nach oben

Earth Science Informatics

Erschienen in:

06.10.2021 | Review Article

A review of downscaling methods of satellite-based precipitation estimates

verfasst von: Arman Abdollahipour, Hassan Ahmadi, Babak Aminnejad

Erschienen in: Earth Science Informatics | Ausgabe 1/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Satellite remote sensing is the main tool for estimating precipitation over areas with sparse rain gauge networks. Accurate gridded precipitation data at high temporal and spatial scales are needed for various studies such as hydrology, climatology, and meteorology. Meanwhile, downscaling of satellite precipitation products is necessary to attain such data because their spatial resolutions are too coarse for use in local region and basin scales or for parameterizing meteorological and hydrological models at a local scale. In recent years, plenty of attempts have been made to improve the resolutions of satellite-based precipitation estimates, and many algorithms have been proposed for this purpose. A review study of existing methods can help improve and develop future precipitation downscaling algorithms. Therefore, in this paper, the existing downscaling methods are reviewed, categorized, and summarized. Also, the performance of these methods in the studied regions is compared, and their advantages and limitations are highlighted. Finally, we concluded by stating the necessary considerations for future studies.

Nächster Artikel What is this article about? Generative summarization with the BERT model in the geosciences domain

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Agam N, Kustas WP, Anderson MC, Li F, Neale CM (2007) A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sens Environ 107(4):545–558. https://doi.org/10.1016/j.rse.2006.10.006CrossRef

Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27(2):93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.xCrossRef

Arshad A, Zhang W, Zhang Z, Wang S, Zhang B, Cheema MJM, Shalamzari MJ (2021) Reconstructing high-resolution gridded precipitation data using an improved downscaling approach over the high altitude mountain regions of Upper Indus Basin (UIB). Sci Total Environ 784:147140. https://doi.org/10.1016/j.scitotenv.2021.147140CrossRef

Atkinson PM (2013) Downscaling in remote sensing. Int J Appl Earth Obs Geoinf 22:106–114. https://doi.org/10.1016/j.jag.2012.04.012CrossRef

Brunsell NA (2006) Characterization of land-surface precipitation feedback regimes with remote sensing. Remote Sens Environ 100(2):200–211. https://doi.org/10.1016/j.rse.2005.10.025CrossRef

Cheema MJM, Bastiaanssen WG (2012) Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin. Int J Remote Sens 33(8):2603–2627. https://doi.org/10.1080/01431161.2011.617397CrossRef

Chen F, Liu Y, Liu Q, Li X (2014) Spatial downscaling of TRMM 3B43 precipitation considering spatial heterogeneity. Int J Remote Sens 35(9):3074–3093. https://doi.org/10.1080/01431161.2014.902550CrossRef

Chen Y, Huang J, Sheng S, Mansaray LR, Liu Z, Wu H, Wang X (2018) A new downscaling-integration framework for high-resolution monthly precipitation estimates: combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data. Remote Sens Environ 214:154–172. https://doi.org/10.1016/j.rse.2018.05.021CrossRef

Chen S, Zhang L, She D, Chen J (2019) Spatial Downscaling of Tropical Rainfall Measuring Mission (TRMM) annual and monthly precipitation data over the middle and lower reaches of the Yangtze River Basin, China. Water 11(3):568. https://doi.org/10.3390/w11030568CrossRef

Chuai XW, Huang XJ, Wang WJ, Bao G (2013) NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998–2007 in Inner Mongolia, China. Int J Climatol 33(7):1696–1706. https://doi.org/10.1002/joc.3543CrossRef

Darand M, Amanollahi J, Zandkarimi S (2017) Evaluation of the performance of TRMM Multi-satellite Precipitation Analysis (TMPA) estimation over Iran. Atmos Res 190:121–127. https://doi.org/10.1016/j.atmosres.2017.02.011CrossRef

Duan Z, Bastiaanssen WGM (2013) First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling–calibration procedure. Remote Sens Environ 131:1–13. https://doi.org/10.1016/j.rse.2012.12.002CrossRef

Duan Z, Liu J, Tuo Y, Chiogna G, Disse M (2016) Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Sci Total Environ 573:1536–1553. https://doi.org/10.1016/j.scitotenv.2016.08.213CrossRef

