Evaluation of satellite and reanalysis estimates of surface and root-zone soil moisture in croplands of Jiangsu Province, China

Fan, Lei; Xing, Zanpin; Lannoy, Gabriëlle De; Frappart, Frédéric; Peng, Jian; Zeng, Jian; Li, Xiaojun; Yang, Kun; Zhao, Tianjie; Shi, Jiancheng; Ma, