{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:20:31Z","timestamp":1760235631737,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T00:00:00Z","timestamp":1631664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010447","name":"Kementerian Riset, Teknologi dan Pendidikan Tinggi","doi-asserted-by":"publisher","award":["3\/E1\/KP.PTNBH\/2021"],"award-info":[{"award-number":["3\/E1\/KP.PTNBH\/2021"]}],"id":[{"id":"10.13039\/501100010447","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity.<\/jats:p>","DOI":"10.3390\/rs13183693","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T12:00:44Z","timestamp":1631707244000},"page":"3693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Time Varying Spatial Downscaling of Satellite-Based Drought Index"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8062-462X","authenticated-orcid":false,"given":"Hone-Jay","family":"Chu","sequence":"first","affiliation":[{"name":"Department of Geomatics, National Cheng Kung University, Tainan 701, Taiwan"}]},{"given":"Regita Faridatunisa","family":"Wijayanti","sequence":"additional","affiliation":[{"name":"Department of Geomatics, National Cheng Kung University, Tainan 701, Taiwan"},{"name":"Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2663-9016","authenticated-orcid":false,"given":"Lalu Muhamad","family":"Jaelani","sequence":"additional","affiliation":[{"name":"Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4915-1075","authenticated-orcid":false,"given":"Hui-Ping","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Chung Hsing University, Taichung 402, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1111\/j.1752-1688.1998.tb05964.x","article-title":"Comparing the palmer drought index and the standardized precipitation index","volume":"34","author":"Guttman","year":"1998","journal-title":"JAWRA J. Am. Water Resour. Assoc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1002\/wat2.1085","article-title":"Hydrological drought explained","volume":"2","year":"2015","journal-title":"Wiley Interdiscip. Rev. Water"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3119","DOI":"10.1007\/s11269-018-1979-4","article-title":"Drought detection of regional nonparametric standardized groundwater index","volume":"32","author":"Chu","year":"2018","journal-title":"Water Resour. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agrformet.2005.07.012","article-title":"Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring","volume":"133","author":"Narasimhan","year":"2005","journal-title":"Agric. For. Meteorol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7153","DOI":"10.1007\/s10661-011-2487-7","article-title":"Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra\/MODIS satellite data","volume":"184","author":"Patel","year":"2012","journal-title":"Environ. Monit. Assess."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1002\/2014RG000456","article-title":"Remote sensing of drought: Progress, challenges and opportunities","volume":"53","author":"AghaKouchak","year":"2015","journal-title":"Rev. Geophys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.rse.2017.06.014","article-title":"A drought event composite analysis using satellite remote-sensing based soil moisture","volume":"203","author":"Zscheischler","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/0143116031000115328","article-title":"Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA","volume":"25","author":"Wan","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","first-page":"106","article-title":"Application of the anomaly vegetation index to monitoring heavy drought in 1992","volume":"9","author":"Weiying","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1029\/2002EO000382","article-title":"World droughts in the new millennium from AVHRR-based vegetation health indices","volume":"83","author":"Kogan","year":"2002","journal-title":"Eos"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/S2095-3119(15)61302-8","article-title":"Comparison between TVDI and CWSI for drought monitoring in the Guanzhong Plain, China","volume":"16","author":"Bai","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"L14703","DOI":"10.1029\/2005GL022760","article-title":"Relationships between precipitation and surface temperature","volume":"32","author":"Trenberth","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.1016\/j.rse.2011.06.009","article-title":"A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China","volume":"115","author":"Jia","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1659\/MRD-JOURNAL-D-14-00119.1","article-title":"Spatial downscaling of monthly TRMM precipitation based on EVI and other geospatial variables over the Tibetan Plateau from 2001 to 2012","volume":"35","author":"Shi","year":"2015","journal-title":"Mt. Res. Dev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5849","DOI":"10.3390\/rs70505849","article-title":"Mapping annual precipitation across Mainland China in the period 2001\u20132010 from TRMM3B43 product using spatial downscaling approach","volume":"7","author":"Shi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"525","DOI":"10.3389\/feart.2020.536337","article-title":"Comparison of different methods for spatial downscaling of GPM IMERG V06B satellite precipitation product over a typical arid to semi-arid area","volume":"8","author":"Chen","year":"2020","journal-title":"Front. Earth Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bai, L., Shi, C., Li, L., Yang, Y., and Wu, J. (2018). Accuracy of CHIRPS satellite-rainfall products over Mainland China. Remote Sens., 10.","DOI":"10.3390\/rs10030362"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3943","DOI":"10.1080\/01431161.2017.1312031","article-title":"Downscaling CHIRPS precipitation data: An artificial neural network modelling approach","volume":"38","author":"Retalis","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"126638","DOI":"10.1016\/j.jhydrol.2021.126638","article-title":"Integrated meteorological drought monitoring framework using multi-sensor and multi-temporal earth observation datasets and machine learning algorithms: A case study of central India","volume":"601","author":"Neeti","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"12619","DOI":"10.1038\/s41598-017-12674-z","article-title":"Causes and predictability of the negative Indian Ocean dipole and its impact on La Ni\u00f1a during 2016","volume":"7","author":"Lim","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"012007","DOI":"10.1088\/1755-1315\/530\/1\/012007","article-title":"Effect of ENSO and IOD on the variability of sea surface temperature (SST) in java sea","volume":"530","author":"Dewi","year":"2020","journal-title":"IOP Conf. Series Earth Environ. Sci."