AMSR-E soil moisture inversion based on GRNN neural network

The polarimetric synthetic aperture radar (PolSAR) can be used to obtain soil moisture by inverting scattering models at high resolution. The convolutional neural network (CNN) has been recently introduced to estimate soil roughness for PolSAR data, which need to be driven by a large amount of data. In this paper, a dual-channel CNN based on polarimetric models is proposed for soil moisture inversion, and it aims to further expand the applicable range of roughness in the X-Bragg model by integration with the integral equation model (IEM). Meanwhile, it fully utilizes the spatial information of PolSAR images to relax the number of required training samples when real data on the surface are difficult to obtain. Besides, we designed a framework based on this network. Coarse-grained inversion and fine-grained inversion of soil moisture are carried out through the qualitative classification network and the quantitative regression network, respectively. Experiments on simulated and airborne E-SAR data show that the proposed network can accurately fit the nonlinear relationship between polarization parameters and soil moisture, so as to improve the inversion accuracy with a small number of samples. In our experiments, the average inversion accuracy reached 95.39%, and the root mean square error (RMSE) of the regression network was 0.98%. This method can be applied to a wide range of soil moisture monitoring applications.

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The Index Optimization Method in Neural Network for Soil Moisture Inversion

DEStech Transactions on Engineering and Technology Research ◽

10.12783/dtetr/cemms2019/32028 ◽

2019 ◽

Author(s):

Sen-hao LIU ◽

Xu-wei HE ◽

Xiao-ai DAI

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Optimization Method ◽

Soil Moisture Inversion

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Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

Remote Sensing ◽

10.3390/rs13040554 ◽

2021 ◽

Vol 13 (4) ◽

pp. 554

Author(s):

A. A. Masrur Ahmed ◽

Ravinesh C Deo ◽

Nawin Raj ◽

Afshin Ghahramani ◽

Qi Feng ◽

...

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Deep Learning ◽

Convolutional Neural Network ◽

Climate Index ◽

Climate Indices ◽

High Temporal Resolution ◽

Synoptic Scale ◽

Time Step ◽

Gated Recurrent Unit

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

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Reconstruction of ESA CCI satellite-derived soil moisture using an artificial neural network technology

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.146602 ◽

2021 ◽

pp. 146602

Author(s):

Linqi Zhang ◽

Yi Liu ◽

Liliang Ren ◽

Adriaan J. Teuling ◽

Xiaoxiang Zhang ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Soil Moisture ◽

Network Technology ◽

Artificial Neural ◽

Neural Network Technology

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Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

Remote Sensing ◽

10.3390/rs12162587 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2587

Author(s):

Yan Nie ◽

Ying Tan ◽

Yuqin Deng ◽

Jing Yu

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Arid Regions ◽

Drought Index ◽

Dynamic Monitoring ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Rapid Acquisition ◽

Semi Arid ◽

Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

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