Spatio-temporal coupling of land-surface and energy balance parameters with monsoon rainfall using remote-sensing technology

This research paper focuses on the spatio-temporal coupling of monsoon rainfall with land-surface and energy balance parameters, which are important for understanding hydrological, climatological, and agricultural aspects at local, regional, and global scales. The dynamics of land-surface and energy balance parameters influence summer monsoon over India. Time scales of the land-surface response to monsoon forcing are different for different land-surface conditions due to different physical processes governing the land-surface–atmosphere exchange through energy balance components. A synergy of satellite data from the Moderate Resolution Imaging Spectroradiometer MODIS 0.05° × 0.05° for obtaining land-surface and energy balance parameters, and the Atmospheric Infrared Sounder AIRS 1° × 1° for obtaining atmospheric parameter and gridded rainfall data 1° × 1° from the Indian Meteorological Department IMD during June to September for three consecutive years 2009–2011 representing low to normal rainfall, were used to develop a coupling model in the spatio-temporal domain. Surface energy fluxes were estimated using a surface energy balance model by partitioning available energy at the surface into latent heat flux LE and sensible heat flux H through the evaporative fraction EF concept of a 2D land-surface temperature LST-albedo scatter plot. The coupling models were based on statistical methods developed at both temporal and spatial scales to explain the linking of various parameters with monsoon rainfall. A significant positive relationship was obtained between rainfall and land-surface parameters such as normalized difference vegetation indices NDVIs, and soil wetness/energy balance parameters such as LE and EF, whereas a strong negative relationship was obtained between rainfall and surface radiation parameters LST and albedo/energy balance parameters such as soil heat flux G and net radiation Rn. This approach has demonstrated its simplicity with remote sensing technology and could identify ‘at risk’ regions at spatio-temporal scales based on coupling models.