scholarly journals Fractional Vegetation Cover Estimation Algorithm for FY-3B Reflectance Data Based on Random Forest Regression Method

2021 ◽  
Vol 13 (11) ◽  
pp. 2165
Author(s):  
Duanyang Liu ◽  
Kun Jia ◽  
Haiying Jiang ◽  
Mu Xia ◽  
Guofeng Tao ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Land Surface ◽  
Climate Models ◽  
Estimation Algorithm ◽  
Global Scale ◽  
Fractional Vegetation Cover ◽  
The Earth ◽  
Temporal Validation ◽  
Reliability And Robustness ◽  
Reflectance Data

As an important land surface vegetation parameter, fractional vegetation cover (FVC) has been widely used in many Earth system ecological and climate models. In particular, high-quality and reliable FVC products on the global scale are important for the Earth surface process simulation and global change studies. Recently, the FengYun-3 (FY-3) series satellites, which are the second generation of Chinese meteorological satellites, launched with the polar orbit and provide continuous land surface observations on a global scale. However, there is rare studying on the FVC estimation using FY-3 reflectance data. Therefore, the FY-3B reflectance data were selected as the representative data to develop a FVC estimation algorithm in this study, which would investigate the capability of the FY-3 reflectance data on the global FVC estimation. The spatial–temporal validation over the regional area indicated that the FVC estimations generated by the proposed algorithm had reliable continuities. Furthermore, a satisfactory accuracy performance (R2 = 0.7336, RMSE = 0.1288) was achieved for the proposed algorithm based on the Earth Observation LABoratory (EOLAB) reference FVC data, which provided further evidence on the reliability and robustness of the proposed algorithm. All these results indicated that the FY-3 reflectance data were capable of generating a FVC estimation with reliable spatial–temporal continuities and accuracy.

scholarly journals Global Fractional Vegetation Cover Estimation Algorithm for VIIRS Reflectance Data Based on Machine Learning Methods

2018 ◽  
Vol 10 (10) ◽  
pp. 1648 ◽  
Author(s):  
Duanyang Liu ◽  
Linqing Yang ◽  
Kun Jia ◽  
Shunlin Liang ◽  
Zhiqiang Xiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vegetation Cover ◽  
Estimation Algorithm ◽  
Multivariate Adaptive Regression Splines ◽  
Estimation Accuracy ◽  
Surface Reflectance ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Fractional Vegetation Cover ◽  
Reflectance Data

Fractional vegetation cover (FVC) is an essential input parameter for many environmental and ecological models. Recently, several global FVC products have been generated using remote sensing data. The Global LAnd Surface Satellite (GLASS) FVC product, which is generated from Moderate Resolution Imaging Spectroradiometer (MODIS) data, has attained acceptable performance. However, the original MODIS operation design lifespan has been exceeded. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) satellite was designed to be the MODIS successor. Therefore, developing an FVC estimation algorithm for VIIRS data is important for maintaining continuous FVC estimates in case of MODIS failure. In this study, a global FVC estimation algorithm for VIIRS surface reflectance data was proposed based on machine learning methods, which investigated the performances of back propagating neural networks (BPNNs), general regression networks (GRNNs), multivariate adaptive regression splines (MARS), and Gaussian process regression (GPR). The training samples were extracted from the GLASS FVC product and corresponding reconstructed VIIRS surface reflectance in 2013 over the global sampling locations. The VIIRS reflectances of red and near infrared (NIR) bands were the input variables for these machine learning methods. The theoretical performances and independent validation results indicated that the four machine learning methods could achieve similar and reliable FVC estimates. Regarding the FVC estimation accuracy, the GPR method achieved the best performance (R2 = 0.9019, RMSE = 0.0887). The MARS method had the obvious advantage of computational efficiency. Furthermore, the FVC estimates achieved good spatial and temporal continuities. Therefore, the proposed FVC estimation algorithm for VIIRS data can potentially generate reliable global FVC data for related applications.

scholarly journals Spatiotemporal Comparison and Validation of Three Global-Scale Fractional Vegetation Cover Products

2019 ◽  
Vol 11 (21) ◽  
pp. 2524 ◽  
Author(s):  
Duanyang Liu ◽  
Kun Jia ◽  
Xiangqin Wei ◽  
Mu Xia ◽  
Xiwang Zhang ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Land Surface ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Glass Product ◽  
Fractional Vegetation Cover ◽  
Temporal Profiles

