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Precision agriculture requires information of many variables, not only in three-dimensional maps, but also as a sequence of maps in time. Generally, space interpolation has been a task afforded with either deterministic or stochastic methods like finite element or geostatistics techniques; however, the time behavior of some of those variables like temperature, and evaporation has been traditionally addressed with time series. This study attempts to address the general problem of general interpolation as a sequence of surfaces in which not only could be generated spatial information but also regeneration of complete lost pictures. The real problem has more inherent complexities when space and time are addressed simultaneously. Therefore, a numerical model should be developed. Numerical techniques and current computers allow to addressing this problem type with promising success. Necessities in knowing and understanding the space and time interpolation functions convey to explore functions with four independent variables. To define the theoretical framework of this interpolation implies utilizing geometrical treatment as well as dealing with time variations. Typical hypotheses have been taken into account like homogeneity or isotropy for each cell. The numerical techniques can be various, but a natural extension of finite element method has been selected because the systematic methodology as well as its strong capabilities to simulate different boundary conditions over irregular domains. To measure its effectiveness embraces the prediction of scenarios with turfgrass reflectance data.