Improving daily rainfall estimation from NDVI using a wavelet transform

Quiroz, Roberto; Yarlequé, Christian; Posadas, Adolfo; Mareš, V.; Immerzeel, Walter W.

doi:10.1016/j.envsoft.2010.07.006

Cited by 71 publications

(59 citation statements)

References 34 publications

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“…Although the response of NDVI to precipitation usually lagged by two or three months [41,44], it was also feasible to downscale monthly, weekly, or daily satellite precipitation datasets by considering lag time. For example, Quiroz et al [45] applied wavelet transform analysis using NDVI data to improve daily rainfall estimates at meteorological stations located on the Andean Plateau. Hunink et al [46] assumed a lag time of one week in the regression models to mimic the response of vegetation to precipitation as well as to estimate spatial distributions of precipitation at a high spatial resolution with a weekly time step in a tropical mountainous region in Ecuador.…”

Section: Introductionmentioning

confidence: 99%

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Zhang

et al. 2018

Remote Sensing

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Abstract:Accurate precipitation data at a high spatial resolution are essential for hydrological, meteorological, and ecological research at regional scales. This study presented a geostatistical downscaling-calibration procedure to derive the high spatial resolution maps of precipitation over a mountainous watershed affected by a monsoon climate. Based on the relationships between precipitation and other environmental variables, such as the Normalized Difference Vegetation Index (NDVI) and digital elevation model (DEM), a regression model with a residual correction method was applied to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 product from coarse resolution (25 km) to fine resolution (1 km). Two methods, geographical difference analysis (GDA) and geographical ratio analysis (GRA), were used to calibrate the downscaled TRMM precipitation data. Monthly 1 km precipitation data were obtained by disaggregating 1 km annual downscaled and calibrated precipitation data using monthly fractions derived from original TRMM data. The downscaled precipitation datasets were validated against ground observations measured by rain gauges. According to the comparison of different regression models and residual interpolation methods, a geographically-weighted regression kriging (GWRK) method was accepted to conduct the downscaling of TRMM data. The downscaled TRMM precipitation data obtained using GWRK described the spatial patterns of precipitation reasonably well at a spatial resolution of 1 km with more detailed information when compared with the original TRMM precipitation. The results of validation indicated that the GRA method provided results with higher accuracy than that of the GDA method. The final annual and monthly downscaled precipitation not only had significant improvement in spatial resolution, but also agreed well with data from the validation rain gauge stations (i.e., R 2 = 0.72, RMSE = 161.0 mm, MAE = 127.5 mm, and Bias = 0.050 for annual downscaled precipitation during 2001 to 2015; and R 2 = 0.91, RMSE = 22.2 mm, MAE = 13.5 mm, and Bias = 0.048 for monthly downscaled precipitation during 2001 to 2015). In general, the downscaling-calibration procedure is useful for complex mountainous areas with insufficient ground gauges.

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Section: Introductionmentioning

confidence: 99%

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Zhang

et al. 2018

Remote Sensing

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“…Unlike sine waves, which are the main functions used in Fourier analysis, wavelets are usually irregular and asymmetric in shape. This property makes a wavelet ideal for analyzing signals that contain sharp changes and discontinuities -a localized signal analysis (Quiroz et al, 2011). Wavelet transforms use different window sizes, which are able to compress and stretch wavelets in different scales or widths; these are then used to decompose a time series (Santos et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008)

Nalley¹,

Adamowski²,

Khalil³

2012

Journal of Hydrology

249

149

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“…Due to the lag time occurring in the response of vegetation to precipitation (Quiroz et al 2011;Duan and Bastiaanssen 2013), the monthly precipitation was not directly downscaled using the NDVI and CON using the RMRC method. In this study, the monthly downscaled precipitation was derived from the annual downscaled Figure 11).…”

Section: Monthly Resultsmentioning

confidence: 99%

A methodological approach for spatial downscaling of TRMM precipitation data in North China

Zheng

Zhu

2014

International Journal of Remote Sensing

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Accurate precipitation data with high spatial resolution are crucial for many applications in water and land management. Tropical Rainfall Monitoring Mission (TRMM) data, with accurate, high spatial resolution are crucial for improving our understanding of temporal and spatial variations of precipitation. However, when used in the Three-North Shelter Forest Programme of China, the spatial resolution of TRMM data is too coarse. In this study, we presented a hybrid method, i.e. a regression model with residual correction method, for downscaling annual TRMM 3B43 from 0.25°to 1 km grids from 2000 to 2009. The regression model was applied to construct the relationship among TRMM 3B43 data, continentality (CON), and the normalized difference vegetation index (NDVI) under five different scales (0.25°, 0.50°, 0.75°, 1.00°, and 1.25°). In the residual correction, three spatial interpolation techniques, i.e. inverse distance weighting (IDW), ordinary kriging, and tension spline, were employed. The 1 km monthly precipitation was disaggregated from 1 km annual precipitation by using monthly fractions. Analysis shows that (1) CON was a good variable for precipitation modelling at large-scale regions; (2) the optimum relationship between precipitation, NDVI, and CON was found at a scale of 1.25°; (3) the most feasible option for residual correction was IDW; and (4) the final annual/monthly downscaled precipitation (1 km) not only improved the spatial resolution but also agreed well with data from 220 rain gauge stations (average R 2 = 0.82, slope = 1.09, RRMSE = 18.30%, and RMSE = 51.91 mm for annual downscaled precipitation; average R 2 = 0.41, slope = 0.79, RRMSE = 76.88%, and RMSE = 15.09 mm for monthly downscaled precipitation).

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Improving daily rainfall estimation from NDVI using a wavelet transform

Cited by 71 publications

References 34 publications

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008)

A methodological approach for spatial downscaling of TRMM precipitation data in North China

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