems, which can further improve remote sensing based land surface phenology assessments in snow-influenced ecosystems.
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| 6. |
- Jin, Hongxiao, et al.
(författare)
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A physically based vegetation index for improved monitoring of plant phenology
- 2014
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 152, s. 512-525
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Tidskriftsartikel (refereegranskat)abstract
- Using a spectral vegetation index (VI) is an efficient approach for monitoring plant phenology from remotely-sensed data. However, the quantitative biophysical meaning of most VIs is still unclear, and, particularly at high northern latitudes characterized by low green biomass renewal rate and snow-affected VI signals, it is difficult to use them for tracking seasonal vegetation growth and retrieving phenology. In this study we propose a physically-based new vegetation index for characterizing terrestrial vegetation canopy green leaf area dynamics: the plant phenology index (PPI). PPI is derived from the solution to a radiative transfer equation, is computed from red and near-infrared (NIR) reflectance, and has a nearly linear relationship with canopy green leaf area index (LAI), enabling it to depict canopy foliage density well. This capability is verified with stacked-leaf measurements, canopy reflectance model simulations, and field LAI measurements from international sites. Snow influence on PPI is shown by modeling and satellite observations to be less severe than on the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), while soil brightness variations in general have moderate influence on PPI. Comparison of satellite-derived PPI to ground observations of plant phenology and gross primary productivity (GPP) shows strong similarity of temporal patterns over several Nordic boreal forest sites. The proposed PPI can thus serve as an efficient tool for estimating plant canopy growth, and will enable improved vegetation monitoring, particularly of evergreen needle-leaf forest phenology at high northern latitudes.
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| 7. |
- Jin, Hongxiao, et al.
(författare)
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Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index
- 2017
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 198, s. 203-212
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Tidskriftsartikel (refereegranskat)abstract
- Land surface phenology is frequently derived from remotely sensed data. However, over regions with seasonal snow cover, remotely-sensed land surface phenology may be dominated by snow seasonality, rather than showing true plant phenology. Overlooking snow influences may lead to inaccurate plant phenology estimation, and consequently to misinterpretation of climate-vegetation interactions. To address the problem we apply the recently developed plant phenology index (PPI) to Moderate Resolution Imaging Spectroradiometer (MODIS) data for estimating plant phenology metrics over northern Europe. We compare PPI-derived start and end of the growing season with ground observations by professionals (6 sites) and nonprofessional citizens (378 sites), with phenology metrics derived from gross primary productivity (GPP, 18 sites), and with data on the timing of snow cover. These data are also compared with land surface phenology metrics derived from the normalized difference vegetation index (NDVI) using the same MODIS data. We find that the PPI-retrieved plant phenology agrees with ground observations and GPP-derived phenology, and that the NDVI-derived phenology to a large extent agrees with the end-of-snowmelt for the start-of-season and the start-of-snowing for the end-of-season. PPI is thereby useful for more accurate estimation of plant phenology from remotely sensed data over northern Europe and other regions with seasonal snow cover.
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| 8. |
- Jin, Hongxiao, et al.
(författare)
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Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application
- 2021
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:10
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Tidskriftsartikel (refereegranskat)abstract
- Miniature hyperspectral and thermal cameras onboard lightweight unmanned aerial vehicles (UAV) bring new opportunities for monitoring land surface variables at unprecedented fine spatial resolution with acceptable accuracy. This research applies hyperspectral and thermal imagery from a drone to quantify upland rice productivity and water use efficiency (WUE) after biochar application in Costa Rica. The field flights were conducted over two experimental groups with bamboo biochar (BC1) and sugarcane biochar (BC2) amendments and one control (C) group without biochar application. Rice canopy biophysical variables were estimated by inverting a canopy radiative transfer model on hyperspectral reflectance. Variations in gross primary productivity (GPP) and WUE across treatments were estimated using light-use efficiency and WUE models respectively from the normalized difference vegetation index (NDVI), canopy chlorophyll content (CCC), and evapotranspiration rate. We found that GPP was increased by 41.9 +/- 3.4% in BC1 and 17.5 +/- 3.4% in BC2 versus C, which may be explained by higher soil moisture after biochar application, and consequently significantly higher WUEs by 40.8 +/- 3.5% in BC1 and 13.4 +/- 3.5% in BC2 compared to C. This study demonstrated the use of hyperspectral and thermal imagery from a drone to quantify biochar effects on dry cropland by integrating ground measurements and physical models.
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| 9. |
- Jin, Hongxiao, et al.
(författare)
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Higher vegetation sensitivity to meteorological drought in autumn than spring across European biomes
- 2023
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Ingår i: Communications Earth and Environment. - 2662-4435. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Europe has experienced severe drought events in recent decades, posing challenges to understand vegetation responses due to diverse vegetation distribution, varying growth stages, different drought characteristics, and concurrent hydroclimatic factors. To analyze vegetation response to meteorological drought, we employed multiple vegetation indicators across European biomes. Our findings reveal that vegetation sensitivity to drought increases as the canopy develops throughout the year, with sensitivities from −0.01 in spring to 0.28 in autumn and drought-susceptible areas from 18.5 to 57.8% in Europe. Soil water shortage exacerbates vegetation-drought sensitivity temporally, while its spatial impact is limited. Vegetation-drought sensitivity strongly correlates with vapor pressure deficit and partially with atmospheric CO2 concentration. These results highlight the spatiotemporal variations in vegetation-drought sensitivities and the influence of hydroclimatic factors. The findings enhance our understanding of vegetation response to drought and the impact of concurrent hydroclimatic factors, providing valuable sub-seasonal information for water management and drought preparedness.
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| 10. |
- Jin, Hongxiao, et al.
(författare)
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In Situ Calibration of Light Sensors for Long-Term Monitoring of Vegetation
- 2015
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - 0196-2892. ; 53:6, s. 3405-3416
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Tidskriftsartikel (refereegranskat)abstract
- Light sensors are increasingly used to monitor vegetation growing status by measuring reflectance or transmittance in multispectral or photosynthetically active radiation (PAR) bands. The measurements are then used to estimate vegetation indices or the fraction of absorbed PAR (FPAR) in a continuous and long-term manner and to serve as inputs to environmental monitoring and calibration/validation data for satellite remote sensing. However, light-sensor calibration is often overlooked or not properly attended to, which leads to difficulties when comparing the measurement results across sites and through time. In this paper, we investigate a practical and accurate user-level in situ calibration method in daylight. The calibration of a sensor pair is made for measuring either bihemispherical reflectance or hemispherical-conical reflectance, which are the two most common ground-based spectral measurements. Procedures and considerations are suggested for user calibration. We also provide a method for calibrating and measuring a single-sensor reflectance-derived Normalized Difference Vegetation Index (NDVI) from red and near-infrared bands. The calibration error propagation is analyzed, and the induced uncertainties in vegetation reflectance and in the NDVI are evaluated. The analysis and field measurements show that the NDVI estimated from a user calibration factor can be as accurate as, or even more accurate than, the manufacturer's calibration. The in situ calibration described here remedies the situation where reflectance for large field-of-view sensors cannot be always estimated from the manufacturer's calibration. The method developed in this paper may help improve the reliability of long-term field spectral measurements and contributes to the near-surface remote sensing of vegetation.
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