Search

Your search keyword '"Mengjia Wang"' showing total 13 results
Start Over Author "Mengjia Wang" Topic 0105 earth and related environmental sciences
13 results on '"Mengjia Wang"'

1. New Global MuSyQ GPP/NPP Remote Sensing Products From 1981 to 2018

Author

Rui Sun, Juanmin Wang, Anran Zhu, Tao Yu, Helin Zhang, Mengjia Wang, Zhiqiang Xiao, and Kunlun Xiang

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Primary production, 02 engineering and technology, Vegetation, Evergreen, Atmospheric sciences, 01 natural sciences, FluxNet, Deforestation, Photosynthetically active radiation, Environmental science, Computers in Earth Sciences, Leaf area index, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Tropical rainforest
Abstract

Long time series of vegetation productivity products are significant for the research of global carbon cycle and climate change. In this article, the 0.05° global gross primary productivity (GPP) and net primary productivity (NPP) products from 1981 to 2018 were estimated by using the improved multisource data synergized quantitative (MuSyQ) NPP algorithm. The model was based on the fraction of absorbed photosynthetically active radiation (FPAR) and leaf area index (LAI) data from the global land surface satellite (GLASS) dataset, the light use efficiency (LUE) from the parameterization approach with the clearness index (CI), the ERA-Interim meteorological data, and other environmental factors. The results suggested that the accuracy of the MuSyQ GPP product was slightly higher than that of the MOD17 GPP product when compared with the FLUXNET GPP, especially for the evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), wetland (WET), cropland (CRO), woody savanna (WSAV), and closed shrubland (CSH) land types. MuSyQ NPP product also has higher accuracy [ R 2 = 0.81, RMSE = 214.6 gC/(m2year)] than MOD17 NPP [ R 2 = 0.55, RMSE = 214.7 gC/(m2year)] when compared with the BigFoot NPP, which indicated the reliability of the improved MuSyQ-NPP algorithm in estimating global NPP. Our results showed a significant upward trend in global NPP, which was most affected by FPAR, followed by LUE, temperature, and PAR. The average NPP declined significantly in Asia and Amazon tropical rainforests and increased significantly in Africa tropical rainforest, which were affected by the local deforestation or the forest expansion, and also the climate factors.

Published
2021
Full Text
View/download PDF

2. A consistent record of vegetation optical depth retrieved from the AMSR-E and AMSR2 X-band observations

Author

Xiaojun Li, Lei Fan, Hongliang Ma, Jean-Pierre Wigneron, Philippe Ciais, Christophe Moisy, Shengli Tao, Frédéric Frappart, Xiangzhuo Liu, Linna Chai, Mengjia Wang, Rui Sun, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), State Key Laboratory of Remote Sensing Science [Beijing], Beijing Normal University (BNU), Southwest University [Chongqing], Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Evolution et Diversité Biologique (EDB), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)

Subjects
010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Management, Monitoring, Policy and Law, 01 natural sciences, Normalized Difference Vegetation Index, 03 medical and health sciences, Calibration, AGB, Vegetation optical depth, Computers in Earth Sciences, Leaf area index, Proxy (statistics), 030304 developmental biology, 0105 earth and related environmental sciences, Earth-Surface Processes, Remote sensing, 0303 health sciences, Global and Planetary Change, Radiometer, AMSR-E, 15. Life on land, Snow, Data set, IB X-VOD, 13. Climate action, Brightness temperature, Environmental science, AMSR2
Abstract