Fang J, Du J, Xu W, Shi P, Li M, Ming X (2013) Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area. Adv Water Resour 61:42–50. https://doi.org/10.1016/j.advwatres.2013.08.011CrossRef

Foody GM (2003) Geographical weighting as a further refinement to regression modelling: an example focused on the NDVI–rainfall relationship. Remote Sens Environ 88(3):283–293. https://doi.org/10.1016/j.rse.2003.08.004CrossRef

Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2(1):1–21. https://doi.org/10.1038/sdata.2015.66CrossRef

Gao Y, Huang J, Li S, Li S (2012) Spatial pattern of non-stationarity and scale-dependent relationships between NDVI and climatic factors—A case study in Qinghai-Tibet Plateau, China. Ecol Ind 20:170–176. https://doi.org/10.1016/j.ecolind.2012.02.007CrossRef

Gebremichael M, Krajewski WF, Over TM, Takayabu YN, Arkin P, Katayama M (2008) Scaling of tropical rainfall as observed by TRMM precipitation radar. Atmos Res 88(3–4):337–354. https://doi.org/10.1016/j.atmosres.2007.11.028CrossRef

Gerlitz L, Conrad O, Böhner J (2015) Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia-a neural-network-based approach. Earth Syst Dyn 6(1):61. https://doi.org/10.5194/esd-6-61-2015CrossRef

Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228(1–2):113–129. https://doi.org/10.1016/S0022-1694(00)00144-XCrossRef

Goovaerts P (2008) Kriging and semivariogram deconvolution in the presence of irregular geographical units. Math Geosci 40(1):101–128. https://doi.org/10.1007/s11004-007-9129-1CrossRef

Harris P, Fotheringham AS, Crespo R, Charlton M (2010) The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets. Math Geosci 42(6):657–680. https://doi.org/10.1007/s11004-010-9284-7CrossRef

Hong Y, Hsu KL, Sorooshian S, Gao X (2004) Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J Appl Meteorol 43(12):1834–1853. https://doi.org/10.1175/JAM2173.1CrossRef

Hsu KL, Gao X, Sorooshian S, Gupta HV (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol 36(9):1176–1190. https://doi.org/10.1175/1520-0450(1997)036%3c1176:PEFRSI%3e2.0.CO;2CrossRef

Hsu KL, Gupta HV, Gao X, Sorooshian S (1999) Estimation of physical variables from multichannel remotely sensed imagery using a neural network: application to rainfall estimation. Water Resour Res 35(5):1605–1618. https://doi.org/10.1029/1999WR900032CrossRef

Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55. https://doi.org/10.1175/JHM560.1CrossRef

Huffman GJ, Bolvin DT, Braithwaite D, Hsu K, Joyce R, Xie P, Yoo SH (2015) NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version, 4, p.26.

Hutengs C, Vohland M (2016) Downscaling land surface temperatures at regional scales with random forest regression. Remote Sens Environ 178:127–141. https://doi.org/10.1016/j.rse.2016.03.006CrossRef

Immerzeel WW, Rutten MM, Droogers P (2009) Spatial downscaling of TRMM precipitation using vegetative response on the Iberian Peninsula. Remote Sens Environ 113(2):362–370. https://doi.org/10.1016/j.rse.2008.10.004CrossRef

Jia S, Zhu W, Lű A, Yan T (2011) A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China. Remote Sens Environ 115(12):3069–3079. https://doi.org/10.1016/j.rse.2011.06.009CrossRef

Jin Y, Ge Y, Wang J, Heuvelink G, Wang L (2018) Geographically weighted area-to-point regression kriging for spatial downscaling in remote sensing. Remote Sens 10(4):579. https://doi.org/10.3390/rs10040579CrossRef

Jing W, Yang Y, Yue X, Zhao X (2016a) A spatial downscaling algorithm for satellite-based precipitation over the Tibetan plateau based on NDVI, DEM, and land surface temperature. Remote Sens 8(8):655. https://doi.org/10.3390/rs8080655CrossRef

Jing W, Yang Y, Yue X, Zhao X (2016b) A comparison of different regression algorithms for downscaling monthly satellite-based precipitation over North China. Remote Sens 8(10):835. https://doi.org/10.3390/rs8100835CrossRef

Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5(3):487–503. https://doi.org/10.1175/1525-7541(2004)005%3c0487:CAMTPG%3e2.0.CO;2CrossRef

Ke Y, Im J, Park S, Gong H (2016) Downscaling of MODIS One kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sens 8(3):215. https://doi.org/10.3390/rs8030215CrossRef

Kizza M, Westerberg I, Rodhe A, Ntale HK (2012) Estimating areal rainfall over Lake Victoria and its basin using ground-based and satellite data. J Hydrol 464:401–411. https://doi.org/10.1016/j.jhydrol.2012.07.024CrossRef

Kubota T, Shige S, Hashizume H, Aonashi K, Takahashi N, Seto S, Hirose M, Takayabu YN, Ushio T, Nakagawa K, Iwanami K (2007) Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation. IEEE Trans Geosci Remote Sens 45(7):2259–2275. https://doi.org/10.1109/TGRS.2007.895337CrossRef

Kuhn M, Johnson K (2013) Applied predictive modeling, vol 26. Springer, New YorkCrossRef

Kumar S (2015) Estimating spatial distribution of soil organic carbon for the Midwestern United States using historical database. Chemosphere 127:49–57. https://doi.org/10.1016/j.chemosphere.2014.12.027CrossRef

Kumar S, Lal R, Liu D (2012) A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma 189:627–634. https://doi.org/10.1016/j.geoderma.2012.05.022CrossRef

Kustas WP, Norman JM, Anderson MC, French AN (2003) Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship. Remote Sens Environ 85(4):429–440. https://doi.org/10.1016/S0034-4257(03)00036-1CrossRef

Kwak GH, Park NW, Kyriakidis P (2018) Development of an R-based spatial downscaling tool to predict fine scale information from coarse scale satellite products. J Remote Sens. https://doi.org/10.7780/kjrs.2018.34.1.6CrossRef

Kyriakidis PC (2004) A geostatistical framework for area-to-point spatial interpolation. Geogr Anal 36(3):259–289. https://doi.org/10.1111/j.1538-4632.2004.tb01135.xCrossRef

Kyriakidis PC, Kim J, Miller NL (2001) Geostatistical mapping of precipitation from rain gauge data using atmospheric and terrain characteristics. J Appl Meteorol 40(11):1855–1877. https://doi.org/10.1175/1520-0450(2001)040%3c1855:GMOPFR%3e2.0.CO;2CrossRef

Levine N (2004) CrimeStat III: a spatial statistics program for the analysis of crime incident locations (version 3.0). Houston (TX): Ned Levine & Associates/Washington, DC: National Institute of Justice.

Li M, Shao Q (2010) An improved statistical approach to merge satellite rainfall estimates and rain gauge data. J Hydrol 385(1–4):51–64. https://doi.org/10.1016/j.jhydrol.2010.01.023CrossRef

Li BG, Tao S, Dawson RW (2002) Relations between AVHRR NDVI and ecoclimatic parameters in China. Int J Remote Sens 23(5):989–999. https://doi.org/10.1080/014311602753474192CrossRef

Li Y, Zhang Y, He D, Luo X, Ji X (2019) Spatial downscaling of the tropical rainfall measuring mission precipitation using geographically weighted regression Kriging over the Lancang River Basin, China. Chin Geogr Sci 29(3):446–462. https://doi.org/10.1007/s11769-019-1033-3CrossRef

Liang X, Xie Z, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res 99(14):415–514. https://doi.org/10.1029/94JD00483CrossRef

Liu Y, Journel AG (2009) A package for geostatistical integration of coarse and fine scale data. Comput Geosci 35(3):527–547. https://doi.org/10.1016/j.cageo.2007.12.015CrossRef

Lloyd CD (2005) Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain. J Hydrol 308(1–4):128–150. https://doi.org/10.1016/j.jhydrol.2004.10.026CrossRef

Lu X, Tang G, Wang X, Liu Y, Wei M, Zhang Y (2020) The development of a two-step merging and downscaling method for satellite precipitation products. Remote Sens 12(3):398. https://doi.org/10.3390/rs12030398CrossRef

Ly S, Charles C, Degré A (2013) Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale: a review. Biotechnol Agron Soc Environ 17(2):392–406

