scholarly journals Model Estimates of the Land and Ocean Contributions to Biospheric Carbon and Water Fluxes Using MODIS Satellite Data

2011 ◽  
Vol 24 (14) ◽  
pp. 3558-3574 ◽  
Author(s):  
Paul B. Alton ◽  
Per E. Bodin
Keyword(s):  
Net Primary Productivity ◽  
Chlorophyll Concentration ◽  
Water Flux ◽  
Global Perspective ◽  
Area Index ◽  
Flux Evaporation ◽  
Moderate Resolution ◽  
Satellite Sensors ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Marine Productivity

Abstract Land and ocean are often treated separately in modeling studies despite their close links through the carbon, water, and energy cycles. However, biospheric models, particularly when used in conjunction with recent satellite datasets, provide a new, fully coupled, global perspective. The current investigation uses a new version of the Grid Enabled Integrated Earth system (GENIE-SF) to compare both the magnitude and the seasonal and zonal variation in water flux [evaporation E and precipitation (PPT)] and carbon flux [net primary productivity (NPP)] above land and ocean. GENIE-SF contains state-of-the-art representations of photosynthesis and is driven by the phenological cycles of leaf area index (LAI) and marine chlorophyll concentration, both recorded with the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors. The current study reveals the striking uniformity of the ocean–atmosphere carbon and water flux exchange, both temporally and spatially, compared to the corresponding land–atmosphere exchange. Although biospheric annual NPP (108 ± 27 GtC yr−1) is split almost equally between land (52% ± 9%) and ocean (48% ± 9%), the oceanic contribution to biospheric annual E exceeds that of the land by a factor of 6.7 ± 1.7. Simulations conducted over a 50-yr period (1951–2000) suggest that a 16% increase in land NPP, owing mainly to CO2 fertilization, may be partially offset by a decline in marine productivity.

scholarly journals An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

2021 ◽  
Vol 13 (4) ◽  
pp. 719
Author(s):  
Xiuxia Li ◽  
Shunlin Liang ◽  
Huaan Jin
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Continuous Data ◽  
Landsat Data ◽  
Area Index ◽  
Free Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Resolution Data

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.

scholarly journals Global White-Sky and Black-Sky FAPAR Retrieval Using the Energy Balance Residual Method: Algorithm and Validation

2019 ◽  
Vol 11 (9) ◽  
pp. 1004 ◽  
Author(s):  
Liu ◽  
Zhang ◽  
Xie ◽  
Liu ◽  
Song ◽  
...  
Keyword(s):  
Energy Balance ◽  
Area Index ◽  
Global Soil ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Comparison Of The Results ◽  
Spectral Mixture ◽  
Sky Conditions ◽  
Absorbed Photosynthetically Active Radiation

The fraction of absorbed photosynthetically active radiation by vegetation (FAPAR) is a key variable in describing the light absorption ability of the vegetation canopy. Most global FAPAR products, such as MCD15A2H and GEOV1, correspond to FAPAR under black-sky conditions at the satellite overpass time only. In this paper, we aim to produce both the global white-sky and black-sky FAPAR products based on the moderate resolution imaging spectroradiometer (MODIS) visible (VIS) albedo, leaf area index (LAI), and clumping index (CI) products. Firstly, a non-linear spectral mixture model (NSM) was designed to retrieve the soil visible (VIS) albedo. The global soil VIS albedo and its dynamics were successfully mapped at a resolution of 500 m using the MCD43A3 VIS albedo product and the MCD15A2H LAI product. Secondly, a method based on the energy balance residual (EBR) principle was presented to retrieve the white-sky and black-sky FAPAR using the MODIS broadband VIS albedo (white-sky and black-sky) product (MCD43A3), the LAI product (MCD15A2H) and CI products. Finally, the two EBR FAPAR products were compared with the MCD15A2H and Geoland2/BioPar version 1 (GEOV1) black-sky FAPAR products. A comparison of the results indicates that these FAPAR products show similar spatial and seasonal patterns. Direct validation using FAPAR observations from the Validation of Land European Remote sensing Instrument (VALERI) project demonstrates that the EBR black-sky FAPAR product was more accurate and had a lower bias (R2 = 0.917, RMSE = 0.088, and bias = −2.8 %) than MCD15A2H (R2 = 0.901, RMSE = 0.096, and bias = 7.6 % ) and GEOV1 (R2 = 0.868, RMSE = 0.105, and bias = 6.1%).

