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Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements

11 pagesPublished: November 4, 2019


Since the network of rainfall gauges and ground radars is generally not dense enough, satellite data have been used to estimate Precipitation (P). These data have the ability to capture the spatial variability pattern of the parameter, but are often inaccurate in relation to the value of the field measured parameter. Therefore, geostatistical methods were evaluated to improve the spatial representativeness of field measurements (FM) and satellite estimates. The work has been made for a hydrological sub region in the Mexican tropic. The geostatistical methods used to interpolate P-FM were ordinary kriging (KO), universal kriging (KU) and regression kriging (RK) as well as the Inverse Distance Weighted (IDW) mechanical interpolator for comparison purposes. Furthermore, the values at the pixel centers of the Tropical Rainfall Monitoring Mission (TRMM) images were interpolated using OK and evaluated using leave-one-out cross validation (LOO-CV). The best LOO-CV evaluated method consisted of the RK interpolation of the point FM taking as auxiliary variable the OK interpolation of the TRMM cell centers. It is concluded that the geostatistical integration between rainfall estimates from satellite data and FM data is promising because satellite information has the ability to capture spatial variability and the point FM add accuracy to the results. These characteristics combined can produce a P product useful for modeling activities and environmental management.

Keyphrases: Bajo Grijalva Basin, Regression Kriging, TRMM

In: Oscar S. Siordia, José Luis Silván Cárdenas, Alejandro Molina-Villegas, Gandhi Hernandez, Pablo Lopez-Ramirez, Rodrigo Tapia-McClung, Karime González Zuccolotto and Mario Chirinos Colunga (editors). Proceedings of the 1st International Conference on Geospatial Information Sciences, vol 13, pages 1--11

BibTeX entry
  author    = {Felipe-Omar Tapia-Silva},
  title     = {Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements},
  booktitle = {Proceedings of the 1st International Conference on Geospatial Information Sciences},
  editor    = {Oscar S. Siordia and Jos\textbackslash{}'e Luis Silv\textbackslash{}'an C\textbackslash{}'ardenas and Alejandro Molina-Villegas and Gandhi Hernandez and Pablo Lopez-Ramirez and Rodrigo Tapia-McClung and Karime Gonz\textbackslash{}'alez Zuccolotto and Mario Chirinos Colunga},
  series    = {Kalpa Publications in Computing},
  volume    = {13},
  pages     = {1--11},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2515-1762},
  url       = {},
  doi       = {10.29007/glj1}}
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