Merging Satellite and Gauge-Measured Precipitation Using LightGBM With an Emphasis on Extreme Quantiles

Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data

“…Although this work already produced a large amount of large-scale results on the use of ensemble learning in the context of improving the accuracy of gridded satellite precipitation products, additional benefits could stem from applying extensions of its methodological framework in the future. Perhaps the most notable among these extensions are the ones referring to the daily time scale (or even to finer time scales), which could additionally benefit from a much larger number of ground-based stations with sufficient record lengths (see, e.g., the number of stations in [16,69]) and, thus, could lead to comparisons on an even larger scale. Other notable extensions are those referring to probabilistic predictions, and they could comprise machine and statistical learning algorithms, such as those summarized in the reviews by [31,79].…”

“…The dependent variable is the gauge-measured total monthly precipitation at a point of interest. According to procedures proposed in [16,30,69], we formed the regression settings by finding, separately for the PERSIANN and IMERG grids (see Figure 3a and Figure 3b, respectively), the four closest grid points to each of the geographical locations of the precipitation ground-based stations (see Figure 2) and by computing the respective distances d i , i = 1, 2, 3 and 4 (in meters). We also indexed these four grid points S i , i = 1, 2, 3 and 4, according to their distance from the stations, where d 1 < d 2 < d 3 < d 4 (see Figure 4).…”

“…Besides, the importance of the more general problem of merging gridded satellite and gauge-measured data for earth observation is well recognised, as proven by both the number and the diversity of the relevant studies (see, e.g., [13][14][15]). Moreover, as noted in [16], even products that already rely on both gridded satellite and gauge-measured precipitation data could be further improved in terms of their accuracy by using gaugemeasured precipitation data in post-processing frameworks. Therefore, in what follows, data from such products and data from purely satellite products will not be explicitly distinguished, as the focus herein will be on their merging with gauge-measured data.…”

“…Machine learning regression algorithms ( [17][18][19]) are regularly used for blending multiple precipitation datasets with differences in terms of spatial density and accuracy (see the relevant reviews by [20,21], as well as examples of such studies in [16,[22][23][24][25][26][27][28][29][30]). In such spatial downscaling (and, more generally, spatial interpolation) settings, satellite precipitation data and topography factors are the predictor variables, and gauged-measured precipitation data are the dependent variables.…”

Comparison between Satellite Derived Solar-Induced Chlorophyll Fluorescence, NDVI and kNDVI in Detecting Water Stress for Dense Vegetation across Southern China

“…It demonstrates substantial superiority over widely used machine learning techniques, such as Random Forest, in aspects of training speed, memory consumption, and predictive accuracy. These attributes render LightGBM particularly adept for processing large datasets while ensuring robust generalization capabilities and stability [31]. In this study, LightGBM is used to examine the nonlinear interactions between various vegetation indices (i.e., SIF, NDVI and kNDVI) and a range of environmental factors, including precipitation, soil moisture, VPD, solar radiation (Srad), and monthly average temperature.…”