Global Soil Moisture Estimation Using Microwave Remote Sensing

2012 ◽  
pp. 399-410
Author(s):  
Yang Hong ◽  
Sadiq Ibrahim Khan ◽  
Chun Liu ◽  
Yu Zhang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Microwave Remote Sensing ◽  
Global Soil ◽  
Moisture Estimation

scholarly journals SOIL MOISTURE RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS: APPLICATION TO PASSIVE MICROWAVE REMOTE SENSING

2018 ◽  
Vol XLII-3 ◽  
pp. 583-586 ◽  
Author(s):  
Z. Hu ◽  
L. Xu ◽  
B. Yu
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Massive Data ◽  
Passive Microwave Remote Sensing ◽  
Global Soil

A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.

AN UNSUPERVISED SOIL MOISTURE ESTIMATION MODELLING APPROACH USING C-BAND DUAL PolSAR DATA

2020 ◽  
Author(s):  
Reet Kamal Tiwari ◽  
Akshar Tripathi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Field Data ◽  
Microwave Remote Sensing ◽  
Data Availability ◽  
Series Data ◽  
Soil Conditions ◽  
Decomposition Models ◽  
Sar Data ◽  
Moisture Estimation

<p>With the advent of remote sensing and its widescale implementation in the field of agriculture and soil studies, today remote sensing has become an integral non-evasive analysis and research tool. After decades of research with conventional optical remote sensing, both airborne and spaceborne, a need was felt to have an all-weather remote sensing data availability. Spaceborne SAR (Synthetic Aperture RADAR) or microwave remote sensing with its all-weather availability and high temporal resolution, owing to its penetration capabilities has been found highly suitable for the soil and crop health studies. Since, SAR remote sensing is highly sensitive to surface roughness and dielectrics in dry and moist soil conditions respectively, it becomes highly important to study and observe the variations of these properties in various polarisation channels. PolSAR (Polarimetric SAR) data with its different decomposition models has an advantage over conventional SAR data since it uses more than one polarisation channels and polarimetric decomposition models which consider several soil and crop parameters. This helps to study the RADAR wave interaction with the target easier. This helps in the proper and better study and understanding of retrieval of soil moisture and analysis of its variation over time. This study makes use of C-band Sentinel 1A satellite dual PolSAR, time series data of VV and VH polarisations. The datasets used are that of pre-monsoon and monsoon period of 2019, February to May respectively for Rupnagar area. In this study it has been aimed to model for retrieval of soil moisture based on RADAR backscatter values and Normalised Differential Moisture Indices values from Sentinel-1A and Sentinel 2 satellite imageries respectively. The process has been performed on both VV and VH polarisations and the results are analysed for both the time periods. Theoretically, it has been observed that VH polarisation yields better and nearer to ground truth results with least Root Mean Squared Error (RMSE) of 0.05 and high R<sup>2</sup>-Squared statistics of 0.72 (72%) in training and testing. This study aims at unsupervised modelling using satellite datasets for model development, training and validation and without the input of field data. The results though not very good yet give an idea of soil moisture estimation and is highly beneficial for areas and conditions when field validations and data collection is difficult or not possible. This study also aims at reducing field validation dependence. Once integrated with field data, accuracy is expected to increase.</p>

scholarly journals Present status of soil moisture estimation by microwave remote sensing

2015 ◽  
Vol 1 (1) ◽  
pp. 1084669 ◽  
Author(s):  
Kousik Das ◽  
Prabir Kumar Paul ◽  
Zdena Dobesova
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Present Status ◽  
Microwave Remote Sensing ◽  
Moisture Estimation
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