Fractional vegetation cover (FVC) is an important parameter for many environmental and ecological models. Large-scale and long-term FVC products are critical for various applications. Currently, several global-scale FVC products have been generated with remote sensing data, such as VGT bioGEOphysical product Version 2 (GEOV2), PROBA-V bioGEOphysical product Version 3 (GEOV3) and Global LAnd Surface Satellite (GLASS) FVC products. However, studies comparing and validating these global-scale FVC products are rare. Therefore, in this study, the performances of three global-scale time series FVC products, including the GEOV2, GEOV3, and GLASS FVC products, are investigated to assess their spatial and temporal consistencies. Furthermore, reference FVC data generated from high-spatial-resolution data are used to directly evaluate the accuracy of these FVC products. The results show that these three FVC products achieve general agreements in terms of spatiotemporal consistencies over most regions. In addition, the GLASS and GEOV2 FVC products have reliable spatial and temporal completeness, whereas the GEOV3 FVC product contains much missing data over high-latitude regions, especially during wintertime. Furthermore, the GEOV3 FVC product presents higher FVC values than GEOV2 and GLASS FVC products over the equator. The main differences between the GEOV2 and GLASS FVC products occur over deciduous forests, for which the GLASS product presents slightly higher FVC values than the GEOV2 product during wintertime. Finally, temporal profiles of the GEOV2 and GLASS FVC products show better consistency than the GEOV3 FVC product, and the GLASS FVC product presents more reliable accuracy (R2 = 0.7878, RMSE = 0.1212) compared with the GEOV2 (R2 = 0.5798, RMSE = 0.1921) and GEOV3 (R2 = 0.7744, RMSE = 0.2224) FVC products over these reference FVC data.

scholarly journals Generating High Spatio-Temporal Resolution Fractional Vegetation Cover by Fusing GF-1 WFV and MODIS Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2324 ◽  
Author(s):  
Tao ◽  
Jia ◽  
Zhao ◽  
Wei ◽  
Xie ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Vegetation Cover ◽  
Field Survey ◽  
Lower Probability ◽  
Important Indicator ◽  
The Real ◽  
Modis Data ◽  
Fractional Vegetation Cover ◽  
Spatio Temporal ◽  
Reflectance Data

As an important indicator to characterize the surface vegetation, fractional vegetation cover (FVC) with high spatio-temporal resolution is essential for earth surface process simulation. However, due to technical limitations and the influence of weather, it is difficult to generate temporally continuous FVC with high spatio-temporal resolution based on a single remote-sensing data source. Therefore, the objective of this study is to explore the feasibility of generating high spatio-temporal resolution FVC based on the fusion of GaoFen-1 Wide Field View (GF-1 WFV) data and Moderate-resolution Imaging Spectroradiometer (MODIS) data. Two fusion strategies were employed to identify a suitable fusion method: (i) fusing reflectance data from GF-1 WFV and MODIS firstly and then estimating FVC from the reflectance fusion result (strategy FC, Fusion_then_FVC). (ii) fusing the FVC estimated from GF-1 WFV and MODIS reflectance data directly (strategy CF, FVC_then_Fusion). The FVC generated using strategies FC and CF were evaluated based on FVC estimated from the real GF-1 WFV data and the field survey FVC, respectively. The results indicated that strategy CF achieved higher accuracies with less computational cost than those of strategy FC both in the comparisons with FVC estimated from the real GF-1 WFV (CF:R2 = 0.9580, RMSE = 0.0576; FC: R2 = 0.9345, RMSE = 0.0719) and the field survey FVC data (CF: R2 = 0.8138, RMSE = 0.0985; FC: R2 = 0.7173, RMSE = 0.1214). Strategy CF preserved spatial details more accurately than strategy FC and had a lower probability of generating abnormal values. It could be concluded that fusing GF-1 WFV and MODIS data for generating high spatio-temporal resolution FVC with good quality was feasible, and strategy CF was more suitable for generating FVC given its advantages in estimation accuracy and computational efficiency.

Simulating riverine nutrient transport on global scale

2020 ◽  
Author(s):  
Tobias Stacke ◽  
Stefan Hagemann ◽  
Gibran Romero-Mujalli ◽  
Jens Hartmann ◽  
Helmuth Thomas
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
River Flow ◽  
Climate Change Impacts ◽  
Global Scale ◽  
Nutrient Loads ◽  
Input Dataset ◽  
Ocean Models ◽  
Energy And Momentum

<p>The currently ongoing CMIP6 simulations feature Earth System Models with interactively coupled components for atmosphere, ocean and land surface. Water, energy and momentum between these components are exchanged conservatively. This is crucial to compute climate interactions and their feedbacks consistently. Currently, the representation of biogeochemical cycles in land surface and ocean models is advancing including not only a carbon cycle but also processes based on nutrients like nitrogen or phosphorus. Some land surface models (LSM) already compute leaching of such constituents from the soil, and some ocean models (OM) consider nutrient influx from the land for a number of processes, e.g. biological activity. However, the OMs usually use observed data as input instead of the nutrient loads computed by the LSMs. This setup cannot represent the effects of climate or land use change on nutrient availability and therefore limits the applications of ESMs in respect to climate change impacts.</p><p>For this reason, we are extending our hydrological discharge model, the HDM, to not only transport water but also other constituents. The HDM is an established component of regional (GCOAST, ESM ROM, Reg-CM-ES) as well as global (MPI-ESM) climate models but also applicable as stand-alone model. In a first step, only inert transport is simulated without considering any chemical reactions or biological transformation during river flow. The transport is realized using the same linear cascade infrastructure as used for water transport. However, a successful offline validation of these new features does not only require a realistic routing scheme and consequently the representation of the most important reactions during transport, but also the generation of sensible input data either from large scale models or from observations. In our presentation, we will outline the state of this work together with the compiled input dataset.</p>

scholarly journals La représentation des surfaces continentales dans la modélisation du climat à Météo-France