International audience; Vegetation optical depth (VOD) retrieved from microwave remote sensing techniques has been used as an important proxy for monitoring the vegetation dynamics at large scales. In a first study we developed a new X-VOD indice from inversion of the X-MEB model from the Microwave Scanning Radiometer 2 (AMSR2, 2012-present) observations. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E, 2002–2011) and AMSR2 share many characteristics, such as local crossing time, incidence angle, frequency at X-band, etc, which makes it possible to produce a consistent long-term X-VOD product over a long period spanning from 2002 to present. The objective of this study was to merge the AMSR-E and AMSR2 observations in order, in a second step, to produce a consistent long-term X-VOD product spanning over the combined periods of AMSR-E and AMSR2.The main challenges in retrieving a consistent X-VOD data set from AMSR-E and AMSR2 are that (i) there is a bias between observations from the two sensors; and (ii) the lack of overlapping observations between the two sensors makes it impossible to achieve a direct inter-calibration. Here, to overcome this problem, we used AMSR-E slow rotation data (AMSR-E L1S), which has similar characteristics as AMSR-E and provided observations concurrently with AMSR2, as a bridge to calibrate the AMSR-E brightness temperature (TB) observations with AMSR2 TB.As our main objective in this TB calibration study was to produce a consistent long-term AMSR-E/AMSR2 X-VOD product, we evaluated the retrieved X-VOD product based on different inter-calibration, either global-based or pixel-based, methods. Both AMSR-E and AMSR2 X-VOD were evaluated against the Aboveground Biomass (AGB), Leaf Area Index (LAI) and the Normalized Difference Vegetation Index (NDVI). The results suggest that global-based inter-calibration methods using homogeneous and temporally stable land covers (Evergreen Broadleaf Forests and Snow and Ice) as reference calibration data sets provided the best results. For instance, the spatial relationships between X-VOD and AGB/LAI/NDVI are highly consistent over the AMSR-E and AMSR2 periods after the calibration work. This study laid a solid foundation for monitoring the dynamics of X-VOD, as a proxy of AGB, over the combined periods of AMSR-E and AMSR2 sensors (almost 20 years).

Published
2021
Full Text
View/download PDF

3. ASCAT IB: A radar-based vegetation optical depth retrieved from the ASCAT scatterometer satellite

Author

Nicolas Baghdadi, Lei Fan, Thomas Jagdhuber, Frédéric Frappart, Philippe Ciais, Jean-Pierre Wigneron, Xiaojun Li, Xiaojing Bai, Mengjia Wang, Mehrez Zribi, Xiangzhuo Liu, Christophe Moisy, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), ICOS-ATC (ICOS-ATC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Chongqing University [Chongqing], Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), German Aerospace Center (DLR), Beijing Normal University (BNU), Nanjing University (NJU), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, NDVI, Soil Science, 01 natural sciences, Normalized Difference Vegetation Index, 03 medical and health sciences, forest, Computers in Earth Sciences, Leaf area index, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Water content, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 0105 earth and related environmental sciences, Remote sensing, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, 0303 health sciences, biomass, VOD, Attenuation, Geology, Active microwave, Vegetation, Enhanced vegetation index, 15. Life on land, Scatterometer, Tree height, EVI, LAI, Lidar, ASCAT, 13. Climate action, [SDE]Environmental Sciences, Africa, Environmental science, Satellite
Abstract

International audience; Vegetation optical depth (VOD), as a microwave-based vegetation index for vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. Currently, VOD is mainly retrieved from passive microwave data and few studies focused on VOD retrievals from active microwave data. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at Vertical-Vertical (VV) polarization. In this study, a new ASCAT INRAE Bordeaux (IB) VOD (hereafter, IB VOD), was developed based on the Water Cloud Model (WCM) coupled with the Ulaby linear model for soil backscattering. The main features of IB VOD are that (i) the ERA5-Land soil moisture (SM) dataset was used as an auxiliary SM dataset in the retrievals, (ii) pixel-based soil model parameters were mapped using Random Forest (RF), and (iii) the vegetation model parameter was calibrated for each day. The IB VOD product was retrieved over Africa during 2015–2019, and its performances were evaluated in space and time by comparing with aboveground biomass (AGB), lidar tree height (TH), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index (LAI). Results were inter-compared with three other VOD products at the same frequency. In terms of spatial correlation with AGB (R = 0.92) and TH (R = 0.89), IB VOD outperforms the other VOD products, suggesting IB VOD has a strong ability to capture spatial patterns of AGB and TH. By comparing all VOD products against NDVI, EVI and LAI, we found that the highest temporal correlation with NDVI (EVI, LAI) was obtained with IB VOD over 29.94% (36.65%, 30.19%) of the study region. Considering all three vegetations indices, highest temporal correlation values with IB VOD could be particularly noted for deciduous broadleaf forests, woody savannas and savannas.

