{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T22:15:30Z","timestamp":1778796930353,"version":"3.51.4"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,13]],"date-time":"2017-04-13T00:00:00Z","timestamp":1492041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The new IMERG and GSMaP-v6 satellite rainfall estimation (SRE) products from the Global Precipitation Monitoring (GPM) mission have been available since January 2015. With a finer grid box of 0.1\u00b0, these products should provide more detailed information than their latest widely-adapted (relatively coarser spatial scale, 0.25\u00b0) counterpart. Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) assessment is done by comparing their rainfall estimations with 247 rainfall gauges from 2014 to 2016 in Bolivia. The comparisons were done on annual, monthly and daily temporal scales over the three main national watersheds (Amazon, La Plata and TDPS), for both wet and dry seasons to assess the seasonal variability and according to different slope classes to assess the topographic influence on SREs. To observe the potential enhancement in rainfall estimates brought by these two recently released products, the widely-used TRMM Multi-satellite Precipitation Analysis (TMPA) product is also considered in the analysis. The performances of all the products increase during the wet season. Slightly less accurate than TMPA, IMERG can almost achieve its main objective, which is to ensure TMPA rainfall measurements, while enhancing the discretization of rainy and non-rainy days. It also provides the most accurate estimates among all products over the Altiplano arid region. GSMaP-v6 is the least accurate product over the region and tends to underestimate rainfall over the Amazon and La Plata regions. Over the Amazon and La Plata region, SRE potentiality is related to topographic features with the highest bias observed over high slope regions. Over the TDPS watershed, the high rainfall spatial variability with marked wet and arid regions is the main factor influencing SREs.<\/jats:p>","DOI":"10.3390\/rs9040369","type":"journal-article","created":{"date-parts":[[2017,4,13]],"date-time":"2017-04-13T10:55:44Z","timestamp":1492080944000},"page":"369","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Comparative Assessments of the Latest GPM Mission\u2019s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds"],"prefix":"10.3390","volume":"9","author":[{"given":"Fr\u00e9d\u00e9ric","family":"Satg\u00e9","sequence":"first","affiliation":[{"name":"Instituto de Geoci\u00eancia (IG), Universidade de Bras\u00edlia, 70910-900 Bras\u00edlia-DF, Brasil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alvaro","family":"Xavier","sequence":"additional","affiliation":[{"name":"Instituto de Geoci\u00eancia (IG), Universidade de Bras\u00edlia, 70910-900 Bras\u00edlia-DF, Brasil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramiro","family":"Pillco Zol\u00e1","sequence":"additional","affiliation":[{"name":"Instituto de Hidr\u00e1ulica e Hidrolog\u00eda (IHH), Universidad Mayor de San Andr\u00e9s, La Paz, Bolivia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yawar","family":"Hussain","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia Civil e Ambiental, Universidade de Brasilia, 70910-900 Bras\u00edlia-DF, Brasil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4155-6764","authenticated-orcid":false,"given":"Franck","family":"Timouk","sequence":"additional","affiliation":[{"name":"G\u00e9osciences Environnement Toulouse (GET) (UMR 5563, IRD, CNRS), Universit\u00e9 Paul Sabatier, 31062 Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00e9r\u00e9mie","family":"Garnier","sequence":"additional","affiliation":[{"name":"Instituto de Geoci\u00eancia (IG), Universidade de Bras\u00edlia, 70910-900 Bras\u00edlia-DF, Brasil"},{"name":"Mixed Laboratory International, Observatory for Environmental Change (LMI-OCE), IRD\/UnB, Campus Darcy Ribeiro, 70910-900 Bras\u00edlia-DF, Brasil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3950-4041","authenticated-orcid":false,"given":"Marie-Paule","family":"Bonnet","sequence":"additional","affiliation":[{"name":"Mixed Laboratory International, Observatory for Environmental Change (LMI-OCE), IRD\/UnB, Campus Darcy Ribeiro, 70910-900 Bras\u00edlia-DF, Brasil"},{"name":"Espace D\u00e9veloppement (ESPACE-DEV) (UMR 228, IRD), 34093 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1175\/JHM-D-15-0068.1","article-title":"Comparison of Integrated Multisatellite Retrievals for GPM ( IMERG ) and TRMM Multisatellite Precipitation Analysis ( TMPA ) Monthly Precipitation Products: Initial Results","volume":"17","author":"Liu","year":"2016","journal-title":"J. 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