Remote Sensing Inversion of Nitrogen Nutrition Status of Apple Tree Leaves during Stopping Period of Spring Shoots

The nutrient inversion model of apple leaves was established by spectral analysis technology to provide technical support for the fine management of apple trees.In Shuangquan Town,Changqing District,Jinan City,Shandong Province,the Fuji apple trees with stopping period of spring shoots were taken as research objects.The spectral reflectance and nitrogen content of apple leaves were measured by ASD Field Spec 4 portable ground object spectrometer.Analyzed the correlation between leaf nitrogen content and spectral reflectance.The sensitive wavelengths with high correlation coefficient were select by fractional differential algorithm,and the optimal vegetation index was constructed and screened out.Partial Least Square Regression(PLSR),Support Vector Machine(SVM)and Random Forests(RF)method were used to construct an inversion model of leaf nitrogen content.The results show that the RF model based on fractional differential second-order treatment is the best inversion model for the nitrogen content of leaves during stopping period of spring shoots.The modeling accuracy determination coefficient R2 reached 0.891,RMSE was 0.0841,and RPD was 2.1396.The determination coefficient R2 of the fitting results of the verification set was 0.617,RMSE was 0.1251,and RPD was 1.7105.The inversion model established by RF method is effective in monitoring the nitrogen content in apple leaves,which provides a theoretical basis for monitoring the growth of apple by hyperspectral technology.

Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning

Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau.In this study,combined with satellite images and bathymetric data,we comprehensively evaluate the accuracy of a multi-factor combined linear regression model(MLR)and machine learning models,create a depth distribution map and compare it with the spatial interpolation,and estimate the change of water level and water storage based on the inverted depth.The results indicated that the precision of the random forest(RF)was the highest with a coefficient of determination(R2)value(0.9311)and mean absolute error(MAE)values(1.13 m)in the test dataset and had high reliability in the overall depth distribution.The water level increased by 9.36 m at a rate of 0.47 m/y,and the water storage increased by 1.811 km3 from 1998 to 2018 based on inversion depth.The water level change was consistent with that of the Shuttle Radar Topography Mission(SRTM)method.Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes.

A spatial resolution effect analysis of remote sensing bathymetry

A spatial resolution effect of remote sensing bathymetry is an important scientific problem. The in situ measured water depth data and images of Dongdao Island are used to study the effect of water depth inversion from different spatial resolution remote sensing images. The research experiments are divided into five groups including Quick Bird and World View-2 remote sensing images with their original spatial resolution（2.4/2.0 m）and four kinds of reducing spatial resolution（4, 8, 16 and 32 m）, and the water depth control and checking points are set up to carry out remote sensing water depth inversion. The experiment results indicate that the accuracy of the water depth remote sensing inversion increases first as the spatial resolution decreases from 2.4/2.0 to 4, 8 and16 m. And then the accuracy decreases along with the decreasing spatial resolution. When the spatial resolution of the image is 16 m, the inversion error is minimum. In this case, when the spatial resolution of the remote sensing image is 16 m, the mean relative errors（MRE） of Quick Bird and World View-2 bathymetry are 21.2% and 13.1%,compared with the maximum error are decreased by 14.7% and 2.9% respectively; the mean absolute errors（MAE） are 2.0 and 1.4 m, compared with the maximum are decreased by 1.0 and 0.5 m respectively. The results provide an important reference for the selection of remote sensing data in the study and application of the remote sensing bathymetry.

Extraction of Desertification Information in Hulun Buir Based on MODIS Image Data

[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.

Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems

Building the physics-driven mechanism model has always been the core scientific paradigm for parameter estimation in Earth surface systems,and developing the data-driven machine learning model is a crucial way for paradigm transformation in geoscience research.The coupling of mechanism and learning models can realize the combination of“rationalism”and“empiricism”,which is one of the most concerned research hotspots.In this paper,for remote sensing inversion and dynamic simulation,we deeply analyze the internal bottleneck and complementarity of mechanism and learning models and build a coupling paradigm framework with mechanism-learning cascading model,learning-embedded mechanism model,and mechanism-infused learning model.We systematically summarize ten specific coupling methods,including preprocessing and initialization,intermediate variable transfer,post-refinement processing,model substitution,model adjustment,model solution,input variable constraints,objective function constraints,model structure constraints,hybrid,etc.,and analyze the main existing problems and future challenges.The research aims to provide a new perspective for in-depth understanding and application of the mechanism-learning coupling model and provide theoretical and technical support for improving the inversion and simulation capabilities of parameters in Earth surface systems and serving the development of Earth system science.