In this paper, an approach for joint estimation of grassland LAI and Cab from unmanned aerial vehicle (UAV) hyperspectral data was proposed. Firstly, based on PROSAIL (PROSPECT+SAIL) model, 15 typical hyperspectral VIs were simulated and analyzed to identify optimal VIs for LAI and Cab estimation. Secondly, three different combinations of VIs were tested in the construction of LAI and Cab inversion matrices. Thirdly, 3-layer 2-dimension VIs matrix was generated to determine the relationship between VIs and LAI values and Cab values. Finally, LAI and Cab were joint retrieved according to the cells of VIs matrix.
Grassland plays an important role in regional economic development and terrestrial ecosystem security. Remote sensing technology is fast, efficient and low cost. The use of remote sensing technology to discriminate grassland species is an important way to monitor the population dynamics and botanical community succession in grassland. Such information is conducive to the timely and accurate detection of changes in the grassland ecological environment and provides an important reference for the scientific management of grassland ecosystems and the construction of an ecologically aware civilization. In this paper, based on the UAV hyperspectral remote sensing image of natural grassland in Inner Mongolia and the linear spectral mixture model, the MTMF (mixture tuned matched filtering) model was used for grass species identification, and the results were verified with FVC (fractional vegetation cover) estimation. The results showed that this method could achieve effective identification and extraction of the dominant species of Leymus chinensis in the study area.
KEYWORDS: Data modeling, Neural networks, Temperature metrology, Performance modeling, Visual process modeling, Machine learning, Lawrencium, Data acquisition, Coastal modeling, Visualization
Accurate temperature prediction is of great significance to human life and social economy. A series of traditional methods and machine learning methods have been proposed to achieve temperature prediction, but it is still a challenging problem. We propose a temperature prediction model that combines seasonal and trend decomposition using loess (STL) and the bidirectional long short-term memory (Bi-LSTM) network to achieve high-accuracy prediction of the daily average temperature of China cities. The proposed model decomposes the temperature data using STL into trend component, seasonal component, and remainder component. Decomposition components and the original temperature data are input into the two-layer Bi-LSTM to learn the features of the temperature data, and the sum of prediction of three components and the original temperature data prediction result are added using learnable weights as the prediction result. The experimental results show that the average root mean square error and mean absolute error of the proposed model on the testing data are 0.11 and 0.09, respectively, which are lower than 0.35 and 0.27 of STL-LSTM, 2.73 and 2.07 of EMD-LSTM, 0.39 and 0.15 of STL-SVM, achieving a higher precision temperature prediction.
Estimation of leaf area index (LAI) is of vital importance to improve the prediction accuracy of crops quality and yield. However, it is more difficult to precisely assess LAI at the late growth stages of crops due to the influences of leaf senescence and soil background. Unmanned aerial vehicles (UAVs), with hyperspectral sensors onboard, can acquire high spatial and spectral resolution images and provide detailed information of fields, and consequently, are widely used for monitoring the biophysical parameters of crops in precision agriculture. The aim of this study was to evaluate the potential of UAV-based hyperspectral data in LAI estimation for sunflower and maize at the milk-filling stage, with machine learning regression algorithms (MLRA) for data analyses. Three algorithms including linear regression (LR), partial least square regression (PLSR) and kernel ridge regression (KRR) were used with the individual vegetation index (VI), VI-combination and spectral reflectance of full wavelengths as input variables. Results indicate that from the perspective of accuracy of estimation models, the PLSR based on VI-combination derived from hyperspectral images outperformed the LR based on individual VI and KRR based on VI-combination or spectral reflectance, which was proven to be the most suitable for the LAI estimation for both maize and sunflower at late growth stage, with 68% and 64% of the variation in LAI were explained, respectively. From the perspective of VIs tested, the modified triangular vegetation index (MTVI1) and improved soil-adjusted vegetation index (MSAVI) were found to be the best LAI estimators for maize and sunflower. Meanwhile, the contributions of the two VIs were also superior over other VIs tested in developing estimation models based on the PLSR method.
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