| 61. |
- Ghent, D., et al.
(författare)
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Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter
- 2010
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Ingår i: Journal of Geophysical Research: Atmospheres. - 2169-8996. ; 115:D19, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- Land surface models have uncertainties due to their approximation of physical processes and the heterogeneity of the land surface. These can be compounded when key variables are inadequately represented. Land surface temperature (LST) is critical as it forms an integral component in the surface energy budget, water stress evaluation, fuel moisture derivation, and soil moisture-climate feedbacks. A reduction in the uncertainty of surface energy fluxes, and moisture quantification, is assumed to be achievable by constraining simulations of LST with observation data. This technique is known as data assimilation and involves the adjustment of the model state at observation times with measurements of a predictable uncertainty. In this paper, the validity of LST simulations in a regionalized parameterization of the land surface model Joint UK Land Environment Simulator (JULES) for Africa is assessed by way of a multitemporal intercomparison study with the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Along Track Scanning Radiometer (AATSR), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) thermal products, with a two-thirds reduction in model bias found when soil properties are reparameterized. A data assimilation experiment of SEVIRI LST into the JULES model via an ensemble Kalman filter shows an improvement in the modeled LST, soil moisture, and latent and sensible heat fluxes. This paper presents the first investigation into reducing the uncertainty in modeling energy and water fluxes with the United Kingdom's most important land surface model, JULES, by means of data assimilation of LST.
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| 62. |
- Ghilain, Nicolas, et al.
(författare)
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A new retrieval algorithm for soil moisture index from thermal infrared sensor on-board geostationary satellites over Europe and Africa and its validation
- 2019
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 11:17
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Tidskriftsartikel (refereegranskat)abstract
- Monitoring soil moisture at the Earth'surface is of great importance for drought early warnings. Spaceborne remote sensing is a keystone in monitoring at continental scale, as satellites can make observations of locations which are scarcely monitored by ground-based techniques. In recent years, several soil moisture products for continental scale monitoring became available from the main space agencies around the world. Making use of sensors aboard polar satellites sampling in the microwave spectrum, soil moisture can be measured and mapped globally every few days at a spatial resolution as fine as 25 km. However, complementarity of satellite observations is a crucial issue to improve the quality of the estimations provided. In this context, measurements within the visible and infrared from geostationary satellites provide information on the surface from a totally different perspective. In this study, we design a new retrieval algorithm for daily soil moisture monitoring based only on the land surface temperature observations derived from the METEOSAT second generation geostationary satellites. Soil moisture has been retrieved from the retrieval algorithm for an eight years period over Europe and Africa at the SEVIRI sensor spatial resolution (3 km at the sub-satellite point). The results, only available for clear sky and partly cloudy conditions, are for the first time extensively evaluated against in-situ observations provided by the International Soil Moisture Network and FLUXNET at sites across Europe and Africa. The soil moisture retrievals have approximately the same accuracy as the soil moisture products derived from microwave sensors, with the most accurate estimations for semi-arid regions of Europe and Africa, and a progressive degradation of the accuracy towards northern latitudes of Europe. Although some possible improvements can be expected by a better use of other products derived from SEVIRI, the new approach developped and assessed here is a valuable alternative to microwave sensors to monitor daily soil moisture at the resolution of few kilometers over entire continents and could reveal a good complementarity to an improved monitoring system, as the algorithm can produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy conditions (over 10% of the time).
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| 63. |
- Ghilain, N., et al.
(författare)
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Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite
- 2012
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1607-7938. ; 16:8, s. 2567-2583
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Tidskriftsartikel (refereegranskat)abstract
- Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG) and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF) are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I), showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual) variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) products for evapotranspiration monitoring with a land surface model at 3-5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north-south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land surface temperature shows an improvement of the evapotranspiration simulations.
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| 64. |
- Hickler, Thomas, et al.
(författare)
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Precipitation controls Sahel greening trend
- 2005
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Ingår i: Geophysical Research Letters. - 1944-8007. ; 32:21
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Tidskriftsartikel (refereegranskat)abstract
- The Sahel region has been identified as a "hot spot'' of global environmental change, but understanding of the roles of different climatic and anthropogenic forcing factors driving change in the region is incomplete. We show that a process-based ecosystem model driven by climatic and atmospheric CO2 data alone closely reproduces the satellite-observed greening trend of the Sahel vegetation and its interannual variability between 1982 and 1998. Changes in precipitation were identified as the primary driver of the aggregated simulated vegetation changes. According to the model, the increasing carbon uptake through vegetation was associated with an increasing relative carbon sink; but integrated over the whole period, the Sahel was predicted to be a net source of carbon.
