Abstract: Abstract: Due to the effects of organic fertilizer and chemical fertilizer release rate and different fertilizers on soil physical and chemical properties, there are differences in response of crops to water and fertilizer in the process of growth. Therefore, time series remote sensing monitoring is needed to realize dynamic monitoring of space-time in different fertilization treatments. Remote sensing technology, as a means to rapidly acquire spatial and temporal dynamic surface information in a wide range, has been widely recognized as important for the development of modern agriculture with high yield, high efficiency and environmental friendliness. However, at present, remote sensing technology can only assist in monitoring the quality of small-scale agricultural products, while in the production process of organic agricultural products. The research on accurate identification of large-scale fertilization methods is still lacking. Spatio-temporal dynamic monitoring of agricultural production process is an urgent problem to be solved in organic/green agricultural product certification. Spatio-temporal accurate identification of different fertilization methods is the key to solve this problem. California Central Valley has Mediterranean climate, hot summer and little rain. Its unique climate conditions provide a good climate condition for acquiring remote sensing images of the whole growth period. The experimental plots in this area are independent and large in area (0.4 hm2). This provides a reference for monitoring crop growth using remote sensing images. Taking the long-term positioning experiment of University of California at Davis as the basic material and maize and tomato as the research objects under the long-term positioning experiment of different fertilization treatments, this paper uses Landsat 8 and Soleno-2 image of time series to study the rotation of fertilizer, fertilizer + green manure, organic manure + green manure in three different treatments. The time series of vegetation index in the plot is used to compare and analyze the difference of NDVI among different fertilization treatments and the correlation between NDVI and yield. The results show that: 1) the general trends of NDVI time series curves under different fertilization treatments are similar, and the difference between organic fertilizer and chemical fertilizer treatment NDVI time series curve is obvious; 2) the NDVI of different fertilization treatments changes regularly with crop growth period. The mean value of NDVI in organic fertilizer treatment is higher than that in chemical fertilizer treatment at the initial and late growth stage, and lower than that in chemical fertilizer treatment in the middle growth stage; 3) the correlations coefficients between NDVI and yield change regularly with crop growth period under different fertilization treatments, and the effects of different fertilization treatments should be considered when applying vegetation index to estimating yield by remote sensing; 4) Fertilizer + green manure application methods can ensure agricultural sustainability while obtaining more. The difference of NDVI time series in different fertilization treatments has been proved in the research, in which we can also know the correlation difference of NDVI and yield. The results of research could provide remote sensing technology support for spatio-temporal dynamic monitoring and certification of organic/green agriculture, which could be used to distinguish organic fertilizer from other fertilization methods, and could deepen the application of remote sensing in agriculture.