Normalized Difference Vegetation Index (NDVI) is often used in several applications such as vegetation monitoring, crop growth modelling, deforestation assessment etc. One of the medium resolution satellite images used to derive this NDVI image is Landsat. But Landsat revisit time is 16 days thus the availability of images is limited. Secondly Landsat images often affected with clouds and shadow covers that further restricts the spatial continuity of NDVI images during the Landsat overpass times. So this Google Earth Engine based JavaScript tool fills the spatial and temporal data gaps in NDVI image. We have used Harmonic models to predict the NDVI at the data gaps. Validation of this model predicted NDVI has been extensively discussed in our published paper (Mohanasundaram et al., 2023). This following GEE script works on any machine with GEE account without any additional data.

Click here for the Google Earth Engine (GEE) script

Note: The default script fills the missing NDVI over a part of Northern Thailand for September 2020. Please change the date and area of interest boundary as per your requirement.

Reference paper to cite:
Mohanasundaram, S., Baghel, T., Thakur, V. et al. Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand. Environ Monit Assess 195, 211 (2023). https://doi.org/10.1007/s10661-022-10802-5 

Bias correction is the process of adjusting projected raw GCM’s simulated data with respect to the reference period observed datasets. Linear Scaling and Quantile Mapping (empirical, theoretical, parametric, regression quantile-quantile, smoothing spline quantile mapping) bias correction methods for correcting gridded raw GCM’s data have been implemented in R scripts (See the GitHub repository). The input to the script is gridded raw GCM datasets. Output from the script is bias corrected gridded GCM’s variables. This R script can be executed with any ordinary computers. Please follow the read me file for detailed execution of the script.

Click here for Bias Correction for Gridded GCM’s Precipitation data script

Reference paper to cite:
Shanmugam, M., Lim, S., Hosan, M.L., Shrestha, S., Babel, M.S., Virdis, S.G.P., 2024. Lapse rate-adjusted bias correction for CMIP6 GCM precipitation data: An application to the Monsoon Asia Region. Environ Monit Assess 196, 49. https://doi.org/10.1007/s10661-023-12187-5