Published
2021
Full Text
View/download PDF

4. An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales

Author

Frédéric Frappart, Philippe Ciais, Rui Sun, Xiaojun Li, Jean-Pierre Wigneron, Xiangzhuo Liu, Lei Fan, Yi Liu, Mengjia Wang, Christophe Moisy, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), ICOS-ATC (ICOS-ATC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Beijing Normal University (BNU), Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Southwest University [Chongqing], Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Nanjing University of Information Science and Technology (NUIST)

Subjects
Spatial correlation, 010504 meteorology & atmospheric sciences, Reference data (financial markets), 0211 other engineering and technologies, Soil Science, 02 engineering and technology, 01 natural sciences, Normalized Difference Vegetation Index, Vegetation optical depth, Biomass, Computers in Earth Sciences, Leaf area index, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Biosphere, Geology, Vegetation, 15. Life on land, Albedo, LPDR, IB X-VOD, Africa, AMSR2, Environmental science, Scale (map), LPRM
Abstract

International audience; Vegetation optical depth (VOD) retrieved from microwave observations has been found to be useful to monitor the dynamics of the vegetation features at global scale. Particularly, many applications could be developed in several fields of research (ecology, water and carbon cycle, etc.) from VOD products retrieved from the SMOS and SMAP observations at L-band, and from the combined AMSR-E (2002−2011)/AMSR2 (2012-present) observations at C- and X-bands. One of the main difficulties in retrieving VOD is that the microwave observations are sensitive to both the soil (mainly soil moisture) and vegetation (mostly VOD) features. The AMSR-E/2 sensors provide only mono-angular observations at two polarizations. These dual-channel observations may be strongly correlated so that retrieving SM and VOD simultaneously can be an ill-posed problem. Here, to overcome this problem, we proposed and evaluated a new retrieval approach from AMSR2 observations at X-band to produce a new X-VOD product. The X-VOD retrievals were based on the inversion of the X-MEB model, an extension of the L-MEB model (L-band microwave emission of the biosphere) to the X-band. The main originality in comparison to previous algorithms is that (i) only VOD was retrieved while SM was estimated from a reanalysis data set (ERA5-Land); (ii) model inversion was based on an innovative approach to initialize the cost function; and (iii) new values for the soil and vegetation X-MEB model parameters were calibrated. To evaluate the methodology, we performed the VOD retrievals over the whole African continent over 2014–2016, including a dry (2015) and a wet (2014) year. In a first step, we calibrated a set of three parameters: effective scattering albedo (ω), soil roughness (HR) and VOD first guess (VODini). Several datasets of vegetation indices as Above-Ground Biomass (AGB), Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) were chosen as reference data to optimize these model parameters. Globally-constant values (ω = 0.06 and HR = 0.6) were found to achieve high spatial and temporal correlations between retrieved X-VOD and the reference vegetation parameters. Comparison with other X-VOD products suggested IB X-VOD had competitive advantages in terms of both spatial and temporal performances. In particular, spatial correlation with three biomass datasets was found to be higher than for previous X-VOD products (R2 ~ 0.76–0.83) and temporal correlation with LAI or NDVI showed obvious improvements, especially in dense vegetation.

Published
2021
Full Text
View/download PDF

5. First Retrievals of ASCAT-IB VOD (Vegetation Optical Depth) at Global Scale

Author

Roberto Fernandez-Moran, Christophe Moisy, Nicolas Baghdadi, Mengjia Wang, Xiaojun Li, A. Al-Yaari, Mehrez Zribi, Zanpin Xing, Xiangzhuo Liu, Philippe Ciais, Bertrand Ygorra, Frédéric Frappart, Lei Fan, Thomas Jagdhuber, Hongliang Ma, Jean-Pierre Wigneron, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Vegetation optical depth, 010504 meteorology & atmospheric sciences, vegetation mapping, 0211 other engineering and technologies, Scale (descriptive set theory), 02 engineering and technology, 01 natural sciences, Combinatorics, remote sensing, vegetation, optical sensor, C-band, ComputingMilieux_MISCELLANEOUS, attenuation, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Mathematics, prediction algorithm, biomass, Order (ring theory), 15. Life on land, Prediction algorithms, ASCAT, 13. Climate action, [SDE]Environmental Sciences, Vegetation optical Depth, Scatterometer, Biomedical optical imaging, Radar Measurement
Abstract

Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass $(\mathrm{R}=0.74,\ \text{RMSE} =44.82\ \text{Mg}\ \text{ha}^{-1})$ , which is better than V16 VOD $(\mathrm{R}=0.64,\ \text{RMSE} =51.27\ \text{Mg} \text{ha}^{-1})$ and VODCA VOD $(\mathrm{R}=0.72,\ \text{RMSE} =47.14\ \text{Mg}\ \text{ha}^{-1})$ . Some retrieval issues for IB VOD were found in boreal regions (e.g., Eastern America, Russia). In the future, we will focus on improving our algorithm in those regions, and produce a global and long-term dataset.