scholarly journals Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation

2021 ◽  
Vol 14 (1) ◽  
pp. 57
Author(s):  
Siyuan Chen ◽  
Lichun Sui ◽  
Liangyun Liu ◽  
Xinjie Liu
Keyword(s):  
Diffuse Radiation ◽  
Terrestrial Ecosystems ◽  
Accurate Estimation ◽  
Data Sets ◽  
Area Index ◽  
Diffuse Fraction ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Meteorological Observations ◽  
Radiation Conditions

Accurate estimation of gross primary productivity (GPP) is necessary to better understand the interaction of global terrestrial ecosystems with climate change and human activities. Light use efficiency (LUE)-based GPP models are widely used for retrieving several GPP products with various temporal and spatial resolutions. However, most LUE-based models assume a clear-sky condition, and the influence of diffuse radiation on GPP estimations has not been well considered. In this paper, a diffuse and direct (DDA) absorbed photosynthetically active radiation (APAR)-based method is proposed for better estimation of half-hourly GPP, which partitions APAR under diffuse and direct radiation conditions. Firstly, energy balance residual (EBR) FAPAR, moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) (MCD15A2H) and clumping index (CI) products, as well as solar radiation records supplied by FLUXNET2015 were used to calculate diffuse and direct APAR at a half-hourly scale. Then, an eddy covariance-LUE (EC-LUE) model and meteorological observations from FLUXNET2015 data sets were used for obtaining corresponding LUE values. A co-variation relationship between LUE and diffuse fraction was observed, and the LUE was higher under more diffuse radiation conditions. Finally, the DDA-based method was tested using the half-hourly FLUXNET GPP and compared with half-hourly GPP calculated using total APAR (GPP_TA). The results indicated that the half-hourly GPP estimated using the DDA-based method (GPP_DDA) was more accurate, giving higher R2 values, lower RMSE and RMSE* values (R2 varied from 0.565 to 0.682, RMSE ranged from 3.219 to 12.405 and RMSE* were within the range of 2.785 to 8.395) than the GPP_TA (R2 varied from 0.558 to 0.653, RMSE ranged from 3.407 to 13.081 and RMSE* were within the range of 3.321 to 9.625) across FLUXNET sites within different vegetation types. This study explored the effects of partitioning the diffuse and direct APAR on half-hourly GPP estimations, which demonstrates a higher agreement with FLUXNET GPP than total APAR-based GPP.

scholarly journals UV Reflectance of the Ocean from DSCOVR/EPIC: Comparisons with a Theoretical Model and Aura/OMI Observations

