scholarly journals Modeling Bidirectional Polarization Distribution Function of Land Surfaces Using Machine Learning Techniques

2020 ◽  
Vol 12 (23) ◽  
pp. 3891
Author(s):  
Siyuan Liu ◽  
Yi Lin ◽  
Lei Yan ◽  
Bin Yang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Distribution Function ◽  
Accurate Estimation ◽  
Polarization Distribution ◽  
Scattering Angle ◽  
Non Linear ◽  
Linear Problems ◽  
Input Variables ◽  
Land Surfaces

Accurate estimation of polarized reflectance (Rp) of land surfaces is critical for remote sensing of aerosol optical properties. In the last two decades, many data-driven bidirectional polarization distribution function (BPDF) models have been proposed for accurate estimation of Rp, among which the generalized regression neural network (GRNN) based BPDF model has been reported to perform the best. GRNN is just a simple machine learning (ML) technique that can solve non-linear problems. Many ML techniques were reported to work well in solving non-linear problems and consequently may provide better performance in BPDF modeling. However, incorporating various ML techniques with BPDF modeling and comparing their performances have never been well documented. In this study, three widely used ML algorithms—i.e., support vector regression (SVR), K-nearest-neighbor (KNN), and random forest (RF)—were applied for BPDF modeling. Using measurements collected by the Polarization and Directionality of the Earth’s Reflectance onboard PARASOL satellite (POLDER/PARASOL), non-linear relationships between Rp and the input variables, i.e., Fresnel factor (Fp), scattering angle (SA), reflectance at 670 nm (R670) and 865 nm (R865), were built using these ML algorithms. Results showed that taking Fp, SA, R670, and R865 as input variables, the performance of the four ML-based BPDF models was quite similar. The KNN-based BPDF model provided slightly better results, and improved the accuracy of the semi-empirical BPDF models by 9.55% in terms of the overall root mean square error (RMSE). Experiments of different configuration of input variables suggested that using multi-band reflectance as input variables provided better results than using vegetation indices. The RF-based BPDF model using all reflectances at six bands as input variables produced the best results, improving the overall accuracy by 6.62% compared with the GRNN-based BPDF model. Among all the input variables, reflectance at absorbing spectral bands—e.g., 490 nm and 670 nm—played more significant roles in RF-based BPDF modeling due to the domination of polarized partition in total reflectance. Fresnel factor and scattering angle were also important for BPDF modeling. This study confirmed the feasibility of applying ML techniques to more accurate BPDF modeling, and the RF-based BPDF model proposed in this study can be used to increase the accuracy of remote sensing of the complete aerosol properties.

scholarly journals Estimation of soil types by non linear analysis of remote sensing data

2008 ◽  
Vol 15 (1) ◽  
pp. 115-126 ◽  
Author(s):  
C. Hahn ◽  
R. Gloaguen
Keyword(s):  
Remote Sensing ◽  
Soil Type ◽  
Remote Sensing Data ◽  
Soil Types ◽  
Support Vector ◽  
Sensing Data ◽  
Vector Machines ◽  
Non Linear ◽  
Input Variables ◽  
Type Classification

Abstract. The knowledge of soil type and soil texture is crucial for environmental monitoring purpose and risk assessment. Unfortunately, their mapping using classical techniques is time consuming and costly. We present here a way to estimate soil types based on limited field observations and remote sensing data. Due to the fact that the relation between the soil types and the considered attributes that were extracted from remote sensing data is expected to be non-linear, we apply Support Vector Machines (SVM) for soil type classification. Special attention is drawn to different training site distributions and the kind of input variables. We show that SVM based on carefully selected input variables proved to be an appropriate method for soil type estimation.

scholarly journals Modeling Polarized Reflectance of Natural Land Surfaces Using Generalized Regression Neural Networks

2020 ◽  
Vol 12 (2) ◽  
pp. 248 ◽  
Author(s):  
Yuhao He ◽  
Bin Yang ◽  
Hui Lin ◽  
Junqiang Zhang
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Accurate Information ◽  
Generalized Regression Neural Network ◽  
Scattering Angle ◽  
Natural Land ◽  
Generalized Regression ◽  
Land Surfaces ◽  
Aerosol Properties ◽  
Polarized Reflectance