Published
2021
Full Text
View/download PDF

6. Development and validation of the SMOS-IC version 2 (V2) soil moisture product

Author

Frédéric Frappart, J. P. Wigneron, Xiangzhuo Liu, Lei Fan, Mengjia Wang, Xiaojun Li, Christophe Moisy, Amen Al-Yaari, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Géosciences Environnement Toulouse (GET), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Nanjing University of Information Science and Technology (NUIST), Pôle Matériel Végétal, UMT Géno-Vigne, and IFV Pôle Rhône-Méditerranée

Subjects
validation, Vegetation optical depth, 010504 meteorology & atmospheric sciences, Moisture, Meteorology, 0211 other engineering and technologies, Biosphere, Inversion (meteorology), 02 engineering and technology, Vegetation, 01 natural sciences, Carbon cycle, vegetation optical depth, L-band, 13. Climate action, Environmental science, Product (category theory), soil moisture, SMOS-IC, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Water content, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, SMOS
Abstract

International audience; Since the first version of the SMOS-IC retrieval product was released in 2017, its soil moisture (SM) and L-band Vegetation Optical depth (VOD) retrievals have proven to be a very interesting alternative product for the SMOS mission. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which is independent of auxiliary data, a key feature making it well-suited for application in hydrology, agriculture, climate, and carbon cycle. This paper describes the development and validation of the most recent SMOS-IC version (V2) soil moisture product. Compared with the previous version (V105), a new constraint was applied on VOD in the cost function which is minimized in the retrieval process. Soil moisture retrievals from SMOS-IC V2 & V105 were inter-compared against the “European Centre for Medium-Range Weather Forecasts” (ECMWF) modelled SM and the “International Soil Moisture Network” (ISMN) in-situ measurements during 2011-2017 over France. It was found that the average retrieval uncertainty of the new version product was lower than that of the old version, particularly when vegetation density increased. The new version of the SMOS-IC soil moisture product will be made available to the public through the CATDS (Centre Aval de Traitements des Données SMOS) website.

Published
2020
Full Text
View/download PDF

7. New ASCAT Vegetation Optical Depth (IB-VOD) Retrievals over Africa

Author

Frédéric Frappart, Nicolas Baghdadi, Mehrez Zribi, Thomas Jagdhuber, Jean-Pierre Wigneron, Xiaojun Li, Lei Fan, Xiangzhuo Liu, Mengjia Wang, Christophe Moisy, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), DLR Microwaves and Radar Institute, German Aerospace Center (DLR), Beijing Normal University (BNU), Nanjing University of Information Science and Technology (NUIST), Department of Earth System Science [Irvine] (ESS), University of California [Irvine] (UCI), University of California-University of California, TOSCA (Terre Ocean Surfaces Continentales et Atmosphere), CNES, France (Centre National d'Etudes Spatiales), and China Scholarship Council

Subjects
Vegetation optical depth, 010504 meteorology & atmospheric sciences, Backscatter, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, vegetation, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, attenuation, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, vegetation water content, [SDE.IE]Environmental Sciences/Environmental Engineering, VOD, Vegetation, 15. Life on land, Scatterometer, ASCAT, 13. Climate action, Africa, [SDE]Environmental Sciences, Environmental science, scatterometer, empirical, Water Cloud Model
Abstract

International audience; Vegetation Optical Depth (VOD) plays an important role in monitoring the earth ecosystems. There are many VOD products released based on different satellites and frequencies. But most of the VOD products are derived from passive microwave data, and very few active VOD products have been released to date. This study investigated retrievals of the active microwave VOD product from C-band ASCAT (Advanced SCATterometer) observations using the water cloud model in large areas. To achieve this, the ASCAT backscatter data and ECMWF soil moisture data were used as inputs to retrieve ASCAT VOD over the whole Africa. The correlation between the retrieved VOD product and proxies of vegetation density (Saatchi biomass) were used to evaluate the model performance.