2020 ◽  
pp. 067
Author(s):  
Bertrand Decharme ◽  
Christine Delire ◽  
Aaron Boone
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Climate Modelling ◽  
History Of ◽  
International Climate ◽  
Land Surfaces ◽  
Computational Resources

Les surfaces continentales jouent un rôle non négligeable dans le système climatique de la Terre. Elles occupent d'ailleurs une place majeure dans les cycles globaux de l'eau et du carbone. Elles ont été prises en compte dès les premiers modèles numériques de climat et, avec l'évolution des connaissances, des capacités de calcul et de la demande sociétale, leur représentation s'est aujourd'hui considérablement complexifiée. Nous présentons ici une brève histoire de l'évolution du modèle de surfaces Isba (Interactions sol-biosphère-atmosphère) de Météo-France dans son utilisation à l'échelle du globe en la replaçant dans le contexte international de la modélisation climatique. Land surfaces play a significant role in the Earth climate system, and they are a major component of the global carbon and water cycles. The first numerical climate models took them into account in very simple ways. Through time the complexity of their representation has increased a lot owing to improved knowledge, larger computational resources and changing societal demands. We present here a brief history of the ISBA (Interactions Soil-Biosphere-Atmosphere) land surface model developed at Météo-France when used at the global scale and how it evolved in the context of international climate modelling.

scholarly journals A biologically driven directional change in susceptibility to global-scale glaciation during the Precambrian-Cambrian transition

2018 ◽  
Author(s):  
Richard A. Boyle ◽  
Carolin R. Löscher
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Abiotic Factors ◽  
Global Scale ◽  
Snowball Earth ◽  
Critical Aspect ◽  
Geological Evidence ◽  
Directional Change ◽  
Temperature Window ◽  
Tectonic Boundary

Integrated geological evidence suggests that grounded ice sheets occurred at sea level across all latitudes during two intervals within the Neoproterozoic era; the “snowball Earth” (SBE) events. Glacial events at ~730 and ~650 million years ago (Ma) were probably followed by a less severe but nonetheless global-scale glaciation at ~580Ma, immediately preceding the proliferation of the first fossils exhibiting unambiguous animal-like form. Existing modelling identifies weathering-induced CO2 draw-down as a critical aspect of glacial inception, but ultimately attributes the SBE phenomenon to unusual tectonic boundary conditions. Here we suggest that the evident directional decrease in Earth’s susceptibility to a SBE suggests that such a-directional abiotic factors are an insufficient explanation for the lack of SBE events since ~580 Ma. Instead we hypothesize that the terrestrial biosphere’s capacity to sustain a given level of biotic weathering-enhancement under suboptimal/declining temperatures, itself decreased over time: because lichens (with a relatively robust tolerance of sub-optimal temperatures) were gradually displaced on the land surface by more complex photosynthetic life (with a narrower temperature window for growth). We use a simple modelling exercise to highlight the critical (but neglected) importance of the temperature sensitivity of the biotic weathering enhancement factor and discuss the likely values of key parameters in relation to both experiments and the results of complex climate models. We show how the terrestrial biosphere’s capacity to sustain a given level of silicate-weathering-induced CO2 draw-down is critical to the temperature/greenhouse forcing at which SBE initiation is conceivable. We do not dispute the importance of low degassing rate and other tectonic factors, but propose that the unique feature of the Neoproterozoic was biology’s capacity to tip the system over the edge into a runaway ice-albedo feedback; compensating for the self-limiting decline in weathering rate during the temperature decrease on the approach to glaciation. Such compensation was more significant in the Neoproterozoic than the Phanerozoic due, ultimately, to changes in the species composition of the weathering interface over the course of evolutionary time.

scholarly journals Estimation of Land Surface Temperature Using LANDSAT 8 Satellite Data of Panchkula District, Haryana

2020 ◽  
pp. 47-66
Author(s):  
Ravi Kumar ◽  
Anup Kumar
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Global Scale ◽  
Sensor Data ◽  
Landsat 8 ◽  
Heat Island ◽  
The Earth ◽  
Satellite Sensor Data

Land surface temperature (LST) represents hotness of the surface of the Earth at a particular location. Land surface temperature is useful for meteorological, climatological changes, heat island, agriculture, hydrological processes at local, regional and global scale. Presently many satellite sensor data are available for calculation of land surface temperature like Landsat 8 and MODIS. In the present study land surface temperature in Panchkula district of Haryana have been calculated using Landsat 8 satellite data of 5th May 2019 and 28th October 2019. Already available equations were used for computation of LST in the study area. LST in the study area varies from 18°C to 56°C. High LST is observed in cultivation land, urban area while low LST is observed in hilly forest area in the study area. In the study validation of LST could not be done because of not available of temperature data of studied dates, however, the result gives idea of land surface temperature on a particular day and location.

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