2019 ◽  
Vol 36 (11) ◽  
pp. 2087-2099 ◽  
Author(s):  
Alexander Vasilkov ◽  
Alexei Lyapustin ◽  
B. Greg Mitchell ◽  
Dong Huang
Keyword(s):  
Radiative Transfer ◽  
Chlorophyll Concentration ◽  
Atmospheric Correction ◽  
Ocean Water ◽  
Cloud Detection ◽  
Ozone Monitoring Instrument ◽  
Inherent Optical Properties ◽  
Uv Reflectance ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractUltraviolet (UV) data collected over the ocean by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) are used. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm adapted for EPIC processing performs cloud detection, aerosol retrievals, and atmospheric correction providing the water-leaving reflectance of the ocean at 340 and 388 nm. The water-leaving reflectance is an indicator of the presence of absorbing and scattering constituents in seawater. The retrieved water-leaving reflectance is compared with full radiative transfer calculations based on a model of inherent optical properties (IOP) of ocean water in UV. The model is verified with data collected on the Aerosol Characterization Experiments (ACE) Asia cruise supported by the NASA Sensor Intercomparison for Marine Biological and Interdisciplinary Ocean Studies (SIMBIOS) project. The model assumes that the ocean water IOPs are parameterized through a chlorophyll concentration. The radiative transfer simulations were carried out using the climatological chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite. The EPIC-derived water-leaving reflectance is also compared with climatological Lambertian-equivalent reflectivity (LER) of the ocean derived from measurements of the Ozone Monitoring Instrument (OMI) on board the NASA polar-orbiting Aura satellite. The EPIC reflectance agrees well (within 0.01) with the model reflectance except for oligotrophic oceanic areas. For those areas, the model reflectance is biased low by about 0.01 at 340 nm and up to 0.03 at 388 nm. The OMI-derived climatological LER is significantly higher than the EPIC water-leaving reflectance, largely due to the surface glint contribution. The globally averaged difference is about 0.04.

scholarly journals Comparison of Normalized Difference Vegetation Index Derived from Landsat, MODIS, and AVHRR for the Mesopotamian Marshes Between 2002 and 2018

2019 ◽  
Vol 11 (10) ◽  
pp. 1245 ◽  
Author(s):  
Reyadh Albarakat ◽  
Venkataraman Lakshmi
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Strong Positive Correlation ◽  
Statistical Correlations ◽  
Mesopotamian Marshes ◽  
Moderate Resolution ◽  
Satellite Sensors ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Linear Slope

The Mesopotamian marshes are a group of water bodies located in southern Iraq, in the shape of a triangle, with the cities Amarah, Nasiriyah, and Basra located at its corners. The marshes are appropriate habitats for a variety of birds and most of the commercial fisheries in the region. The normalized difference vegetation index (NDVI) has been derived using observations from various satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very-High-Resolution Radiometer (AVHRR), and Landsat over the Mesopotamian marshlands for the 17-year period between 2002 and 2018. We have chosen this time series (2002–2018) to monitor the change in vegetation of the study area since it is considered as a period of rehabilitation for the marshes (following a period when there was little to no water flowing into the marshes). Statistical analyses were performed to monitor the variability of the maximum biomass time (month of June). The results illustrated a strong positive correlation between the NDVI derived from Landsat, MODIS, and AVHRR. The statistical correlations were 0.79, 0.77, and 0.96 between Landsat and AVHRR, MODIS and AVHRR, and Landsat and MODIS, respectively. The linear slope of NDVI (Landsat, MODIS, and AVHRR) for each pixel over the period 2002–2018 displays a long-term trend of green biomass (NDVI) change in the study area, and the slope is slightly negative over most of the area. Slope values (−0.002 to −0.05) denote a slight decrease in the observed vegetation index over 17 years. The green biomass of the marshlands increased by 33.2% of the total area over 17 years. The areas of negative and positive slopes correspond to the same areas in slope map when calculated from Landsat, MODIS, and AVHRR, although they are different in spatial resolution (30 m, 1 km, and 5 km, respectively). The time series of the average NDVI (2002–2018) for three different sensors shows the highest and lowest NDVI values during the same years (for the month of June each year). The highest values were 0.19, 0.22, and 0.22 for Landsat, MODIS, and AVHRR, respectively, in 2006, and the lowest values were 0.09, 0.14, and 0.09 for Landsat, MODIS, and AVHRR, respectively, in 2003.

scholarly journals Changes in Atmospheric, Meteorological, and Ocean Parameters Associated with the 12 January 2020 Taal Volcanic Eruption