Published
2020
Full Text
View/download PDF

8. Estimation of Global Net Primary Productivity from 1981 to 2018 with Remote Sensing Data

Author

Tao Yu, Mengjia Wang, Juanmin Wang, Zhiqiang Xiao, Anran Zhu, and Rui Sun

Subjects
010504 meteorology & atmospheric sciences, Annual growth rate, Biome, 0211 other engineering and technologies, Northern Hemisphere, Primary production, 02 engineering and technology, Vegetation, 01 natural sciences, Climatology, Environmental science, Satellite, Productivity, Southern Hemisphere, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

The long time series vegetation productivity products are of great significance to the research of increasing CO 2 and global changes. In this paper, global net primary productivity (NPP) in 1981–2018 was firstly estimated with Global LAnd Surface Satellite (GLASS) data, ERA-Interim meteorological data and the other variables by using the improved Multi-source data Synergized Quantitative (MuSyQ) NPP algorithm. The average global NPP is 61.0 PgC/yr in 1981–2018, which is in great agreement with the other similar products. The global NPP has shown a significant increase trend, with an annual growth rate of 0.10 PgC/yr over the past 38 years. NPP in the northern hemisphere and southern hemisphere account for 62.0% and 38.0% of the global respectively, both show an increasing trend. The overall increasing trends in NPP are also consistent among most of the biomes.

Published
2020
Full Text
View/download PDF

9. Vegetation Optical Depth Retrieval from AMSR-E/AMSR2 Observations Using L-MEB Inversion

Author

Lei Fan, Mengjia Wang, Xiangzhuo Liu, Martin Brandt, Xiaojun Li, Philippe Ciais, Yi Liu, Jean-Pierre Wigneron, Rasmus Fensholt, Rui Sun, Frédéric Frappart, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Beijing Normal University (BNU), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Department of Geosciences and Natural Resource Management [Copenhagen] (IGN), Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Nanjing University (NJU), Department of Earth System Science [Irvine] (ESS), University of California [Irvine] (UCI), University of California-University of California, Nanjing University of Information Science and Technology (NUIST), University of Copenhagen = Københavns Universitet (KU), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), University of California [Irvine] (UC Irvine), University of California (UC)-University of California (UC), and University of Copenhagen = Københavns Universitet (UCPH)

Subjects
Vegetation optical depth, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Inversion (meteorology), 02 engineering and technology, Vegetation, 15. Life on land, 01 natural sciences, Atmospheric radiative transfer codes, 13. Climate action, Satellite data, Environmental science, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

International audience; Decade years of efforts on the retrieval of soil moisture based on radiative transfer model have largely improved the accuracy of soil moisture (SM). This paper focus on the other parameter, namely vegetation optical depth (VOD). We retrieved X-band VOD from AMSR-E and AMSR2 observations by inverting the L-MEB model (Wigneron et al. 2007 [1]) at X-band, considering that SM was known. As SM input to the L-MEB inversion we used the ECMWF SM product. This step avoids correlation between VOD and SM retrievals from the mono-angular AMSR-E observations. In a first step we evaluated the retrieved VOD with the Copernicus Global Land Service (CGLS) LAI. The evaluation results indicate our model has a great potential for VOD retrievals from AMSR-E/2 satellite data.

Published
2020
Full Text
View/download PDF

10. Evaluation and Comparison of Light Use Efficiency and Gross Primary Productivity Using Three Different Approaches

Author

Rui Sun, Mengjia Wang, Zhiqiang Xiao, and Anran Zhu

Subjects
clearness index, 010504 meteorology & atmospheric sciences, Science, 0211 other engineering and technologies, 02 engineering and technology, Atmospheric sciences, 01 natural sciences, Gross primary productivity, Atmosphere, FluxNet, light use efficiency, gross primary productivity, parameterization approach, Cubist regression tree approach, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, cubist regression tree approach, Contrast (statistics), Vegetation, Regression, Physics::Space Physics, General Earth and Planetary Sciences, Environmental science, Satellite, Scale (map)
Abstract

