Inversion Of Cotton Leaf SPAD And Nitrogen Based On Sentinel-2 Remote Sensing Image

Cotton plays a vital role in the production and construction of the Xinjiang Alar Reclamation area.Therefore,ensuring the normal growth and development of cotton is of utmost importance.The health of cotton is closely associated with the SPAD and LNC of its leaves.Monitoring the SPAD and LNC of cotton leaves is an essential method for assessing the growth and development of cotton.In this study,we focused on investigating the SPAD and LNC of cotton leaves.To obtain data,we utilized Sentinel-2 SR remote sensing images and derived various spectral indices.By conducting correlation analysis between these spectral indices and the measured SPAD and LNC values of cotton leaves,we identified the spectral indices that exhibited a high correlation.Subsequently,we employed the K-nearest neighbor model(KNN),random forest model(RF),XGBoost model,and optimization integration model(OIA)to construct estimation models for cotton leaf SPAD and LNC.After evaluating the models,we selected the optimal one and employed the Sentinel-2 SR remote sensing image for spatial and temporal inversion of cotton leaf SPAD and LNC.This inversion process provides a theoretical foundation and technical support for monitoring cotton growth and managing fields in the Alar reclamation area of Xinjiang.Research has been carried out in the following five aspects:(1)By analyzing twelve spectral indices including GNDVI,GRNDVI,NDVI,RVI,GRVI,DVI,GBNDVI,OSAVI,EVI,SAVI,GDVI,and NDWI,we found a strong and significant correlation between these indices and the SPAD and LNC values of cotton leaves.This correlation highlights the close relationship between these spectral indices and the SPAD and LNC of cotton leaves..(2)We employed the NDVI,RVI,SAVI,OSAVI,GRNDV,and GNDVI spectral indices as independent variables to assess the fitting and prediction performance of the SPAD value of cotton leaves during the flowering and boll stages.The evaluation was conducted using the KNN model,RF model,XGBoost model,and OIA model.Among these models,the OIA model outperformed the three traditional machine learning models and proved to be the most accurate model for estimating the SPAD content in cotton leaves.The determination coefficient(R~2),mean squared error(MSE),and mean absolute error(MAE)of the NDVI estimation model constructed by the OIA model were determined to be 0.909,2.0213,and1.1115,respectively.In the verification set,the R~2value was 0.8366,the MSE was 4.1678,and the MAE was 1.6969.(3)To improve the fitting and prediction of the LNC(nitrogen content)during the cotton flower boll stage,we utilized the selected spectral indices,including GBNDVI,GNDVI,GRVI,GRNDVI,OSAVI,and NDVI,as independent variables.We employed the KNN model,RF model,XGBoost model,and OIA model for this purpose.Among these models,the OIA model outperformed the traditional machine learning models and emerged as the best model for estimating the LNC content in cotton.Among the OIA models,the GBNDVI model proved to be the most accurate estimation model.It exhibited a modeling determination coefficient(R~2)of 0.9099,a mean squared error(MSE)of 0.0017,and a mean absolute error(MAE)of 0.033.Additionally,the coefficient of determination(R~2),MSE,and MAE for the overall models were 0.8825,0.0023,and 0.0389,respectively.(4)Through the inversion of cotton leaf SPAD and LNC in the Alar Reclamation area using Sentinel-2SR remote sensing images and the OIA model,it was observed that these models yielded similar results regarding the spatial distribution of cotton leaf SPAD and LNC in high and high regions.This similarity can be attributed to the fact that high SPAD and LNC regions in cotton leaves generally indicate better plant health,and these models effectively capture this relationship.However,some variations may arise in the inference of the low and middle regions of cotton leaf SPAD and LNC due to differences in the sensitivity and parameter settings of the spectral indices used by each model to estimate cotton leaf SPAD and LNC.(5)The SPAD and LNC of cotton leaves in the Alar Reclamation area were inverted using the Sentinel-2 SR remote sensing image and the optimized OIA model for the years 2019 and 2022,respectively.The findings revealed a declining trend in the growth of cotton SPAD and LNC,transitioning from high levels to low,medium,and high levels in most regions of the Alar Reclamation area over the past four years.