2020 ◽  
Vol 12 (6) ◽  
pp. 1026
Author(s):  
Feng Jing ◽  
Akshansha Chauhan ◽  
Ramesh P Singh ◽  
Prasanjit Dash
Keyword(s):  
Volcanic Eruption ◽  
Volcanic Plume ◽  
Ozone Monitoring Instrument ◽  
Volcanic Gas ◽  
Sea Temperature ◽  
Monitoring Instrument ◽  
Taal Volcano ◽  
Moderate Resolution ◽  
Satellite Sensors ◽  
Moderate Resolution Imaging Spectroradiometer

The Taal volcano erupted on 12 January 2020, the first time since 1977. About 35 mild earthquakes (magnitude greater than 4.0) were observed on 12 January 2020 induced from the eruption. In the present paper, we analyzed optical properties of volcanic aerosols, volcanic gas emission, ocean parameters using multi-satellite sensors, namely, MODIS (Moderate Resolution Imaging Spectroradiometer), AIRS (Atmospheric Infrared Sounder), OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) and ground observations, namely, Argo, and AERONET (AErosol RObotic NETwork) data. Our detailed analysis shows pronounced changes in all the parameters, which mainly occurred in the western and south-western regions because the airmass of the Taal volcano spreads westward according to the analysis of airmass trajectories and wind directions. The presence of finer particles has been observed by analyzing aerosol properties that can be attributed to the volcanic plume after the eruption. We have also observed an enhancement in SO2, CO, and water vapor, and a decrease in Ozone after a few days of the eruption. The unusual variations in salinity, sea temperature, and surface latent heat flux have been observed as a result of the ash from the Taal volcano in the south-west and south-east over the ocean. Our results demonstrate that the observations combining satellite with ground data could provide important information about the changes in the atmosphere, meteorology, and ocean parameters associated with the Taal volcanic eruption.

scholarly journals Effect of Using Multi-Year LULC and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

2021 ◽  
Author(s):  
Ibrahim Olayode Busari ◽  
Mehmet Cüneyd Demirel ◽  
Alice Newton
Keyword(s):  
Land Cover ◽  
Potential Evapotranspiration ◽  
Hydrologic Model ◽  
Parameter Uncertainties ◽  
Area Index ◽  
Distributed Hydrologic Model ◽  
Land Cover Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

This study explores the use of satellite-based LULC (Land Use / Land Cover) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

scholarly journals ValLAI_Crop, a validation dataset for coarse-resolution satellite LAI products over Chinese cropland

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Bowen Song ◽  
Liangyun Liu ◽  
Shanshan Du ◽  
Xiao Zhang ◽  
Xidong Chen ◽  
...  
Keyword(s):  
Field Measurements ◽  
Validation Dataset ◽  
Landsat Images ◽  
Area Index ◽  
Coarse Resolution ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Fine Resolution ◽  
Modis Lai

AbstractNumerous validation efforts have been conducted over the last decade to assess the accuracy of global leaf area index (LAI) products. However, such efforts continue to face obstacles due to the lack of sufficient high-quality field measurements. In this study, a fine-resolution LAI dataset consisting of 80 reference maps was generated during 2003–2017. The direct destructive method was used to measure the field LAI, and fine-resolution LAI images were derived from Landsat images using semiempirical inversion models. Eighty reference LAI maps, each with an area of 3 km × 3 km and a percentage of cropland larger than 75%, were selected as the fine-resolution validation dataset. The uncertainty associated with the spatial scale effect was also provided. Ultimately, the fine-resolution reference LAI dataset was used to validate the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. The results indicate that the fine-resolution reference LAI dataset builds a bridge to link small sampling plots and coarse-resolution pixels, which is extremely important in validating coarse-resolution LAI products.

scholarly journals Long-Term Variation of Global GEOV2 and MODIS Leaf Area Index (LAI) and Their Uncertainties: An Insight into the Product Stabilities

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hongliang Fang ◽  
Yao Wang ◽  
Yinghui Zhang ◽  
Sijia Li
Keyword(s):  
Leaf Area Index ◽  
Product Quality ◽  
Leaf Area ◽  
Vegetation Change ◽  
Temporal Variations ◽  
Area Index ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Global Vegetation

Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.

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