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| 65. |
- Ichii, Kazuhito, et al.
(författare)
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New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression
- 2017
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Ingår i: Journal of Geophysical Research - Biogeosciences. - 2169-8953. ; 122:4, s. 767-795
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Tidskriftsartikel (refereegranskat)abstract
- The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial CO2 fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated CO2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8days are reproduced (e.g., r2=0.73 and 0.42 for 8day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r2=1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land CO2 fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO2 fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO2 fluxes. These data-driven estimates can provide a new opportunity to assess CO2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models.
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| 66. |
- Jamali, Sadegh, et al.
(författare)
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Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel
- 2014
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257. ; 141, s. 79-89
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Tidskriftsartikel (refereegranskat)abstract
- Over the last few decades, increasing rates of change in the structure and function of ecosystems have been brought about by human modification of land cover, of which a major component is vegetation. Metrics derived from linear regression models applied to high temporal resolution satellite data are commonly used to estimate rates of vegetation change. This approach implicitly assumes that vegetation changes gradually and linearly, which may not always be the case. In order to account for non-linear change in annual observations of vegetation from satellites, we test and apply a polynomial fitting-based scheme to annual GIMMS (Global Inventory Modeling and Mapping Studies)–NDVI (Normalized Difference Vegetation Index) observations for North Africa (including the Sahel) for the period 1982–2006. The scheme divides vegetation change into cubic, quadratic, linear, and “concealed” trend behaviors, the latter indicating that while no net change in vegetation amount has occurred over the period, the curve exhibits at least one minimum or/and maximum indicating that the vegetation has undergone change during the elapsed time period. Our results show that just over half the study area (51.9%) exhibit trends that are statistically significant, with a dominance of positive linear trends (22.2%) that are distributed in an east-west band across the Sahel, thus confirming previous studies. Non-linear trends occur much less frequently and are more widely scattered. Nevertheless, they tend to cluster within or on the outskirts of zones of linear trend, underscoring their importance for detecting anomalous change features. We also show that the ratio of linear vs. non-linear trends tends to be associated with different land cover types/land cover change estimates, many of which reflect biome-level controls on vegetation dynamics. However, more local drivers related to direct human impact, such as urbanization, cannot be ruled out. Our change detection approach retains the more complex signatures embedded in long-term time series by preserving details about change rates, therefore allowing for a more subtle interpretation of change trajectories on a case-by-case basis. The fitting method is entirely automated and does not require the judicious selection of thresholds. However, while polynomials can give a better fit, they like linear models are based on assumptions, and may sometimes lead to oversimplification or miss short-term variations. Our method can help to contribute more accurate information to one of the major goals of the burgeoning field of land change science, namely to observe and monitor land changes underway throughout the world.
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| 67. |
- Jamali, Sadegh, et al.
(författare)
-
Comparing parametric and non-parametric approaches for estimating trends in multi-year NDVI
- 2012
-
Konferensbidrag (refereegranskat)abstract
- The aim of this study is to systematically compare parametric and non-parametric techniques for analyzing trends in annual NDVI derived from NOAA AVHRR sensor in order to examine how trend type and departure from normality assumptions affect the accuracy of detecting long-term change. To generate annual data, the mean NDVI of a four-month long ‘green’ season was computed for fifteen sites (located in Africa, Spain, Italy, Sweden, and Iraq) from the GIMMS product for the periods 1982-2006. Trends in these time series were then estimated by Ordinary Least-Squares (OLS) regression (parametric) and the combined Mann-Kendall test with Theil-Sen slope estimator (non-parametric), and compared using slope value and statistical significance measures. We also estimated optimal polynomial model for the annual NDVI, by using Akaike Information Criterion (AIC), to determine the trend type at each site. Results indicate that slopes and their statistical significances obtained from the two approaches at sites with low degree polynomials (mostly linear) and steep monotonic (gradually increasing or decreasing) trends compare favourably with one another. At sites with weak linear slopes, the two approaches had similar results as well. Exceptions include sites with abrupt step-like changes resulting in departures from linearity and consequently high degree polynomials where the least-squares method outperformed the Mann-Kendall Theil-Sen method. In sum, we conclude that OLS is superior for detecting NDVI trends using annual data though further investigation using other techniques is recommended.
