A semi-empirical inversion model for assessing surface soil moisture using AMSR-E brightness temperatures

Chen, Xiuzhi; Chen, Shuisen; Zhong, Rugang; Su, Yongxian; Liao, Jishan; Li, Dan; Han, Liusheng; Li, Yong; Li, Xia

doi:10.1016/j.jhydrol.2012.05.022

Cited by 14 publications

(11 citation statements)

References 31 publications

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“…After matching the in situ measurements with the pixel‐based data, we calculated the errors of the soil moisture values derived from the ECV_SM data product at each station in each year, using the in situ station as the source. Our previous studies have revealed that the retrieval error of soil moisture using passive microwave remote sensing was highly related to land cover conditions [ Chen et al ., , , ]. Vegetation can interfere with the signals coming from the underlying soil.…”

Section: Methodologiesmentioning

confidence: 99%

“…It can help reduce data gap and bridge the spatial scales of in situ, regional, and national monitoring. Passive microwave remote sensing has been used to monitor surface soil moisture for more than 30 years [ Ulaby et al ., ; Schmugge , ; Wang et al ., ; Jackson et al ., ; Schmugge , ; Uitdewilligen et al ., ; Chen et al ., ]. Due to its longer wavelength, the microwave can penetrate deeper into the media (i.e., soil and vegetation canopy) than the optical electromagnetic wave, and the penetration depth increases with wavelength [ Ulaby et al ., ].…”

Section: Introductionmentioning

confidence: 99%

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Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979–2010)

Chen

Liao

et al. 2016

JGR Atmospheres

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Understanding the historical trends and driving mechanism of China's soil moisture change is an important step in combating climate change. Using the time series satellite-derived Essential Climate Variable Soil Moisture (ECV_SM) product, we detected a significant decrease trend in land surface soil moisture in eastern China over a 32 year period . Theoretical sensitivity analysis suggested that soil moisture is regulated collectively by precipitation (P), potential evapotranspiration (PET), land surface conditions such as land cover/use changes, landscape features, irrigation and urban expansion, (m), and the water balance between input and output water supplies O (the input water supplies minus the output). The change in spatial pattern and temporal trend of P/PET is highly consistent with the corresponding change in soil moisture. The magnitude of soil moisture variation is also well correlated with that of P/PET (R 2 = 0.43; p < 0.001). Therefore, P/PET is believed to be the dominant factor in determining the temporal trends of soil moisture change. Among the 29 drainage basins with significant decreasing trend of soil moisture change, the areas of forest cover increased by 36.08% and the average topographic slope was twice steeper than that of other regions. Therefore, besides the climate factor (P/PET variable), land surface conditions (such as land cover changes and topographic) also played important roles in regulating the trend of regional soil moisture change. 1998;Uitdewilligen et al., 2003;Chen et al., 2012]. Due to its longer wavelength, the microwave can penetrate deeper into the media (i.e., soil and vegetation canopy) than the optical electromagnetic wave, and the CHEN ET AL.CHINA'S 32 YEAR SOIL MOISTURE 5177

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Section: Methodologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979–2010)

Chen

Liao

et al. 2016

JGR Atmospheres

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“…Based on the optical and thermal infrared remote sensing, many approaches have been developed by establishing relationships between SM and soil reflectivity or surface temperature/vegetation coverage and soil thermal properties. Based on the microwave remote sensing, various methods have been proposed for the past 35 years [ 6 , 33 ]. For passive microwave remote sensing, the representative models are GOM [ 34 ], POM [ 34 ], SPM [ 34 ], IEM [ 35 , 36 ], AIEM [ 37 , 38 ], Q/H [ 39 , 40 ] and Q/P [ 41 ] for bare soil area.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review

