114 related articles for article (PubMed ID: 37972784)
1. Quantifying key vegetation parameters from Sentinel-3 and MODIS over the eastern Eurasian steppe with a Bayesian geostatistical model.
Li Z; Ding L; Shen B; Chen J; Xu D; Wang X; Fang W; Pulatov A; Kussainova M; Amarjargal A; Isaev E; Liu T; Sun C; Xin X
Sci Total Environ; 2024 Jan; 909():168594. PubMed ID: 37972784
[TBL] [Abstract][Full Text] [Related]
2. Spatial patterns and driving factors of aboveground and belowground biomass over the eastern Eurasian steppe.
Ding L; Li Z; Shen B; Wang X; Xu D; Yan R; Yan Y; Xin X; Xiao J; Li M; Wang P
Sci Total Environ; 2022 Jan; 803():149700. PubMed ID: 34487901
[TBL] [Abstract][Full Text] [Related]
3. Forest biomass estimation using remote sensing and field inventory: a case study of Tripura, India.
Pandey PC; Srivastava PK; Chetri T; Choudhary BK; Kumar P
Environ Monit Assess; 2019 Aug; 191(9):593. PubMed ID: 31456055
[TBL] [Abstract][Full Text] [Related]
4. Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine.
Reyes-Muñoz P; Pipia L; Salinero-Delgado M; Belda S; Berger K; Estévez J; Morata M; Rivera-Caicedo JP; Verrelst J
Remote Sens (Basel); 2022 Mar; 14(6):1347. PubMed ID: 36016907
[TBL] [Abstract][Full Text] [Related]
5. Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources.
De Grave C; Verrelst J; Morcillo-Pallarés P; Pipia L; Rivera-Caicedo JP; Amin E; Belda S; Moreno J
Remote Sens Environ; 2020 Dec; 251():. PubMed ID: 36082362
[TBL] [Abstract][Full Text] [Related]
6. Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data.
Tao H; Feng H; Xu L; Miao M; Long H; Yue J; Li Z; Yang G; Yang X; Fan L
Sensors (Basel); 2020 Feb; 20(5):. PubMed ID: 32120958
[TBL] [Abstract][Full Text] [Related]
7. Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm.
Wang Y; Wu G; Deng L; Tang Z; Wang K; Sun W; Shangguan Z
Sci Rep; 2017 Jul; 7(1):6940. PubMed ID: 28761059
[TBL] [Abstract][Full Text] [Related]
8. Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution: A Multiscale Analysis with the Sentinel-3 OLCI Sensor.
De Grave C; Pipia L; Siegmann B; Morcillo-Pallarés P; Rivera-Caicedo JP; Moreno J; Verrelst J
Remote Sens (Basel); 2021 Apr; 13(8):1419. PubMed ID: 36082339
[TBL] [Abstract][Full Text] [Related]
9. [Retrieval of leaf area index of Phyllostachys praecox forest based on MODIS reflectance time series data.].
Zhu DE; Xu XJ; DU HQ; Zhou GM; Mao FJ; Li XJ; Li YG
Ying Yong Sheng Tai Xue Bao; 2018 Jul; 29(7):2391-2400. PubMed ID: 30039679
[TBL] [Abstract][Full Text] [Related]
10. Spatial modelling of agro-ecologically significant grassland species using the INLA-SPDE approach.
Fichera A; King R; Kath J; Cobon D; Reardon-Smith K
Sci Rep; 2023 Mar; 13(1):4972. PubMed ID: 36973470
[TBL] [Abstract][Full Text] [Related]
11. Inversion models of aboveground grassland biomass in Xinjiang based on multisource data.
Zhang RP; Zhou JH; Guo J; Miao YH; Zhang LL
Front Plant Sci; 2023; 14():1152432. PubMed ID: 36993850
[TBL] [Abstract][Full Text] [Related]
12. Combining remote sensing imagery and forest age inventory for biomass mapping.
Zheng G; Chen JM; Tian QJ; Ju WM; Xia XQ
J Environ Manage; 2007 Nov; 85(3):616-23. PubMed ID: 17134821
[TBL] [Abstract][Full Text] [Related]
13. Estimating the aboveground biomass of coniferous forest in Northeast China using spectral variables, land surface temperature and soil moisture.
Jiang F; Kutia M; Ma K; Chen S; Long J; Sun H
Sci Total Environ; 2021 Sep; 785():147335. PubMed ID: 33933773
[TBL] [Abstract][Full Text] [Related]
14. Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India.
Sinha SK; Padalia H; Dasgupta A; Verrelst J; Rivera JP
Int J Appl Earth Obs Geoinf; 2020 Apr; 86():102027. PubMed ID: 36081897
[TBL] [Abstract][Full Text] [Related]
15. An evaluation model for aboveground biomass based on hyperspectral data from field and TM8 in Khorchin grassland, China.
Zhang X; Chen X; Tian M; Fan Y; Ma J; Xing D
PLoS One; 2020; 15(2):e0223934. PubMed ID: 32109248
[TBL] [Abstract][Full Text] [Related]
16. Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research.
Liu R; Chen JM; Liu J; Deng F; Sun R
J Environ Manage; 2007 Nov; 85(3):649-58. PubMed ID: 17123698
[TBL] [Abstract][Full Text] [Related]
17. Estimation of Aboveground Biomass in Agroforestry Systems over Three Climatic Regions in West Africa Using Sentinel-1, Sentinel-2, ALOS, and GEDI Data.
Kanmegne Tamga D; Latifi H; Ullmann T; Baumhauer R; Bayala J; Thiel M
Sensors (Basel); 2022 Dec; 23(1):. PubMed ID: 36616947
[TBL] [Abstract][Full Text] [Related]
18. Beyond "greening" and "browning": Trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation.
Lewińska KE; Ives AR; Morrow CJ; Rogova N; Yin H; Elsen PR; de Beurs K; Hostert P; Radeloff VC
Glob Chang Biol; 2023 Aug; 29(16):4620-4637. PubMed ID: 37254258
[TBL] [Abstract][Full Text] [Related]
19. [Variation of leaf area index estimation in forests based on remote sensing images of different spatial scales.].
Liu T; Chen C; Fan WY; Mao XG; Yu Y
Ying Yong Sheng Tai Xue Bao; 2019 May; 30(5):1687-1698. PubMed ID: 31107026
[TBL] [Abstract][Full Text] [Related]
20. Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI.
Liu S; Cheng F; Dong S; Zhao H; Hou X; Wu X
Sci Rep; 2017 Jun; 7(1):4182. PubMed ID: 28646198
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]