In order to understand the effect of different sampling strategy, few sampling strategy been tested with varying sample size. To verify whether ESS helps in getting better estimates, more exhaustive sampling approaches have been tested under the ESS of 25% sample. These approaches could be summarized in four broader categories: Simple Random Sampling, Systematic Sampling,  geographically stratified sampling, and balanced sampling.
Simple random sampling (SRS) is the most well-known sampling design. For SRS all possible samples have the same probability, and any one of the samples is randomly selected with equal probability. The second approach is systematic sampling which works on systematically selecting samples out of N population. A variation in systematic sampling uses ordered attributes. In this paper, population density is ranked in decreasing order and the sample is systematically selected, ensuring sample range is similar to the population. Systematic sampling could yield a biased result if any periodic pattern exists in the population. Sampling with geographical stratification assures that the sample is well spread over the population. Stratification forces the sample points to disperse while retaining a random component. This is the type of sampling design employs hexagons, squares or any other geographic tessellations. The fourth approach of spatially stratified balanced sampling improves the efficiency of estimated values by maximizing spatial independence among sample locations \cite{theobald2007using}.  These methods uses auxiliary variable space to maximize the spatial independence in the sampled locations.  Table X lists all the variations in these four sampling that have been used in the study.
Table: Sampling Methods with name and code