Light use efficiency (LUE), which characterizes the efficiency with which vegetation converts captured/absorbed radiation into organic dry matter through photosynthesis, is a key parameter for estimating vegetation gross primary productivity (GPP). Studies suggest that diffuse radiation induces a higher LUE than direct radiation in short-term and site-scale experiments. The clearness index (CI), described as the fraction of solar incident radiation on the surface of the earth to the extraterrestrial radiation at the top of the atmosphere, is added to the parameterization approach to explain the conditions of diffuse and direct radiation in this study. Machine learning methods—such as the Cubist regression tree approach—are also popular approaches for studying vegetation carbon uptake. This paper aims to compare and analyze the performances of three different approaches for estimating global LUE and GPP. The methods for collecting LUE were based on the following: (1) parameterization approach without CI; (2) parameterization approach with CI; and (3) Cubist regression tree approach. We collected GPP and meteorological data from 180 FLUXNET sites as calibration and validation data and the Global Land Surface Satellite (GLASS) products and ERA-interim data as input data to estimate the global LUE and GPP in 2014. Site-scale validation with FLUXNET measurements indicated that the Cubist regression approach performed better than the parameterization approaches. However, when applying the approaches to global LUE and GPP, the parameterization approach with the CI became the most reliable approach, then closely followed by the parameterization approach without the CI. Spatial analysis showed that the addition of the CI improved the LUE and GPP, especially in high-value zones. The results of the Cubist regression tree approach illustrate more fluctuations than the parameterization approaches. Although the distributions of LUE presented variations over different seasons, vegetation had the highest LUE, at approximately 1.5 gC/MJ, during the whole year in equatorial regions (e.g., South America, middle Africa and Southeast Asia). The three approaches produced roughly consistent global annual GPPs ranging from 109.23 to 120.65 Pg/yr. Our results suggest the parameterization approaches are robust when extrapolating to the global scale, of which the parameterization approach with CI performs slightly better than that without CI. By contrast, the Cubist regression tree produced LUE and GPP with lower accuracy even though it performed the best for model validation at the site scale.

Published
2020

11. Assessment of Npp Dynamics and the Responses to Climate Changes in China From 1982 to 2012

Author

Gang Liu, Rui Sun, Mengjia Wang, and Zhiqiang Xiao

Subjects
010504 meteorology & atmospheric sciences, Climate change, Growing season, Vegetation, 010501 environmental sciences, Atmospheric sciences, 01 natural sciences, Carbon cycle, Period (geology), Environmental science, Spatial variability, Terrestrial ecosystem, Precipitation, 0105 earth and related environmental sciences
Abstract

NPP is calculated to characterize vegetation activity as well as improve our understanding of terrestrial ecosystem carbon cycle. In this study, we estimated a time series of NPP and the spatial and temporal variations from 1982 to 2012 in China. Subsequently, the correlations between the NPP and climate factors (temperature and precipitation) were evaluated to show the responses of vegetation NPP to climate changes. The results showed that NPP in China decreased from southeast to northwest due to the spatial variability of vegetation types and climate characteristics. Annual NPP had a fluctuating increase tendency during our study period with values ranging from 1.92 to 2.73 PgC•a-1, with an annual increase of 0.02 PgC•a-2. In addition, NPP in north China correlates positively with precipitation and negatively correlates with temperature, this is owing to the fact that this region is relatively dry and increasing precipitation extends the growing season of vegetation. In south China the results were the opposite.

Published
2019
Full Text
View/download PDF

12. Global-scale assessment and inter-comparison of recently developed/reprocessed microwave satellite vegetation optical depth products

Author

Christophe Moisy, Philippe Ciais, Xiangzhuo Liu, Alexandra G. Konings, Dara Entekhabi, Rasmus Fensholt, Amen Al-Yaari, Lei Fan, Jean-Pierre Wigneron, Mengjia Wang, Martin Brandt, Xiaojun Li, Frédéric Frappart, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Nanjing University of Information Science and Technology (NUIST), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), ICOS-ATC (ICOS-ATC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Department of Geosciences and Natural Resource Management [Copenhagen] (IGN), Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), MIT Parsons Laboratory, Massachusetts Institute of Technology (MIT), Stanford University, Beijing Normal University (BNU), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOREST CARBON STOCKS, Height of vegetation, 010504 meteorology & atmospheric sciences, LAND SURFACES, 0208 environmental biotechnology, Biome, Soil Science, 1.4 GHZ, 02 engineering and technology, 01 natural sciences, SURFACE SOIL-MOISTURE, Vegetation cycle, EFFECTIVE SCATTERING ALBEDO, medicine, EMPIRICAL-MODEL, Vegetation optical depth, SMAP MT-DCA, Ecosystem, Biomass, Computers in Earth Sciences, 0105 earth and related environmental sciences, Remote sensing, Tree canopy, Phenology, Geology, AMSR-E, 15. Life on land, Seasonality, medicine.disease, LPDR, VODCA, 020801 environmental engineering, DATA SETS, Boreal, 13. Climate action, [SDE]Environmental Sciences, Environmental science, Satellite, medicine.symptom, SMOS-IC, Vegetation (pathology), LPRM, L-BAND, SMOS
Abstract

