The invention discloses a
shallow lake turbidity remote sensing inversion method and
system based on wind drive physical constraint, and the method comprises the steps: constructing a
time sequence pairing sample through combining a
remote sensing image and actually measured data, and extracting wind drive characteristics such as
wind speed,
wind stress and strong wind duration; establishing direction consistency priori by using monotone calibration to generate an expected direction
signal, and constructing a wind-driven
amplitude response surface interpolation to obtain an expected change amplitude; in model training, a double-moment regression
network sharing weights is adopted, and an actual measurement supervision error and wind-driven physical constraints are jointly optimized; and finally, carrying out pixel-level reasoning and
verification on the multi-temporal
remote sensing image to realize continuous
estimation of
spatial distribution and dynamic change of
turbidity. According to the method, a double-moment input and shared weight
network structure is introduced, the physical mechanism and
deep learning advantages are fused, the
time sequence continuity, the physical
interpretability and the cross-space-time generalization ability are remarkably improved while high inversion precision is kept, and an efficient and reliable technical approach is provided for
water environment remote sensing monitoring.