Retrieval of complete aerosol properties over land through remote sensing requires accurate information about the polarization characteristics of natural land surfaces. In this paper, a new bidirectional polarization distribution function (BPDF) is proposed, using the generalized regression neural network (GRNN). This GRNN-based BPDF model builds a quite accurate nonlinear relationship between polarized reflectance and four input parameters, i.e., Fresnel factor, scattering angle, red, and near-infrared reflectances. It learns fast because only a smoothing parameter needs to be adjusted. The GRNN-based model is compared to six widely used BPDF models (i.e., Nadal–Bréon, Maignan, Waquet, Litivinov, Diner, and Xie–Cheng models), using the Polarization and Directionality of the Earth’s Reflectance (POLDER) measurements. Experiments suggest that the GRNN-based BPDF model is more accurate than these models. Compared with the best current models, the averaged root-mean-square error (RMSE) from the GRNN-based BPDF model can be reduced by 13.4% by using data collected during the whole year and is lower for 97.4% cases with data collected during every month. Moreover, compared to the widely used BPDF models, the GRNN-based BPDF model provides better performance when the scattering angle is small, and it is the first model that is able to reproduce negative polarized reflectance. The GRNN-based BPDF model is thus useful for the remote sensing of complete aerosol properties over land.

scholarly journals Assessment and Comparison of Six Machine Learning Models in Estimating Evapotranspiration over Croplands Using Remote Sensing and Meteorological Factors

2021 ◽  
Vol 13 (19) ◽  
pp. 3838
Author(s):  
Yan Liu ◽  
Sha Zhang ◽  
Jiahua Zhang ◽  
Lili Tang ◽  
Yun Bai
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Hybrid Model ◽  
Regional Scale ◽  
Hybrid Models ◽  
Gradient Boosting ◽  
Accurate Estimation ◽  
Support Vector ◽  
Extreme Gradient Boosting ◽  
Monteith Equation

Accurate estimates of evapotranspiration (ET) over croplands on a regional scale can provide useful information for agricultural management. The hybrid ET model that combines the physical framework, namely the Penman-Monteith equation and machine learning (ML) algorithms, have proven to be effective in ET estimates. However, few studies compared the performances in estimating ET between multiple hybrid model versions using different ML algorithms. In this study, we constructed six different hybrid ET models based on six classical ML algorithms, namely the K nearest neighbor algorithm, random forest, support vector machine, extreme gradient boosting algorithm, artificial neural network (ANN) and long short-term memory (LSTM), using observed data of 17 eddy covariance flux sites of cropland over the globe. Each hybrid model was assessed to estimate ET with ten different input data combinations. In each hybrid model, the ML algorithm was used to model the stomatal conductance (Gs), and then ET was estimated using the Penman-Monteith equation, along with the ML-based Gs. The results showed that all hybrid models can reasonably reproduce ET of cropland with the models using two or more remote sensing (RS) factors. The results also showed that although including RS factors can remarkably contribute to improving ET estimates, hybrid models except for LSTM using three or more RS factors were only marginally better than those using two RS factors. We also evidenced that the ANN-based model exhibits the optimal performance among all ML-based models in modeling daily ET, as indicated by the lower root-mean-square error (RMSE, 18.67–21.23 W m−2) and higher correlations coefficient (r, 0.90–0.94). ANN are more suitable for modeling Gs as compared to other ML algorithms under investigation, being able to provide methodological support for accurate estimation of cropland ET on a regional scale.

scholarly journals GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

2019 ◽  
Vol 11 (14) ◽  
pp. 1655 ◽  
Author(s):  
Yan Jia ◽  
Shuanggen Jin ◽  
Patrizia Savi ◽  
Yun Gao ◽  
Jing Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Ground Truth ◽  
Satellite System ◽  
Soil Conditions ◽  
Retrieval Process ◽  
Method Performance ◽  
Ensemble Machine Learning ◽  
Input Variables

Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with different soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data.

scholarly journals Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques

2021 ◽  
Vol 13 (12) ◽  
pp. 2352
Author(s):  
Liying Geng ◽  
Tao Che ◽  
Mingguo Ma ◽  
Junlei Tan ◽  
Haibo Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Vegetation Index ◽  
Machine Learning Techniques ◽  
Accurate Estimation ◽  
Observation Data ◽  
Learning Models ◽  
Learning Techniques ◽  
Crop Biomass ◽  
Machine Learning Models

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

scholarly journals Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

2021 ◽  
Vol 13 (4) ◽  
pp. 641
Author(s):  
Gopal Ramdas Mahajan ◽  
Bappa Das ◽  
Dayesh Murgaokar ◽  
Ittai Herrmann ◽  
Katja Berger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Spectral Indices ◽  
Learning Models ◽  
Leaf Nutrients ◽  
Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

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