Metadata, covariance matrix of PCA.

The 12 agriculture landscapes located in the surroundings of Kunming, south China (24°42’45’’N-25°22’43’’N, 102°22’18’’E-103°10’90’’E). it was selected by use of Google Earth Profession and field inspections (ground-truthing) once a month during the tomato growing seasons in 2018 and 2019. The cover types in each landscape were divided into 10 types according to vegetation type, human factor interference and land type characteristics. A Principal Components Analysis (PCA) was performed to reduce the dimensions of the landscape data. These ten land cover types were divided for the PCA analysis, the land cover type with the largest area in one landscape and the absolute value of first principal component greater than 0.9 was selected as the landscape type. Principal component axes were extracted using correlations among variables, and the resulting factors were not rotated. (XLSX)