Mongolia 30m resolution grass yield estimation dataset (2017-2021)

The obtained data and the measured grass yield were normalized, and a three-layer neural network model was built. Fitting the relationship between the grass yield and the other three influencing factors, build the grass yield estimation model.

Due to the small number of training samples, the method of cross validation is adopted. 70% of the training samples are randomly selected as the training set and 30% of the data as the validation set. The grass yield estimation model is obtained through three cross validation.Grass yield estimation: based on the improvement of model estimation accuracy, the three grass yield estimation models trained are loaded into the GEE platform by calling the Python language API interface provided by GEE.