},{"key":"ref_22","first-page":"012029","article-title":"Taqyyudin identification of dry areas on agricultural land using normalized difference drought index in magetan regency","volume":"540","author":"Rismayatika","year":"2020","journal-title":"IOP Conf. Series: Earth Environ. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1175\/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2","article-title":"The status of the tropical rainfall measuring mission (TRMM) after two years in orbit","volume":"39","author":"Kummerow","year":"2000","journal-title":"J. Appl. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.advwatres.2014.11.012","article-title":"A generalized framework for deriving nonparametric standardized drought indicators","volume":"76","author":"Farahmand","year":"2015","journal-title":"Adv. Water Resour."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1029\/JZ068i003p00813","article-title":"A plotting rule for extreme probability paper","volume":"68","author":"Gringorten","year":"1963","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1007\/s10040-020-02211-0","article-title":"Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations","volume":"28","author":"Ali","year":"2020","journal-title":"Hydrogeol. J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chu, H.-J., He, Y.-C., Chusnah, W., Jaelani, L., and Chang, C.-H. (2021). Multi-reservoir water quality mapping from remote sensing using spatial regression. Sustainability, 13.","DOI":"10.3390\/su13116416"},{"key":"ref_28","unstructured":"(2021, August 15). Climate Risk Profile: Indonesia 2021. The World Bank Group and Asian Development Bank. Available online: https:\/\/climateknowledgeportal.worldbank.org\/sites\/default\/files\/2021-05\/15504-Indonesia%20Country%20Profile-WEB_0.pdf."},{"key":"ref_29","unstructured":"(2021, September 06). Historical El Nino\/La Nina Episodes (1950\u2013Present), Available online: https:\/\/origin.cpc.ncep.noaa.gov\/products\/analysis_monitoring\/ensostuff\/ONI_v5.php."},{"key":"ref_30","unstructured":"(2021, September 06). Meet ENSO\u2019s neighbor, the Indian Ocean Dipole, Available online: https:\/\/www.climate.gov\/news-features\/blogs\/enso\/meet-enso%E2%80%99s-neighbor-indian-ocean-dipole."},{"key":"ref_31","first-page":"012019","article-title":"Assessment of the Standardized Precipitation Index (SPI) in Tegal City, Central Java, Indonesia","volume":"129","author":"Pramudya","year":"2018","journal-title":"IOP Conf. Series: Earth Environ. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"22079","DOI":"10.1038\/s41598-020-79064-w","article-title":"Spatial calibration and PM2.5 mapping of low-cost air quality sensors","volume":"10","author":"Chu","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8432064","DOI":"10.1155\/2016\/8432064","article-title":"Assessing the added value of dynamical downscaling using the standardized precipitation index","volume":"2016","author":"Bowden","year":"2016","journal-title":"Adv. Meteorol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"83","DOI":"10.20937\/ATM.2015.28.02.02","article-title":"Downscaling standardized precipitation index via model output statistics","volume":"28","author":"Tatli","year":"2015","journal-title":"Atm\u00f3sfera"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.gloplacha.2016.10.015","article-title":"Regional downscaling of Mediterranean droughts under past and future climatic conditions","volume":"151","author":"Hertig","year":"2017","journal-title":"Glob. Planet. Chang."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8217","DOI":"10.1002\/2016WR019034","article-title":"Spatial downscaling of precipitation using adaptable random forests","volume":"52","author":"He","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1175\/JHM-D-19-0289.1","article-title":"Spatial and temporal downscaling of TRMM precipitation with novel algorithms","volume":"21","author":"Zhang","year":"2020","journal-title":"J. Hydrometeorol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"034042","DOI":"10.1088\/1748-9326\/aaafda","article-title":"How well do meteorological indicators represent agricultural and forest drought across Europe?","volume":"13","author":"Bachmair","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s41748-018-0055-9","article-title":"Using drought indices to model the statistical relationships between meteorological and agricultural drought in Raya and its environs, Northern Ethiopia","volume":"2","author":"Gidey","year":"2018","journal-title":"Earth Syst. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1146\/annurev-environ-102017-030136","article-title":"The effects of tropical vegetation on rain-fall","volume":"43","author":"Spracklen","year":"2018","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2018.06.004","article-title":"Spatial downscaling of TRMM precipitation data considering the impacts of macro-geographical factors and local elevation in the Three-River Headwaters Region","volume":"215","author":"Zhang","year":"2018","journal-title":"Remote. Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10451","DOI":"10.1038\/s41598-020-67423-6","article-title":"Correlation analysis of land surface temperature and topo-graphic elements in Hangzhou, China","volume":"10","author":"Peng","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/2016RG000543","article-title":"A review of spatial downscaling of satellite remotely sensed soil moisture","volume":"55","author":"Peng","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e2019JD031946","DOI":"10.1029\/2019JD031946","article-title":"Uncertainties in drought from index and data selection","volume":"125","author":"Hoffmann","year":"2020","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"112028","DOI":"10.1016\/j.jenvman.2021.112028","article-title":"Drought disaster monitoring using MODIS derived index for drought years: A space-based information for ecosystems and environmental conservation","volume":"284","author":"Orimoloye","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s10661-021-09126-7","article-title":"Drought conditions appraisal using geoinformatics and multi-influencing factors","volume":"193","author":"Dyosi","year":"2021","journal-title":"Environ. Monit. Assess."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s11069-019-03569-5","article-title":"The relationship between the normalized difference vegetation index and drought indices in the South Central United States","volume":"96","author":"Bushra","year":"2019","journal-title":"Nat. Hazards"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3693\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:21Z","timestamp":1760166021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,15]]},"references-count":47,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183693"],"URL":"https:\/\/doi.org\/10.3390\/rs13183693","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,9,15]]}}}