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| 68. |
- Jamali, Sadegh, et al.
(författare)
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Detecting changes in vegetation trends using time series segmentation
- 2015
-
Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 156:January, s. 182-195
-
Tidskriftsartikel (refereegranskat)abstract
- Although satellite-based sensors have made vegetation data series available for several decades, the detection of vegetation trend and change is not yet straightforward. This is partly due to the scarcity of available change detection algorithms suitable for identifying and characterizing both abrupt and non-abrupt changes, without sacrificing accuracy or computational speed. We propose a user-friendly program for analysing vegetation time series, with two main application domains: generalising vegetation trends to main features, and characterizing vegetation trend changes. This program, Detecting Breakpoints and Estimating Segments in Trend (DBEST) uses a novel segmentation algorithm which simplifies the trend into linear segments using one of three user-defined parameters: a generalisation-threshold parameter δ, the m largest changes, or a threshold β for the magnitude of changes of interest for detection. The outputs of DBEST are the simplified trend, the change type (abrupt or non-abrupt), and estimates for the characteristics (time and magnitude) of the change. DBEST was tested and evaluated using simulated Normalized Difference Vegetation Index (NDVI) data at two sites, which included different types of changes. Evaluation results demonstrate that DBEST quickly and robustly detects both abrupt and non-abrupt changes, and accurately estimates change time and magnitude. DBEST was also tested using data from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI image time series for Iraq for the period 1982–2006, and was able to detect and quantify major change over the area. This showed that DBEST is able to detect and characterize changes over large areas. We conclude that DBEST is a fast, accurate and flexible tool for trend detection, and is applicable to global change studies using time series of remotely sensed data sets.
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| 69. |
- Jamali, Sadegh, et al.
(författare)
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Investigating temporal relationships between rainfall, soil moisture and MODIS-derived NDVI and EVI for six sites in Africa
- 2011
-
Konferensbidrag (refereegranskat)abstract
- This study investigates temporal relationships between vegetation growth, rainfall, and soil moisture for six sites located in sub-Saharan and southern Africa for the period 2005-2009. Specifically, seasonal components of time series of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) composites from the Moderate Resolution Imaging Spectroradiometer (MODIS) and half-hourly in-situ rainfall and soil moisture data at different depths (5-200 cm) during the growing season were used in a lagged correlation analysis in order to understand how vegetation growth responds to rainfall and soil moisture across different sites. Results indicate that both vegetation indices are strongly related to soil moisture (EVI slightly stronger than NDVI) for the upper 1 m reaching maximum correlations when they lag soil moisture by 0-28 days. They respond to rainfall with a 24-32 day lag at the sub-Saharan sites, EVI slightly earlier than NDVI, but their response at the southern hemisphere sites is complex.
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| 70. |
- Johnston, Alice S.A., et al.
(författare)
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Temperature thresholds of ecosystem respiration at a global scale
- 2021
-
Ingår i: Nature Ecology and Evolution. - : Springer Science and Business Media LLC. - 2397-334X. ; 5:4, s. 487-494
-
Tidskriftsartikel (refereegranskat)abstract
- Ecosystem respiration is a major component of the global terrestrial carbon cycle and is strongly influenced by temperature. The global extent of the temperature–ecosystem respiration relationship, however, has not been fully explored. Here, we test linear and threshold models of ecosystem respiration across 210 globally distributed eddy covariance sites over an extensive temperature range. We find thresholds to the global temperature–ecosystem respiration relationship at high and low air temperatures and mid soil temperatures, which represent transitions in the temperature dependence and sensitivity of ecosystem respiration. Annual ecosystem respiration rates show a markedly reduced temperature dependence and sensitivity compared to half-hourly rates, and a single mid-temperature threshold for both air and soil temperature. Our study indicates a distinction in the influence of environmental factors, including temperature, on ecosystem respiration between latitudinal and climate gradients at short (half-hourly) and long (annual) timescales. Such climatological differences in the temperature sensitivity of ecosystem respiration have important consequences for the terrestrial net carbon sink under ongoing climate change.
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