The vegetation optical depth (VOD), a vegetation index retrieved from passive or active microwave remote sensing systems, is related to the intensity of microwave extinction effects within the vegetation canopy layer. This index is only marginally impacted by effects from atmosphere, clouds and sun illumination, and thus increasingly used for ecological applications at large scales. Newly released VOD products show different abilities in monitoring vegetation features, depending on the algorithm used and the satellite frequency. VOD is increasingly sensitive to the upper vegetation layer as the frequency increases (from L-, C- to X-band), offering different capacities to monitor seasonal changes of the leafy and/or woody vegetation components, vegetation water status and aboveground biomass. This study evaluated nine recently developed/reprocessed VOD products from the AMSR2, SMOS and SMAP space-borne instruments for monitoring structural vegetation features related to phenology, height and aboveground biomass.For monitoring the seasonality of green vegetation (herbaceous and woody foliage), we found that X-VOD products, particularly from the LPDR-retrieval algorithm, outperformed the other VOD products in regions that are not densely vegetated, where they showed higher temporal correlation values with optical vegetation indices (VIs). However, LPDR X-VOD time series failed to detect changes in VOD after rainfall events whereas most other VOD products could do so, and overall daily variations are less pronounced in LPDR X-VOD. Results show that the reprocessed VODCA C- and X-VOD have almost comparable performance and VODCA C-VOD correlates better with VIs than other C-VOD products. Low frequency L-VOD, particularly the new version (V2) of SMOS-IC, show a higher temporal correlation with VIs, similar to C-VOD, in medium-densely vegetated biomes such as savannas (R similar to 0.70) than for other short vegetation types. Because the L-VOD indices are more sensitive to the non-green vegetation components (trunks and branches) than higher frequency products, they are well-correlated with aboveground biomass: (R similar to 0.91) across space between predicted and observed values for both SMOS-IC V2 and SMAP MT-DCA. However, when compared with forest canopy height, results at L-band are not systematically better than C- and X-VOD products. This revealed specific VOD retrieval issues for some ecosystems, e.g., boreal regions. It is expected that these findings can contribute to algorithm refinements, product enhancements and further developing the use of VOD for monitoring above-ground vegetation biomass, vegetation dynamics and phenology.

Published
2021
Full Text
View/download PDF

13. Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland

Author

Rui Sun, Mengjia Wang, and Zhiqiang Xiao

Subjects
Canopy, 010504 meteorology & atmospheric sciences, Science, 0211 other engineering and technologies, 02 engineering and technology, Atmospheric sciences, 01 natural sciences, Altimeter, ICESat GLAS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Shore, Biomass (ecology), Tree canopy, geography, geography.geographical_feature_category, random forest model, Similar distribution, forest canopy height, aboveground biomass, Landsat, Lidar, General Earth and Planetary Sciences, Environmental science, Aboveground biomass
Abstract

Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height and the aboveground biomass at a 30 m spatial resolution in Maryland by combining Geoscience Laser Altimeter System (GLAS) data and Landsat spectral imageries. The processes for calculating the forest biomass included the following: (i) processing the GLAS waveform and calculating spatially discrete forest canopy heights; (ii) developing canopy height models from Landsat imagery and extrapolating them to spatially contiguous canopy heights in Maryland; and, (iii) estimating forest aboveground biomass according to the relationship between canopy height and biomass. In our study, we explore the ability to use the GLAS waveform to calculate canopy height without ground-measured forest metrics (R2 = 0.669, RMSE = 4.82 m, MRE = 15.4%). The machine learning models performed better than the principal component model when mapping the regional forest canopy height and aboveground biomass. The total forest aboveground biomass in Maryland reached approximately 160 Tg. When compared with the existing Biomass_CMS map, our biomass estimates presented a similar distribution where higher values were in the Western Shore Uplands region and Folded Application Mountain section, while lower values were located in the Delmarva Peninsula and Allegheny Mountain regions.

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results