@article {bnh-4233,
	title = {Downscaling of soil dryness estimates: a short review},
	number = {353},
	year = {2017},
	month = {11/2017},
	institution = {Bushfire and Natural Hazards CRC},
	address = {Melbourne},
	abstract = {<p>Accurate and fine-scale landscape dryness estimation is critical for the management and timely warning of disasters like landscape fires, floods, heatwaves, landslips. It has application in environmental management, agriculture and other types of farming like livestock, and silviculture as well. In a fire danger context, the estimated landscape dryness is calculated for assessing the fuel availability. Though new techniques like remote sensing and land surface modelling provide accurate soil moisture information, it is at a relatively coarser scale than that is required for the above mentioned applications. A common practice to overcome such a problem is to employ downscaling methods to increase the spatial scale of the product. The downscaling approach can be broadly subdivided into deterministic and stochastic. The present study provide a brief review on some of these downscaling methods that are used to derive finer scale information from remote sensing or land surface model outputs. We also highlight some of the studies which has used the above methods for soil moisture applications. The discussion presented here is not intended to be complete and reflect authors{\textquoteright} interest. But we still hope that it helps to highlight some of the most commonly used downscaling approaches that are well known to the hydrological community.</p>},
	issn = {353},
	author = {Vinod Kumar and Imtiaz Dharssi}
}
