International Journal of Earth & Environmental Sciences Volume 4 (2019), Article ID 4:IJEES-167, 11 pages
https://doi.org/10.15344/2456-351X/2019/167
https://doi.org/10.15344/2456-351X/2019/167
Original Article
The Spatial-temporal Variation of Land Surface Albedo in Hengshui During 2001-2015 by Blending Landsat and GLASS Data
References
- Dickinson RE (1983) Land surface processes and climate-surface albedos and energy balance. J Adv Geophys 25: 305-353. View
- Wiscombe WJ, Warren SG (1980) A model for the spectral albedo of snow. I: pure snow. J Atmos Sci 37: 2712-2733. View
- Wang S, Grant RF, Verseghy DL, Black TA (2001) Modelling plant carbon and nitrogen dynamics of a boreal aspen forest in CLASS-the Canadian land surface scheme. Ecol Model 142: 135-154. View
- Viterbo P, Betts AK (1999) Impact on ECMWF forecasts of changes to the albedo of the boreal forests in the presence of snow. J Geophys Res 104: 27803-27810. View
- Bloch MR (2017) Dust-induced albedo changes of polar ice sheets and glacierization. J Glaciol 5: 241-244. View
- Pinty B, Lavergne T, Kaminski T, Aussedat O, Giering R, et al. (2008) Partitioning the solar radiant fluxes in forest canopies in the presence of snow. J Geophys Res. View
- Betts AK, Ball JH (1997) Albedo over the boreal forest. J Geophys Res 102: 28901-28909. View
- Koerner RM (1980) Instantaneous glacierization, the rate of albedo change, and feedback effects at the beginning of an ice age. Quat Res 13: 153-159. View
- Jönsson P, Eklundh L (2004) TIMESAT - a program for analyzing time-series of satellite sensor data. Comput Geosci 30: 833-845. View
- Spruce JP, Hargrove WW, Gasser G (2013) Monitoring regional forest disturbances across the US with near real time MODIS NDVI products resident to the ForWarn Forest Threat Early Warning System. C AGU Fall Meeting Abstracts. View
- Hansen AJ, Neilson RP, Dale VH, Flather CH, Iverson LR, et al. (2001) Global change in forests: responses of species, communities, and biomes. Bioscience 51: 765-779. View
- Dale VH, Joyce LA, Mcnulty SG, Neilson RP, Ayres MP, et al. (2001) Climate change and forest disturbances. Bioscience 51: 723-734. View
- Kennedy RE, Yang ZQ, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. Landtrendr - temporal segmentation algorithms. Remote Sens. Environ 114: 2897-2910. View
- Walker JJ, Beurs KMD, Wynne RH, Gao F (2012) Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology. Remote Sens. Environ 117: 381-393. View
- Kulakowski D, Bebi P, Rixen C (2011) The interacting effects of land use change, climate change and suppression of natural disturbances on landscape forest structure in the Swiss Alps. Oikos 120: 216-225. View
- Shuai Y, Masek JG, Gao F, Schaaf CB, He T, et al. (2014) Approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge. Remote Sens Environ 152: 467-479. View
- Melesse AM, Weng Q, Thenkabail PS, Senay GB (2007) Remote sensing sensors and applications in environmental resources mapping and modelling. Sensors 7: 3209-3241. View
- Wang Z, Schaaf CB, Sun Q, Shuai Y, Román MO, et al. (2018) Capturing rapid land surface dynamics with collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products. Remote Sens Environ 207: 50-64. View
- Hazaymeh K, Hassan QK (2015) Spatiotemporal image-fusion model for enhancing the temporal resolution of landsat-8 surface reflectance images using MODIS images. J Appl Remote Sens 9: 1-14. View
- Wu W, Shi P, Hu W (2012) Study on land ecological risk in oasis city based on LUCC-a case study in the Ganzhou district. Arid Zone Res 29: 122-128.
- Cai F, Zhou G, Ming H, Li R, Zhang G, et al. (2012) A simulative study of effects of dynamic parameterization of surface albedo on land-atmosphere flux exchange: a case of rainfed maize field in northeast China. Acta Meteorol. Sin. 70: 1149-1164.
