scholarly journals OPERATIONAL 333m BIOPHYSICAL PRODUCTS OF THE COPERNICUS GLOBAL LAND SERVICE FOR AGRICULTURE MONITORING

2015 ◽  
Vol XL-7/W3 ◽  
pp. 53-56 ◽  
Author(s):  
R. Lacaze ◽  
B. Smets ◽  
F. Baret ◽  
M. Weiss ◽  
D. Ramon ◽  
...  
Keyword(s):  
Distribution System ◽  
Land Surface ◽  
Surface Albedo ◽  
Water Cycle ◽  
Vegetation Indices ◽  
Sensor Data ◽  
Current Status ◽  
Vegetation Dynamic ◽  
Area Index ◽  
Global Land

The Copernicus Global Land service provides continuously a set of bio-geophysical variables describing, over the whole globe, the vegetation dynamic, the energy budget at the continental surface and some components of the water cycle. These generic products serve numerous applications including agriculture and food security monitoring. The portfolio of the Copernicus Global Land service contains Essential Climate Variables like the Leaf Area Index (LAI), the Fraction of PAR absorbed by the vegetation (FAPAR), the surface albedo, the Land Surface Temperature, the soil moisture, the burnt areas, the areas of water bodies, and additional vegetation indices. They are generated every hour, every day or every 10 days on a reliable automatic basis from Earth Observation satellite data. Beside this timely production, the available historical archives have been processed, using the same innovative algorithms, to get consistent time series as long as possible. All products are accessible, free of charge after registration through FTP/HTTP (<a href="http://land.copernicus.eu/global/"target="_blank">http://land.copernicus.eu/global/</a>) and through the GEONETCast satellite distribution system. The evolution of the service towards the operations at 333m resolution is partly supported by the FP7/ImagineS project which focuses on the retrieval of LAI, FAPAR, fraction of vegetation cover and surface albedo from PROBA-V sensor data. The paper presents the innovations of the 333m biophysical products, make an overview of their current status, and introduce the next steps of the evolution of the Copernicus Global Land service.

scholarly journals Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

2017 ◽  
Author(s):  
Clément Albergel ◽  
Simon Munier ◽  
Delphine Jennifer Leroux ◽  
Hélène Dewaele ◽  
David Fairbairn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Gross Primary Production ◽  
Mediterranean Area ◽  
Surface Model ◽  
Strong Impact ◽  
Control Variables ◽  
Area Index ◽  
Model Control ◽  
Global Land

Abstract. In this study, a global Land Data Assimilation system (LDAS-Monde) is tested over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface Soil Moisture (SM) and Leaf Area Index (LAI) observations to constrain the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system. It makes use of the CO2-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. Surface SM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow-dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 cm to 100 cm depth). A sensitivity test of the Jacobians over 2000–2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and surface SM have an impact on the different control variables. From the assimilation of surface SM, the LDAS is more effective in modifying soil-moisture from the top layers of soil as model sensitivity to surface SM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 cm to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Assimilation impact shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. The assimilation impact's evaluation is successfully carried out using (i) agricultural statistics over France, (ii) river discharge observations, (iii) satellite-derived estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observations based estimates of up-scaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement.

Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance

2016 ◽  
Vol 54 (9) ◽  
pp. 5301-5318 ◽  
Author(s):  
Zhiqiang Xiao ◽  
Shunlin Liang ◽  
Jindi Wang ◽  
Yang Xiang ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Surface Reflectance ◽  
Area Index ◽  
Long Time Series ◽  
Global Land ◽  
Long Time

scholarly journals Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018

2019 ◽  
Vol 11 (6) ◽  
pp. 735 ◽  
Author(s):  
Moustapha Tall ◽  
Clément Albergel ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Françoise Guichard ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Burkina Faso ◽  
Land Surface ◽  
Gross Primary Production ◽  
Added Value ◽  
Surface Model ◽  
Strong Impact ◽  
Area Index ◽  
Soil Layers ◽  
Global Land

This study focuses on the ability of the global Land Data Assimilation System, LDAS-Monde, to improve the representation of land surface variables (LSVs) over Burkina-Faso through the joint assimilation of satellite derived surface soil moisture (SSM) and leaf area index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5 as well as ERA-Interim former reanalysis, leading to reanalyses of LSVs at 0.25° × 0.25° and 0.50° × 0.50° spatial resolution, respectively. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated with a simplified extended Kalman filter (SEKF) using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF based on in situ data. Results of four experiments are then compared: Open-loop simulation (i.e., no assimilation) and analysis (i.e., joint assimilation of SSM and LAI) forced by either ERA5 or ERA-Interim. After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below), as reflected by the structure of the SEKF Jacobians. The added value of using ERA5 reanalysis over ERA-Interim when used in LDAS-Monde is highlighted. The assimilation is able to improve the simulation of both SSM and LAI: The analyses add skill to both configurations, indicating the healthy behavior of LDAS-Monde. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina-Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project and sun-induced fluorescence from the Global Ozone Monitoring Experiment-2 (GOME-2) are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.

