Downscaling of passive microwave soil moisture retrievals based on spectral analysis

Zhong, Aifen; Wang, Anqi; Li, Jiwei; Xu, Tingbao; Meng, Dan; Ke, Yinghai; Li, Xiaojuan; Chen, Yongqin David

doi:10.1080/01431161.2017.1378456

Cited by 8 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Polynomial Fitting method is based on the approximate polynomial relationship between SM and Land Surface Temperature (LST), Normalized Differential Vegetation Index (NDVI), surface net incoming radiation (Rn), and so on [29]. Additionally, more complex machine learning methods were employed such as the Bayesian merging method [30], Fourier transformation method [31], General Regression Neural Network, Artificial Neural Net-work (ANN), Random Forest (RF), Support Vector Regression (SVR), and Deep Learning methods [32][33][34][35][36], etc. Through the previous research, the flexibility and capability of machine learning methods to deal with massive remote sensing data and nonlinear problems in downscaling SM have been proved [32].…”

Section: > Replace This Line With Your Manuscript Id Number (Double-c...mentioning

confidence: 99%

Series or Parallel? An Exploration in Coupling Physical Model and Machine Learning Method for Disaggregating Satellite Microwave Soil Moisture

Sun

Zhang

Zhao

2022

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Remotely sensed soil moisture (SM) dataset with well accuracy and fine spatiotemporal resolution is very valuable in various fields. Downscaling is a promising way to obtain such an SM dataset. There are currently two basic methodologies in the downscaling with satellite datasets, i.e., the machine learning (ML) methods and the physical or semi-physical (PH) models. This study focuses on exploring feasible ways to integrate them for boosting the performance of downscaling. Here, three parallel modes i.e., arithmetic average (Ari), geometric average (Geo), and weighted average (Wei), and two series modes i.e., first PH then ML (PH-ML) and first ML then PH (ML-PH) were developed and evaluated. A representative of PH, DSCALE_mod16, and three ML algorithms Random Forest, XGBoost, and LightGBM were used in this study. Microwave SM datasets from Soil Moisture Active and Passive Mission (SMAP), satellite datasets from MODIS, and in-situ SM observations spanning from April 2015 to December 2018 were employed in the evaluation. Spatial dynamic range, energy conservation, and precision preservation analyses were conducted as evaluation methods. Results demonstrated the PH-ML series mode outperformed the other coupling modes, which was even better than the better one of the PH and ML. The PH can first generate a valuable initial estimate, and the initial estimate's advantages can be preserved while its errors can be suppressed by the ML. Resultantly, the ideology of first using the PH to obtain an initial estimate and then putting the initial estimate into the ML is suggested for further microwave SM downscaling.

show abstract

Section: > Replace This Line With Your Manuscript Id Number (Double-c...mentioning

confidence: 99%

Series or Parallel? An Exploration in Coupling Physical Model and Machine Learning Method for Disaggregating Satellite Microwave Soil Moisture

Sun

Zhang

Zhao

2022

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In recent decades, a variety of microwave soil moisture products have been made available in several parts of the world [34][35][36][37]. Release and application of the above-mentioned soil moisture products have made it possible to take full account of the spatial and temporal variations of soil moisture content in the discrimination of freeze/thaw states of the ground surface.…”

Section: Introductionmentioning

confidence: 99%

An Improved Algorithm for Discriminating Soil Freezing and Thawing Using AMSR-E and AMSR2 Soil Moisture Products

2018

View full text Add to dashboard Cite

Discriminating between surface soil freeze/thaw states with the use of passive microwave brightness temperature has been an effective approach so far. However, soil moisture has a direct impact on the brightness temperature of passive microwave remote sensing, which may result in uncertainties in the widely used dual-index algorithm (DIA). In this study, an improved algorithm is proposed to identify the surface soil freeze/thaw states based on the original DIA in association with the AMSR-E and AMSR2 soil moisture products to avoid the impact of soil moisture on the brightness temperature derived from passive microwave remotely-sensed soil moisture products. The local variance of soil moisture (LVSM) with a 25-day interval was introduced into this algorithm as an effective indicator for selecting a threshold to update and modify the original DIA to identify surface soil freeze/thaw states. The improved algorithm was validated against in-situ observations of the Soil Moisture/Temperature Monitoring Network (SMTMN). The results suggest that the temporal and spatial variation characteristics of LVSM can significantly discriminate between surface soil freeze/thaw states. The overall discrimination accuracy of the improved algorithm was approximately 89% over a remote area near the town of Naqu on the East-Central Tibetan Plateau, which demonstrated an obvious improvement compared with the accuracy of 82% derived with the original DIA. More importantly, the correct classification rate for the modified pixels was over 96%.

show abstract

Discrimination and mapping ground surface freeze and thaw states over Northeastern China based on the improved dual-index algorithm

Gao¹,

Zhang

Chen

et al. 2023

Cold Regions Science and Technology

View full text Add to dashboard Cite

Downscaling of passive microwave soil moisture retrievals based on spectral analysis

Cited by 8 publications

References 29 publications

Series or Parallel? An Exploration in Coupling Physical Model and Machine Learning Method for Disaggregating Satellite Microwave Soil Moisture

Series or Parallel? An Exploration in Coupling Physical Model and Machine Learning Method for Disaggregating Satellite Microwave Soil Moisture

An Improved Algorithm for Discriminating Soil Freezing and Thawing Using AMSR-E and AMSR2 Soil Moisture Products

Discrimination and mapping ground surface freeze and thaw states over Northeastern China based on the improved dual-index algorithm

Contact Info

Product

Resources

About