- Sutterlin M, Stöckli R, Schaaf CB, Wunderle S (2016) Albedo climatology for European land surfaces retrieved from AVHRR data (1990–2014) and its spatial and temporal analysis from green-up to vegetation senescence. J. Geophys. Res. 121: 8156-8171. View
- Geleyn J F, Preuß HJ (1983) A new data set of satellite-derived surface albedo values for operational use at ECMWF. Meteorol Atmos Phys 32: 353-359. View
- Martonchik JV, Diner DJ, Pinty B, Verstraete MM, Myneni RB, et al. (1998) Determination of land and ocean reflective, radiative, and biophysical properties using multiangle imaging. IEEE Trans Geosci Remote Sensing 36: 1266-1281. View
- Csiszar I, Gutman G (1999) Mapping global land surface albedo from NOAA AVHRR. J Geophys Res: Atmos 104: 6215-6228. View
- Schaaf CB, Gao F, Strahler AH, Lucht W, Li X, et al. (2002) First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ 83: 135-148. View
- Qu Y, Liang S, Liu Q, He T, Liu S, et al. (2015) Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products. Remote Sens 7: 990-1020. View
- Mokhtari MH, Busu I (2011) Downscaling albedo from moderate-resolution imaging spectroradiometer (MODIS) to advanced space-borne thermal emission and reflection radiometer (ASTER) over an agricultural area utilizing ASTER visible-near infrared spectral bands. Int J Phys Sci 6: 5804- 5821. View
- Inglada J, Hagolle O, Dedieu G (2011) Low and high spatial resolution time series fusion for improved land cover map production. Analysis of Multi- Temporal Remote Sensing Images. IEEE. View
- Li X, Ling F, Foody GM, Ge Y, Zhang Y, et al. (2017) Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps. Remote Sens Environ 196: 293-311. View
- Rao Y, Zhu X, Chen J, Wang J (2015) An improved method for producing high spatial-resolution NDVI time series datasets with multi-temporal MODIS NDVI data and Landsat TM/ETM+ images. Remote Sens 7: 7865- 7891. View
- Röder A (2012) Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian savanna. J Appl Remote Sens 6: 2240-2246. View
- Tian F, Wang Y, Fensholt R, Wang K, Zhang L, et al. (2013) Mapping and evaluation of NDVI trends from synthetic time series obtained by blending Landsat and MODIS data around a coalfield on the Loess Plateau. Remote Sens 5: 4255-4279. View
- Zhang W, Li A, Jin H, Bian J, Zhang Z, et al. (2013) An enhanced spatial and temporal data fusion model for fusing Landsat and MODIS surface reflectance to generate high temporal Landsat-like data. Remote Sens 5: 5346-5368. View
- Barnes CA, Roy DP (2008) Radiative forcing over the conterminous United States due to contemporary land cover land use albedo change. Geophys Res Lett 35: 148-161. View
- Cohen WB, Goward SN (2004) Landsat's role in ecological applications of remote sensing. Bioscience 54: 535-545. View
- Griffiths P, Kuemmerle T, Kennedy RE, Abrudan IV, Knorn J, et al. (2012) Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania. Remote Sens Environ 118: 199-214. View
- Roy DP, Wulder MA, Loveland TR, Woodcock CE, Allen RG, et al. (2014) Landsat-8: science and product vision for terrestrial global change research. Remote Sens Environ 145: 154-172. View
- Masek J, Vermote E, Saleous NA, Wolfe RE, Hall FG, et al. (2006) A Landsat surface reflectance dataset for north America, 1990-2000. IEEE Geosci. Remote Sens Lett 3: 68-72. View
- Shuai Y, Masek JG (2011) An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF. Remote Sens. Environ. 115: 2204-2216. View
- Wang Z, Schaaf CB, Sun Q, Kim J, Erb AM, et al. (2017) Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/ Albedo product. Int J Appl Earth Obs Geoinf 59: 104-117. View
- Huang B, Zhang H (2014) Spatio-temporal reflectance fusion via unmixing: accounting for both phenological and land-cover changes. Int J Remote Sens 35: 6213-6233. View
