Estimation of Cotton Nitrogen Content Based on Multi-Angle Hyperspectral Data and Machine Learning Models
نویسندگان
چکیده
For crop growth monitoring and agricultural management, it is important to use hyperspectral remote sensing techniques estimate canopy nitrogen content in a timely accurate manner. The traditional nadir method has limited ability assess the trophic state of cotton shoots, which not conducive high-precision inversion, whereas multi-angle can effectively extract canopy’s physicochemical information. However, spectral information affected by variety factors, frequently causes shifts band associated with uptake, lowers estimation accuracy. capacity index aerial concentration (ANC) was therefore investigated this work under various observation zenith angles (VZAs), Relief−F employed select best weight for ANC that insensitive VZA. Therefore, study, explored different VZAs, Relief-F algorithm used optimize optimal angle (AINI) VZAs calculated using expression (R530 − R704)/(R1412 + R704). results show correlation between chosen study stronger than off-nadir observations, coefficients Photochemical Reflectance Index (PRI), AINI, are highest when VZA −20° −50° (r = 0.866 0.893, respectively). Compared vegetation index, AINI had > 0.84), performance backscatter direction estimated be better forward-scatter direction. At same time, model indices PRI combined machine learning achieve accuracy, prediction accuracy random forest (RF) R2 0.98 RMSE 0.590. This shows VZAs. Simultaneously, backscattered revealed more estimation. findings encourage observations nutrient estimation, will also help improve ground-based satellite sensors.
منابع مشابه
Machine Learning Models for Housing Prices Forecasting using Registration Data
This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...
متن کاملthe effect of explicit teaching of metacognitive vocabulary learning strategies on recall and retention of idioms
چکیده ندارد.
15 صفحه اولSpatiotemporal Estimation of PM2.5 Concentration Using Remotely Sensed Data, Machine Learning, and Optimization Algorithms
PM 2.5 (particles <2.5 μm in aerodynamic diameter) can be measured by ground station data in urban areas, but the number of these stations and their geographical coverage is limited. Therefore, these data are not adequate for calculating concentrations of Pm2.5 over a large urban area. This study aims to use Aerosol Optical Depth (AOD) satellite images and meteorological data from 2014 to 2017 ...
متن کاملProcessing Hyperspectral Data in Machine Learning
The adaptive and automated analysis of hyperspectral data is mandatory in many areas of research such as physics, astronomy and geophysics, chemistry, bioinformatics, medicine, biochemistry, engineering, and others. Hyperspectra di er from other spectral data that a large frequency range is uniformly sampled. The resulting discretized spectra have a huge number of spectral bands and can be seen...
متن کاملThermal conductivity of Water-based nanofluids: Prediction and comparison of models using machine learning
Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learni...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Remote Sensing
سال: 2023
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs15040955