Zhang

Zhou

2016

Sensors

202

110

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As an important parameter in recent and numerous environmental studies, soil moisture (SM) influences the exchange of water and energy at the interface between the land surface and atmosphere. Accurate estimate of the spatio-temporal variations of SM is critical for numerous large-scale terrestrial studies. Although microwave remote sensing provides many algorithms to obtain SM at large scale, such as SMOS and SMAP etc., resulting in many data products, they are almost low resolution and not applicable in small catchment or field scale. Estimations of SM from optical and thermal remote sensing have been studied for many years and significant progress has been made. In contrast to previous reviews, this paper presents a new, comprehensive and systematic review of using optical and thermal remote sensing for estimating SM. The physical basis and status of the estimation methods are analyzed and summarized in detail. The most important and latest advances in soil moisture estimation using temporal information have been shown in this paper. SM estimation from optical and thermal remote sensing mainly depends on the relationship between SM and the surface reflectance or vegetation index. The thermal infrared remote sensing methods uses the relationship between SM and the surface temperature or variations of surface temperature/vegetation index. These approaches often have complex derivation processes and many approximations. Therefore, combinations of optical and thermal infrared remotely sensed data can provide more valuable information for SM estimation. Moreover, the advantages and weaknesses of different approaches are compared and applicable conditions as well as key issues in current soil moisture estimation algorithms are discussed. Finally, key problems and suggested solutions are proposed for future research.

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“…However, the effects of climate change and forest management on hydrological processes are still not fully understood as they are a multidisciplinary problem (Goyal, ). Surface soil moisture is a vital variable used to describe water and energy exchanges at the land surface and atmosphere interface (Chen et al, ). Therefore, it is important to assess the long‐term and large‐scale historical patterns and trends of regional surface soil moisture, which provide useful information to understand the individual effects from climate variability and land cover changes.…”

Section: Introductionmentioning

confidence: 99%

“…Comparing with the in situ observations, the satellite‐based active and passive microwave remote sensing, which has been used for national (Tao, Yokozawa, Hayashi, & Lin, ; Chen et al, ; Lai et al, ) and even global studies (Kuenzer, Bartalis, Schmidt, Zhao, & Wagner, ; Dorigo et al, ; Albergel et al, ), can provide long‐term and large‐scale surface soil moisture datasets with adequate spatial–temporal resolution and accuracy (Chen et al, ; Albergel et al, ). The spatio‐temporal variations of soil moisture were calculated based on the pixel values of the time‐series remote sensing imageries (Lu & Shi, ; Albergel et al, ).…”

Section: Introductionmentioning

confidence: 99%

Spatial clusters and temporal trends of seasonal surface soil moisture across China in responses to regional climate and land cover changes

Chen

Liu

et al. 2017

Ecohydrology

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Investigating the spatiotemporal changes of regional surface soil moisture in responses to climate and land cover changes is vital for understanding the underlying mechanisms of hydrological processes. While previous studies mainly attributed the causes of soil moisture changes to climatic factors, few took land cover into consideration. We analyzed the seasonal-differentiated effects of climate and land cover changes on surface soil moisture of China's 80 drainage basins using the Essential Climate Variable Soil Moisture product spanning the 1979-2010 period. The low-low spatial clusters of annual (Jan-Dec), warm-season (Apr-Sept) and cool-season (Oct-Dec and Jan-Mar) surface soil moisture have spread from Northwest China to northeast and even Central China during the past three decades. In cool seasons, significant decreasing trends of surface soil moisture in most drainage basins of Northwest, Northeast, Central, and South China were detected. But in warm seasons, the surface soil moisture in Central and South China showed significant increasing trends. Precipitation, potential evapotranspiration and the ratio of the two are three driving forces for warm-season surface soil moisture changes in Northeast and Northwest China. These climatic factors are main contributors to the declining trends of cool-season surface soil moisture in Central and South China. Land cover changes showed to be the major factor driving the significant decreasing trends of cool-season surface soil moisture in Northwest, Central, and South China. Due to vegetation's self-shading effect, the large-scale reforestation and vegetation growth are believed as main causes of the increasing trends of warm-season surface soil moisture in Central and South China.

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A semi-empirical inversion model for assessing surface soil moisture using AMSR-E brightness temperatures

Cited by 14 publications

References 31 publications

Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979–2010)

Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979–2010)

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review

Spatial clusters and temporal trends of seasonal surface soil moisture across China in responses to regional climate and land cover changes

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