scholarly journals The Influence of Natural and Anthropogenic Forcing on Water and Energy Balance and on Photosynthesis

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1151
Author(s):  
Jaeyoung Song ◽  
Sungbo Shim ◽  
Ji-Sun Kim ◽  
Jae-Hee Lee ◽  
Young-Hwa Byun ◽  
...  
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Surface Processes ◽  
Net Radiation ◽  
Land Surface Processes ◽  
Surface Moisture ◽  
Detection And Attribution ◽  
Aerosol Emission ◽  
Global Land ◽  
Use Efficiency

Land surface processes are rarely studied in Detection and Attribution Model Inter-comparison Project (DAMIP) experiments on climate change. We analyzed a CMIP6 DAMIP historical experiment by using multi-linear regression (MLRM) and analysis of variance methods. We focused on energy and water budgets, including gross primary productivity (GPP). In MLRM, we estimated each forcing’s contribution and identified the role of natural forcing, which is usually ignored. Contributions of the forcing factors varied by region, and high-ranked variables such as net radiation could receive multiple influences. Greenhouse gases (GHG) accelerated energy and water cycles over the global land surface, including evapotranspiration, runoff, GPP, and water-use efficiency. Aerosol (AER) forcing displayed the opposite characteristics, and natural forcing accounted for short-term changes. A long-term analysis of total soil moisture and water budget indicated that as the AER increases, the available water on the global land increases continuously. In the recent past, an increase in net radiation (i.e., a lowered AER) reduced surface moisture and hastened surface water cycle (GHG effect). The results imply that aerosol emission and its counterbalance to GHG are essential to most land surface processes. The exception to this is GPP, which was overdominated by GHG effects.

scholarly journals Oil palm modelling in the global land-surface model ORCHIDEE-MICT

2020 ◽  
Author(s):  
Yidi Xu ◽  
Philippe Ciais ◽  
Le Yu ◽  
Wei Li ◽  
Xiuzhi Chen ◽  
...  
Keyword(s):  
Oil Palm ◽  
Land Surface ◽  
Carbon Allocation ◽  
Land Surface Model ◽  
Rapid Expansion ◽  
Surface Model ◽  
Ecological Processes ◽  
Area Index ◽  
Global Land ◽  
Improved Model

Abstract. Oil palm is the most productive oil crop that provides ~40 % of the global vegetable oil supply, with 7 % of the cultivated land devoted to oil plants. The rapid expansion of oil palm cultivation is seen as one of the major cause for deforestation emissions and threatens the conservation of rain forest and swamp areas and their associated ecosystem services in tropical areas. Given the importance of oil palm in oil production and its adverse environmental consequences, it is important to understand the physiological and phenological processes of oil palm and its impacts on the carbon, water and energy cycles. In most global vegetation models, oil palm is represented by generic plant functional types (PFT) without specific representation of its morphological, physical and physiological traits. This would cause biases in the subsequent simulations. In this study, we introduced a new specific PFT for oil palm in the global land surface model ORCHIDEE-MICT (v8.4.2). The specific morphology, phenology and harvest process of oil palm were implemented, and the plant carbon allocation scheme was modified to support the growth of branch, leaf and fruit component of each phytomer. A new age-specific parameterization scheme for photosynthesis, autotrophic respiration, and carbon allocation was also developed for the oil palm PFT, based on observed physiology, and was calibrated by observations. The improved model generally reproduces the leaf area index, biomass density and fruit yield during the life cycle at 14 observation sites. Photosynthesis, carbon allocation and biomass components for oil palm also agree well with observations. This explicit representation of oil palm in global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.

scholarly journals The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

2020 ◽  
Vol 12 (12) ◽  
pp. 1979
Author(s):  
Dandan Xu ◽  
Deshuai An ◽  
Xulin Guo
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Area Index ◽  
Plant Area Index ◽  
The Impact

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

Effect of forest canopy structure on wintertime Land Surface Albedo: Comparing CLM5 simulations to in-situ observations

