The Study Of Rice Heavy Metal Stress Inversion Method Based On Synergy Of Multi-Source Remote Sensing Data

Accurate and large-scale monitoring of heavy metals in paddy fields is important to agricultural production and food security. Most of the previous studies use optical remote sensing data for monitoring heavy metal stress on rice, the monitoring accuracy is relatively low. In order to assess the heavy metal stress more accuraty, this article synergy microwave remote sensing and optical remote sensing to construct the crop heavy metal stress monitoring model. Typical rice sample polluted by heavy metal stress in Suzhou city was selected as the experimental area; ASD Spetrum biochemical parameters, heavy metal contents(soil, rice) data were collected for rice in growth stage, and the HJ-1A HIS and Radarsat-2 SAR satellite data was obtained. Based on the mechanism of rice in response to heavy metal stress, the spectral indexes were extracted sensitive to heavy metal, and HIS and SAR remote sensing index was synergized to build two-dimensional feature space to monitor heavy metal pollution stress. Model spatial scale expansion would produce scale error, and wavelet analysis method was used for characterization of fractal dimension error and error reduction. In this paper, the work and main conclusions are as follows:(1) Using the spectral characteristics analysis combined with statistical methods or random forest algorithm to build canopy chlorophyll index NVI which is sensitive to heavy metal stress, and a heavy metal stress level inversion model based on hyper spectral HIS optical remote sensing image was established.(2) Using statistical methods to build microwave index SVI characterize changes in biomass, the heavy metal stress level inversion model was established based on microwave SAR image.(3) Combining biochemical parameters with Morphological parameters in response to heavy metal stress, giving full play to the complementary of multisource remote sensing images, synergizing NVI which is sensitive to chlorophyll and SVI which is sensitive to biomass, a two-dimensional feature index space was built. It could determine the level of heavy metal stress. Then the heavy metal stress level inversion model based on multi-source remote sensing data synergized was built.(4) By analyzing the causes of scale error when the model applied at different spatial scale, combining wavelet transform with fractal theory to analyze scale error, using wavelet fractal to simulate scale variation, moderately estimating and reducing scale error, scale expansion model was achieved.In this paper, the innovation points are: â‘  a hyperspectral sensitivity index of heavy metal stress and microwave sensitivity index was proposed; heavy metal stress level inversion was achieved by the two-dimensional remote sensing index, the method can be applied to various kinds of environmental stress; â‘¡ the model space scale expansion was achieved by reducing error based on wavelet fractal and providing a thinking way for upscaling.