Ma Z, Shi Z, Zhou Y, Xu J, Yu W, Yang Y (2017a) A spatial data mining algorithm for downscaling TMPA 3B43 V7 data over the Qinghai-Tibet Plateau with the effects of systematic anomalies removed. Remote Sens Environ 200:378–395. https://doi.org/10.1016/j.rse.2017.08.023CrossRef

Ma Z, Zhou Y, Hu B, Liang Z, Shi Z (2017b) Downscaling annual precipitation with TMPA and land surface characteristics in China. Int J Climatol 37(15):5107–5119. https://doi.org/10.1002/joc.5148CrossRef

Ma Z, He K, Tan X, Xu J, Fang W, He Y, Hong Y (2018a) Comparisons of spatially downscaling TMPA and IMERG over the Tibetan Plateau. Remote Sensing 10(12):1883. https://doi.org/10.3390/rs10121883CrossRef

Ma Z, Tan X, Yang Y, Chen X, Kan G, Ji X, Lu H, Long J, Cui Y, Hong Y (2018b) The first comparisons of IMERG and the downscaled results based on IMERG in hydrological utility over the Ganjiang River Basin. Water 10(10):1392. https://doi.org/10.3390/w10101392CrossRef

Ma Z, He K, Tan X, Liu Y, Lu H, Shi Z (2019) A new approach for obtaining precipitation estimates with a finer spatial resolution on a daily scale based on TMPA V7 data over the Tibetan Plateau. Int J Remote Sens 40(22):8465–8483. https://doi.org/10.1080/01431161.2019.1612118CrossRef

Ma Z, Xu J, He K, Han X, Ji Q, Wang T, Xiong W, Hong Y (2020) An updated moving window algorithm for hourly-scale satellite precipitation downscaling: a case study in the Southeast Coast of China. J Hydrol 581:124378. https://doi.org/10.1016/j.jhydrol.2019.124378CrossRef

Mahesh C, Prakash S, Sathiyamoorthy V, Gairola RM (2011) Artificial neural network based microwave precipitation estimation using scattering index and polarization corrected temperature. Atmos Res 102(3):358–364. https://doi.org/10.1016/j.atmosres.2011.09.003CrossRef

McVicar TR, Van Niel TG, Li L, Hutchinson MF, Mu X, Liu Z (2007) Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences. J Hydrol 338(3–4):196–220. https://doi.org/10.1016/j.jhydrol.2007.02.018CrossRef

Merlin O, Walker JP, Chehbouni A, Kerr Y (2008) Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency. Remote Sens Environ 112(10):3935–3946. https://doi.org/10.1016/j.rse.2008.06.012GetCrossRef

Merlin O, Al Bitar A, Walker JP, Kerr Y (2009) A sequential model for disaggregating near-surface soil moisture observations using multi-resolution thermal sensors. Remote Sens Environ 113(10):2275–2284. https://doi.org/10.1016/j.rse.2009.06.012CrossRef

Merlin O, Al Bitar A, Walker JP, Kerr Y (2010a) An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data. Remote Sens Environ 114(10):2305–2316. https://doi.org/10.1016/j.rse.2010.05.007CrossRef

Merlin O, Duchemin B, Hagolle O, Jacob F, Coudert B, Chehbouni G, Dedieu G, Garatuza J, Kerr Y (2010b) Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images. Remote Sens Environ 114(11):2500–2512. https://doi.org/10.1016/j.rse.2010.05.025CrossRef

Nastos PT, Kapsomenakis J, Philandras KM (2016) Evaluation of the TRMM 3B43 gridded precipitation estimates over Greece. Atmos Res 169:497–514. https://doi.org/10.1016/j.atmosres.2015.08.008CrossRef

Park NW (2013) Spatial downscaling of TRMM precipitation using geostatistics and fine scale environmental variables. Adv Meteorol. https://doi.org/10.1155/2013/237126CrossRef

Park NW, Hong S, Kyriakidis PC, Lee W, Lyu SJ (2016) Geostatistical downscaling of AMSR2 precipitation with COMS infrared observations. Int J Remote Sens 37(16):3858–3869. https://doi.org/10.1080/01431161.2016.1204031CrossRef

Park NW, Kyriakidis PC, Hong S (2017) Geostatistical integration of coarse resolution satellite precipitation products and rain gauge data to map precipitation at fine spatial resolutions. Remote Sens 9(3):255. https://doi.org/10.3390/rs9030255CrossRef