2020 ◽  
Author(s):  
Johanna Malle ◽  
Nick Rutter ◽  
Clare Webster ◽  
Giulia Mazzotti ◽  
Leanne Wake ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Surface Albedo ◽  
Canopy Structure ◽  
Surface Energy Budget ◽  
Area Index ◽  
Albedo Feedback ◽  
Snow Albedo ◽  
Snow Cover Extent ◽  
Wide Range

&lt;p&gt;Seasonal snow massively impacts the surface energy budget through its high reflectivity and is therefore an important component of land-atmosphere models. It affects climate through Snow Albedo Feedback (SAF), a positive feedback mechanism between a reduced snow cover extent due to climate warming and the corresponding increase of shortwave absorption, which provokes a further reduction in snow cover extent. SAF has been shown to be the largest climate feedback over the extratropical Northern Hemisphere (NH) during the snow melt period. Yet, large biases in SAF projections are linked to snow-vegetation interactions.&lt;/p&gt;&lt;p&gt;This study aims at investigating uncertainties associated with the representation of wintertime Land Surface Albedo (LSA) of forested environments in global climate models, which is an essential aspect when studying SAF. UAV-based observations of LSA were used to assess corresponding LSA simulations in CLM5, the land component of the NCAR Community Earth System Model. Our measurements capture a wide range of forest structure and species found in seasonally snow covered environments, spanning from Swiss sub-alpine to Finnish boreal forests, and show a strong dependency of LSA on solar angle and canopy density. CLM5 simulations failed to capture a realistic range in LSA and shortcomings were identified particularly with regards to simulations at sparsely forested sites. In these environments, Leaf Area Index as the main descriptor of canopy structure was unable to explain observed LSA differences in space and time. This study emphasizes the need to improve the representation of canopy structure in land surface models with critical implications for simulations of Snow Albedo Feedback strength over the NH extratropics.&lt;/p&gt;

Validation of Clear-Sky Global LAnd Surface Satellite (GLASS) Longwave Radiation Product

2020 ◽  
Author(s):  
Qi Zeng ◽  
Jie Cheng ◽  
Feng Yang
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Radiant Energy ◽  
Remote Sensing Data ◽  
Longwave Radiation ◽  
Energy System ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Clear Sky ◽  
Global Land

&lt;p&gt;Surface longwave (LW) radiation plays an important rolein global climatic change, which is consist of surface longwave upward radiation (LWUP), surface longwave downward radiation (LWDN) and surface longwave net radiation (LWNR). Numerous studies have been carried out to estimate LWUP or LWDN from remote sensing data, and several satellite LW radiation products have been released, such as the International Satellite Cloud Climatology Project&amp;#8208;Flux Data (ISCCP&amp;#8208;FD), the Global Energy and Water cycle Experiment&amp;#8208;Surface Radiation Budget (GEWEX&amp;#8208;SRB) and the Clouds and the Earth&amp;#8217;s Radiant Energy System&amp;#8208;Gridded Radiative Fluxes and Clouds (CERES&amp;#8208;FSW). But these products share the common features of coarse spatial resolutions (100-280 km) and lower validation accuracy.&lt;/p&gt;&lt;p&gt;Under such circumstance, we developed the methods of estimating long-term high spatial resolution all sky&amp;#160; instantaneous LW radiation, and produced the corresponding products from MODIS data from 2000 through 2018 (Terra and Aqua), named as Global LAnd Surface Satellite (GLASS) Longwave Radiation product, which can be free freely downloaded from the website (http://glass.umd.edu/Download.html).&lt;/p&gt;&lt;p&gt;In this article, ground measurements collected from 141 sites in six independent networks (AmerciFlux, AsiaFlux, BSRN, CEOP, HiWATER-MUSOEXE and TIPEX-III) are used to evaluate the clear-sky GLASS LW radiation products at global scale. The bias and RMSE is -4.33 W/m&lt;sup&gt;2 &lt;/sup&gt;and 18.15 W/m&lt;sup&gt;2 &lt;/sup&gt;for LWUP, -3.77 W/m&lt;sup&gt;2 &lt;/sup&gt;and 26.94 W/m&lt;sup&gt;2&lt;/sup&gt; for LWDN, and 0.70 W/m&lt;sup&gt;2 &lt;/sup&gt;and 26.70 W/m&lt;sup&gt;2&lt;/sup&gt; for LWNR, respectively. Compared with validation results of the above mentioned three LW radiation products, the overall accuracy of GLASS LW radiation product is much better. We will continue to improve the retrieval algorithms and update the products accordingly.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document