129 related articles for article (PubMed ID: 24101210)
21. Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method.
Liu M; Wang T; Skidmore AK; Liu X; Li M
Environ Pollut; 2019 Apr; 247():488-498. PubMed ID: 30703682
[TBL] [Abstract][Full Text] [Related]
22. Application and Comparison of the MODIS-Derived Enhanced Vegetation Index to VIIRS, Landsat 5 TM and Landsat 8 OLI Platforms: A Case Study in the Arid Colorado River Delta, Mexico.
Jarchow CJ; Didan K; Barreto-Muñoz A; Nagler PL; Glenn EP
Sensors (Basel); 2018 May; 18(5):. PubMed ID: 29757265
[TBL] [Abstract][Full Text] [Related]
23. Automatic and adaptive paddy rice mapping using Landsat images: Case study in Songnen Plain in Northeast China.
Qiu B; Lu D; Tang Z; Chen C; Zou F
Sci Total Environ; 2017 Nov; 598():581-592. PubMed ID: 28454031
[TBL] [Abstract][Full Text] [Related]
24. [Temporal and spatial variation of MODIS vegetation indices in Hunan Province].
Lin H; Xiong YJ; Wan LF; Mo DK; Sun H
Ying Yong Sheng Tai Xue Bao; 2007 Mar; 18(3):581-5. PubMed ID: 17552197
[TBL] [Abstract][Full Text] [Related]
25. Characterizing spatiotemporal non-stationarity in vegetation dynamics in China using MODIS EVI dataset.
Qiu B; Zeng C; Tang Z; Chen C
Environ Monit Assess; 2013 Nov; 185(11):9019-35. PubMed ID: 23649474
[TBL] [Abstract][Full Text] [Related]
26. [Comparison of GIMMS and MODIS normalized vegetation index composite data for Qing-Hai-Tibet Plateau].
Du JQ; Shu JM; Wang YH; Li YC; Zhang LB; Guo Y
Ying Yong Sheng Tai Xue Bao; 2014 Feb; 25(2):533-44. PubMed ID: 24830255
[TBL] [Abstract][Full Text] [Related]
27. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine.
Dong J; Xiao X; Menarguez MA; Zhang G; Qin Y; Thau D; Biradar C; Moore B
Remote Sens Environ; 2016 Nov; 185():142-154. PubMed ID: 28025586
[TBL] [Abstract][Full Text] [Related]
28. Crop Classification Based on Time Series MODIS EVI and Ground Observation for Three Adjoining Years in Xinjiang.
Muhammad S; Niu Z; Wang L; Aablikim A; Hao PY; Wang CY
Guang Pu Xue Yu Guang Pu Fen Xi; 2015 May; 35(5):1345-50. PubMed ID: 26415458
[TBL] [Abstract][Full Text] [Related]
29. Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images.
Liu M; Wang T; Skidmore AK; Liu X
Sci Total Environ; 2018 Oct; 637-638():18-29. PubMed ID: 29738893
[TBL] [Abstract][Full Text] [Related]
30. Spatial and temporal distribution of the cloud optical depth over China based on MODIS satellite data during 2003-2016.
Li X; Che H; Wang H; Xia X; Chen Q; Gui K; Zhao H; An L; Zheng Y; Sun T; Sheng Z; Liu C; Zhang X
J Environ Sci (China); 2019 Jun; 80():66-81. PubMed ID: 30952354
[TBL] [Abstract][Full Text] [Related]
31. [Adaptability analysis of FAO Penman-Monteith model over typical underlying surfaces in the Sanjiang Plain, Northeast China].
Jia ZJ; Han L; Wang G; Zhang TS
Ying Yong Sheng Tai Xue Bao; 2014 May; 25(5):1327-34. PubMed ID: 25129932
[TBL] [Abstract][Full Text] [Related]
32. Spatiotemporal characteristics of aerosols and their trends over mainland China with the recent Collection 6 MODIS and OMI satellite datasets.
Hu K; Kumar KR; Kang N; Boiyo R; Wu J
Environ Sci Pollut Res Int; 2018 Mar; 25(7):6909-6927. PubMed ID: 29273982
[TBL] [Abstract][Full Text] [Related]
33. Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring.
Wu M; Li H; Huang W; Niu Z; Wang C
Environ Sci Process Impacts; 2015 Aug; 17(8):1396-404. PubMed ID: 26165141
[TBL] [Abstract][Full Text] [Related]
34. Evaluating Crop Area Mapping from MODIS Time-Series as an Assessment Tool for Zimbabwe's "Fast Track Land Reform Programme".
Hentze K; Thonfeld F; Menz G
PLoS One; 2016; 11(6):e0156630. PubMed ID: 27253327
[TBL] [Abstract][Full Text] [Related]
35. [Quantifying direct N2O emissions from paddy fields during rice growing season in China: model establishment].
Zou JW; Qin YM; Liu SW
Huan Jing Ke Xue; 2009 Feb; 30(2):313-21. PubMed ID: 19402475
[TBL] [Abstract][Full Text] [Related]
36. Monitoring forest dynamics with multi-scale and time series imagery.
Huang C; Zhou Z; Wang D; Dian Y
Environ Monit Assess; 2016 May; 188(5):273. PubMed ID: 27056478
[TBL] [Abstract][Full Text] [Related]
37. Estimation of water consumption for ecosystems based on Vegetation Interfaces Processes Model: A case study of the Aksu River Basin, Northwest China.
Yang P; Xia J; Zhan C; Mo X; Chen X; Hu S; Chen J
Sci Total Environ; 2018 Feb; 613-614():186-195. PubMed ID: 28915455
[TBL] [Abstract][Full Text] [Related]
38. [Effects of marshland reclamation on evapotranspiration in the Sanjiang Plain].
Jia ZJ; Zhang W; Huang Y; Zhao XS; Song CC
Huan Jing Ke Xue; 2010 Apr; 31(4):833-42. PubMed ID: 20527159
[TBL] [Abstract][Full Text] [Related]
39. Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.
Paciorek CJ; Liu Y;
Res Rep Health Eff Inst; 2012 May; (167):5-83; discussion 85-91. PubMed ID: 22838153
[TBL] [Abstract][Full Text] [Related]
40. [Identification of forest vegetation types in southern China based on spatio-temporal fusion of GF-1 WFV and MODIS data].
Xu L; Ouyang XZ; Pan P; Zang H; Liu J; Yang K
Ying Yong Sheng Tai Xue Bao; 2022 Jul; 33(7):1948-1956. PubMed ID: 36052799
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