Piles M, Sánchez N, Vall-llossera M, Camps A, Martínez-Fernández J, Martínez J, González-Gambau V (2014) A downscaling approach for SMOS land observations: evaluation of high-resolution soil moisture maps over the Iberian Peninsula. IEEE J Select Topics Appl Earth Obs Remote Sens 7(9):3845–3857. https://doi.org/10.1109/JSTARS.2014.2325398CrossRef

Pombo S, de Oliveira RP (2015) Evaluation of extreme precipitation estimates from TRMM in Angola. J Hydrol 523:663–679. https://doi.org/10.1016/j.jhydrol.2015.02.014CrossRef

Quinlan JR (1987) Simplifying decision trees. Int J Man Mach Stud 27(3):221–234. https://doi.org/10.1016/S0020-7373(87)80053-6CrossRef

Quiroz R, Yarlequé C, Posadas A, Mares V, Immerzeel WW (2011) Improving daily rainfall estimation from NDVI using a wavelet transform. Environ Model Softw 26(2):201–209. https://doi.org/10.1016/j.envsoft.2010.07.006CrossRef

Retalis A, Tymvios F, Katsanos D, Michaelides S (2017) Downscaling CHIRPS precipitation data: an artificial neural network modelling approach. Int J Remote Sens 38(13):3943–3959. https://doi.org/10.1080/01431161.2017.1312031CrossRef

Richard Y, Poccard IJIJORS (1998) A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa. Int J Remote Sens 19(15):2907–2920. https://doi.org/10.1080/014311698214343CrossRef

Schultz PA, Halpert MS (1995) Global analysis of the relationships among a vegetation index, precipitation and land surface temperature. Remote Sens 16(15):2755–2777. https://doi.org/10.1080/01431169508954590CrossRef

Sharifi E, Saghafian B, Steinacker R (2019) Downscaling satellite precipitation estimates with multiple linear regression, artificial neural networks, and spline interpolation techniques. J Geophys Res 124(2):789–805. https://doi.org/10.1029/2018JD028795CrossRef

Shi Y, Song L (2015) Spatial downscaling of monthly TRMM precipitation based on EVI and other geospatial variables over the Tibetan Plateau from 2001 to 2012. Mt Res Dev 35(2):180–194. https://doi.org/10.1659/MRD-JOURNAL-D-14-00119.1CrossRef

Smith MB, Koren VI, Zhang Z, Reed SM, Pan JJ, Moreda F (2004) Runoff response to spatial variability in precipitation: an analysis of observed data. J Hydrol 298(1–4):267–286. https://doi.org/10.1016/j.jhydrol.2004.03.039CrossRef

Sokol Z, Bližňák V (2009) Areal distribution and precipitation–altitude relationship of heavy short-term precipitation in the Czech Republic in the warm part of the year. Atmos Res 94(4):652–662. https://doi.org/10.1016/j.atmosres.2009.03.001CrossRef

Sorooshian S, Hsu KL, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull Am Meteor Soc 81(9):2035–2046. https://doi.org/10.1175/1520-0477(2000)081%3c2035:EOPSSE%3e2.3.CO;2CrossRef

Tao K, Barros AP (2010) Using fractal downscaling of satellite precipitation products for hydrometeorological applications. J Atmos Oceanic Tech 27(3):409–427. https://doi.org/10.1175/2009JTECHA1219.1CrossRef

Verlinde J (2011) TRMM rainfall data downscaling in the Pangani Basin in Tanzania. Master Sci Thesis Delft Univ Technol 1:1–72

Vila DA, De Goncalves LGG, Toll DL, Rozante JR (2009) Statistical evaluation of combined daily gauge observations and rainfall satellite estimates over continental South America. J Hydrometeorol 10(2):533–543. https://doi.org/10.1175/2008JHM1048.1CrossRef

Wu C, Chen JM (2012) The use of precipitation intensity in estimating gross primary production in four northern grasslands. J Arid Environ 82:11–18. https://doi.org/10.1016/j.jaridenv.2012.02.014CrossRef

Wu C, Gonsamo A, Gough CM, Chen JM, Xu S (2014) Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS. Remote Sens Environ 147:79–88. https://doi.org/10.1016/j.rse.2014.03.001CrossRef

