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To effectively reduce the power consumed during data transfer between forest microclimate monitoring stations, a dictionary-toggling-based compressed sensing method was proposed. After the sample data were characterized and classified, a discrete Fourier transform fixed dictionary and K-SVD learning dictionary was switched to realize sparse expression and compression of the sample data. And then the sample data were fitted by using a Gaussian function. The coefficient of determination R2 and root-mean-square fitting error were adopted as toggling factors to define the dictionary toggling strategy. Parameters such as air temperature, air humidity, soil temperature and soil moisture content were selected for testing to verify the feasibility of the dictionary-toggling strategy. Experimental results revealed that when the sparseness and compression rate were identical for forest microclimate monitoring stations, combining the advantages of DFT and K-SVD dictionaries, the dictionary-toggling-based compressed sensing algorithm yielded smaller reconstruction errors than those based on single dictionary. Experiments on the actual power consumption of stations demonstrated that when the sparseness K was 16, the dictionary-toggling-based compressed sensing algorithm caused the average daily power consumption to be decreased by 16.35%. Thus, the proposed dictionary-toggling-based compressed sensing method ensured low-power consumption operation and reliable data transfers for forest microclimate monitoring stations.