Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression

Zhang, Hongyan; Wang, Shudong; Liu, Kai; Li, Xueke; Li, Zhengqiang; Zhang, Xiaoyuan; Liu, Bingxuan

doi:10.3390/ijgi11020101

Cited by 9 publications

(8 citation statements)

References 72 publications

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“…Microwave remote sensing is based on the contrast between the dielectric constants of dry soil and water and is considered to be the most suitable monitoring method for soil moisture due to its working characteristics of all-day, all-weather, and certain penetration of soil and vegetation. Active microwave remote sensing has high spatial resolution, but is more susceptible to soil roughness and crops, and is suitable for small-scale soil moisture inversion [ 11 , 12 ]. Passive microwave remote sensing has a short revisit period and is relatively less affected by roughness and terrain, but the spatial resolution of the images is relatively low, which is suitable for large-scale agricultural drought monitoring.…”

Section: Introductionmentioning

confidence: 99%

Rice Drought Damage Assessment Using AMSR-E Data Inversion of Surface Temperature

Wang

Zhang

2022

Computational Intelligence and Neuroscience

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The extreme drought events caused by global warming have become one of the major issues of general concern all over the world. It is estimated that over the past 50 years, the average annual drought-affected area has reached more than 200,000 km2, resulting in a global economic loss of US$6-8 billion, far exceeding other meteorological disasters. Therefore, conducting real-time and effective drought monitoring research is of great significance for issues such as climate change, drought defense, water resources management, and protection in various regions. Rice is the largest food crop in China and plays a pivotal role in food production. Drought is often regarded as one of the most important stress factors. Scientific, accurate, and timely assessment of the impact of drought on rice yield is essential for improving crop drought resistance and ensuring food production. In this study, based on the meteorological data, rice growth period and yield of the main rice planting areas in Chongqing Yangtze River Basin, and based on the drought index of passive microwave remote sensing observation data (AMSR-E), a statistical model of rice meteorological yield and drought index under the influence of drought is established. A rice drought disaster assessment is carried out. The results of the disaster assessment indicate that under the influence of drought, the rice yield reduction rate of representative sites in Chongqing Yangtze River Basin is between 3% and 10%.

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Section: Introductionmentioning

confidence: 99%

Rice Drought Damage Assessment Using AMSR-E Data Inversion of Surface Temperature

Wang

Zhang

2022

Computational Intelligence and Neuroscience

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“…These variables can be collected through reliable and readily available data at large scales, thereby enhancing the model's potential for extension to diverse regions. These variables have been consistently associated with soil moisture in previous downscaling studies (Liu et al, 2023;Ranney et al, 2015;Song et al, 2014;Zhang et al, 2022). Specifically, atmospheric variables (e.g., LST, albedo, and precipitation) maintain temporal variability, while geophysical variables (e.g., DEM) capture spatial variability of the downscaled soil moisture.…”

Section: Feasibility Of Chosen Explanatory Factorsmentioning

confidence: 68%

“…Soil moisture downscaling, an effective technique for improving spatial resolution, has received substantial attention (Zhang et al, 2022). Statistical approaches and land surface models (Famiglietti et al, 2008;Grayson and Western, 1998) have been widely used, but these methods typically require large amounts of parametric data with ground data.…”

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confidence: 99%

Downscaling satellite-derived soil moisture in the Three North region using ensemble machine learning and multiple-source knowledge integration

Liu,

Zhang,

et al. 2024

Preprint

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Abstract. Soil moisture plays a crucial role in hydrological and ecological systems. While remote sensing has advanced large-scale soil moisture monitoring, current satellite products often face spatial resolution limitations. This study presents a reliable framework for downscaling satellite-derived soil moisture, leveraging ensemble machine learning and multiple knowledge sources. Our approach efficiently converges outputs from diverse machine learning algorithms through Bayesian model, harnessing spatiotemporal domains and point-wise data. Covering approximately five million square kilometres in the Three Northern region of China, our model generates 1-km daily soil moisture maps, accurately reflecting soil water content patterns and showing spatial consistency with outputs from two credible numerical models. Validation against in situ measurements from three ground networks confirms the accuracy of the downscaled dataset. Comparative analysis demonstrates the superiority of the Bayesian-based method over four individual machine learning methods. The high-resolution dataset produced proves effective in capturing drought dynamics, particularly extreme drought patterns. The robustness of our framework is further affirmed through uncertainty analysis, employing leave-one-out and progressive sample reduction approaches. In summary, our ensemble machine learning-based framework offers an efficient solution for acquiring accurate and high-resolution soil moisture data across large regions, with implications for water resource management and drought monitoring.

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“…Subsequently, we downscaled the gap‐filled SSM data from 25‐ to 1‐km resolution using the RF algorithm, integrating remote sensing data sets, digital terrain characteristics, and climatic variables, as described by H. Zhang et al. (2022).…”

Section: Methodsmentioning

confidence: 99%

Hybrid Theory‐Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China

Bo,

Li,

Liu

et al. 2024

Water Resources Research

Self Cite

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Current irrigation water use (IWU) estimation methods confront uncertainties warranting further attention, primarily stemming from constraints within model structure and data quality. This study proposes a hybrid framework that integrates multiple machine learning (ML) methods with theory‐guided strategies to calculate IWU for three principal cereal crops within the Chinese agricultural landscape. We generated high resolution time series data sets of evapotranspiration and surface soil moisture (SM) using remote sensing resources. ML techniques, along with the Bayesian three‐cornered hat ensemble, were employed to drive multiple remote sensing‐derived data sets in IWU calculation. We applied two theory‐guided mechanisms to quantify irrigation signals: first, converting original SM values into logarithmic terms, and second, extracting process‐based SM residuals. Proposed framework has been validated at 12 field stations across China, yielding coefficient of determination (R2) ranging from 0.54 to 0.70, and root mean square error (RMSE) spanning 278–335 mm/yr. Our framework demonstrates considerable strength in IWU estimation when compared to reported IWU values form 341 cities across China. Specifically, for rice, wheat, and maize, the R2 values range from 0.78 to 0.83, 0.68 to 0.76, and 0.53 to 0.64, respectively, with corresponding RMSE measuring 0.22–0.25, 0.10–0.12, and 0.11–0.13 km3/yr, respectively. These findings highlight the effectiveness of theory‐guided strategies in discerning irrigation‐related information, thereby improving overall model performance. Attention should be directed toward the uncertainties in evapotranspiration and precipitation products on model performance, which remained modest, with a relative change of less than 5%.

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Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression

Cited by 9 publications

References 72 publications

Rice Drought Damage Assessment Using AMSR-E Data Inversion of Surface Temperature

Rice Drought Damage Assessment Using AMSR-E Data Inversion of Surface Temperature

Downscaling satellite-derived soil moisture in the Three North region using ensemble machine learning and multiple-source knowledge integration

Hybrid Theory‐Guided Data Driven Framework for Calculating Irrigation Water Use of Three Staple Cereal Crops in China

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