Xie P, Arkin PA (1997) Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteor Soc 78(11):2539–2558. https://doi.org/10.1175/1520-0477(1997)078%3c2539:GPAYMA%3e2.0.CO;2CrossRef

Xie P, Janowiak JE, Arkin PA, Adler R, Gruber A, Ferraro R, Huffman GJ, Curtis S (2003) GPCP pentad precipitation analyses: An experimental dataset based on gauge observations and satellite estimates. J Clim 16(13):2197–2214. https://doi.org/10.1175/2769.1CrossRef

Xie S, Liu Y, Yao F (2020) Spatial downscaling of TRMM precipitation using an optimal regression model with NDVI in inner Mongolia, China. Water Resourc 47(6):1054–1064. https://doi.org/10.1134/S0097807820060123CrossRef

Xu S, Wu C, Wang L, Gonsamo A, Shen Y, Niu Z (2015) A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics. Remote Sens Environ 162:119–140. https://doi.org/10.1016/j.rse.2015.02.024CrossRef

Yoo C, Im J, Park S, Cho D. Thermal characteristics of Daegu using land cover data and satellite-derived surface temperature downscaled based on machine learning. Korean Journal of Remote Sensing. 2017;33(6):1101–1118. Doi: https://doi.org/10.7780/kjrs.2017.33.6.2.6

Zhan W, Chen Y, Wang J, Zhou J, Quan J, Liu W, Li J (2012) Downscaling land surface temperatures with multi-spectral and multi-resolution images. Int J Appl Earth Obs Geoinf 18:23–36. https://doi.org/10.1016/j.jag.2012.01.003CrossRef

Zhan W, Chen Y, Zhou J, Wang J, Liu W, Voogt J, Zhu X, Quan J, Li J (2013) Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats. Remote Sens Environ 131:119–139. https://doi.org/10.1016/j.rse.2012.12.014CrossRef

Zhan C, Han J, Hu S, Liu L, Dong Y (2018) Spatial downscaling of GPM annual and monthly precipitation using regression-based algorithms in a mountainous area. Adv Meteorol. https://doi.org/10.1155/2018/1506017CrossRef

Zhang X, Hu Y, Zhuang D, Qi Y, Ma X (2009) NDVI spatial pattern and its differentiation on the Mongolian Plateau. J Geog Sci 19(4):403–415. https://doi.org/10.1007/s11442-009-0403-7CrossRef

Zhang T, Li B, Yuan Y, Gao X, Sun Q, Xu L, Jiang Y (2018) Spatial downscaling of TRMM precipitation data considering the impacts of macro-geographical factors and local elevation in the Three-River Headwaters Region. Remote Sens Environ 215:109–127. https://doi.org/10.1016/j.rse.2018.06.004CrossRef

Zhang H, Loáiciga HA, Ha D, Du Q (2020) Spatial and temporal downscaling of TRMM precipitation with novel algorithms. J Hydrometeorol 21(6):1259–1278. https://doi.org/10.1175/JHM-D-19-0289.1CrossRef

Zhao N (2021) An efficient downscaling scheme for high-resolution precipitation estimates over a high mountainous watershed. Remote Sens 13(2):234. https://doi.org/10.3390/rs13020234CrossRef

Zhao X, Jing W, Zhang P (2017) Mapping fine spatial resolution precipitation from TRMM precipitation datasets using an ensemble learning method and MODIS optical products in China. Sustainability 9(10):1912. https://doi.org/10.3390/su9101912CrossRef

Titel: A review of downscaling methods of satellite-based precipitation estimates
verfasst von: Arman Abdollahipour
Hassan Ahmadi
Babak Aminnejad
Publikationsdatum: 06.10.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 1/2022
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-021-00669-4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2022

Analysis of Influence Mechanism of Spatial Distribution of Incoming Solar Radiation Based on DEM

A novel approach for predicting the tc center of remotely sensed images using pso based density matrix

Geospatial quantitative analysis of the Aral Sea Shoreline changes using RS and GIS techniques

Object detection in satellite images by faster R-CNN incorporated with enhanced ROI pooling (FrRNet-ERoI) framework

Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach

Analysis of geomorphologic features of the circular structure in the south of Lufeng Dinosaur Valley

Premium Partner