- Jarihani AA, Mcvicar TR, Van Niel TG, Emelyanova IV, Callow JN, et al. (2014) Blending Landsat and MODIS data to generate multispectral indices: a comparison of “index-then-blend” and “blend-then-index” approaches. Remote Sens 6: 9213-9238. View
- Gao F, Masek J, Schwaller M, Hall F (2006) On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans Geosci Remote Sensing 44: 2207-2218. View
- Emelyanova IV, Mcvicar TR, Van Niel TG, Li L, Dijk AI, et al. (2013) Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: a framework for algorithm selection. Remote Sens Environ 133: 193-209. View
- Gevaert CM, García-Haro FJ (2015) A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote Sens Environ 156: 34-44. View
- Zhu X, Chen J, Gao F, Chen X, Masek JG, et al. (2010) An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens Environ 114: 2610-2623. View
- Yu T, Sun R, Xiao Z, Zhang Q, Wang J, et al. (2018) Generation of High Resolution Vegetation Productivity from a Downscaling Method. Remote Sens 10: 1748. View
- Meng J, Wu B, Du X, Niu L, Zhang F, et al. (2011) Method to construct high spatial and temporal resolution NDVI dataset-STAVFM. J. Remote Sens 15: 44-59. View
- Meng J, Du X, Wu B (2013) Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation. International Journal of Digital Earth 6: 203-218. View
- Hoshino B, Ma J, Wang Q, Kaneko M, Fukuyama R (2004) Scaling transformation of remote sensing digital image with multiple resolutions from different sensors. Acta Geogr. Sin. 59: 101-110. View
- Qu Y, Liu Q, Liang S, Wang L, Liu N, et al. (2014) Direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Trans Geosci Remote Sensing 52: 907-919. View
- Chen J, Cao X, Peng S, Ren H (2017) Analysis and applications of GlobeLand30: a review. ISPRS Int J Geo-Inf 6: 230. View
- Wang L, Zheng X, Sun L, Liu Q, Liu S, et al. (2014) Validation of GLASS albedo product through Landsat TM data and ground measurements. J Remote Sens 18: 547-558.
- Long C, Ackerman T (2000) Identification of clear skies from broadband pyranometer measurements and calculation of downwelling shortwave cloud effects. J Geophys Res 105: 15609-15626. View
- Hilker T, Wulder MA, Coops NC, Seitz N, White JC, et al. (2009) Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. Remote Sens Environ 113: 1988-1999. View
- Wu S, Wen J, Liu Q, Dou B, You D, et al. (2015) Estimation of land surface albedo and spatio-temporal variability over Heihe River Basin. Advances in Earth Science 6: 680-690. View
- Gao B, Jia L, Wang T (2014) Derivation of land surface albedo at high resolution by combining HJ-1A/B reflectance observations with MODIS BRDF products. Remote Sens 6: 8966-8985. View
- He T, Liang S, Song D (2015) Analysis of global land surface albedo climatology and spatial-temporal variation during 1981-2010 from multiple satellite products. J Geophys Res Atmos 119: 10281-10298. View
- Hengshui Statistical Bureau (2009) Hengshui statistical yearbook. Beijing: China Statistics Press.
- Hasituya, Chen Z, Wang L, Jiang Z, He L, et al. (2016) Monitoring plasticmulched farmland by Landsat-8 OLI imagery using spectral and textural features. Remote Sens 8: 353. View
- Lu L, Di L, Ye Y (2014) A decision-tree classifier for extracting transparent plastic-mulched landcover from Landsat-5 TM images. IEEE J Sel Top Appl Earth Observ Remote Sens 7: 4548-4558. View
- Zhao G, Li J, Li T, Yue Y, Warner T, et al. (2004) Utilizing Landsat TM imagery to map greenhouses in Qingzhou, Shandong Province, China. Pedosphere 14: 363-369. View
- Hengshui Statistical Bureau (2008) Hengshui statistical yearbook. Beijing: China Statistics Press.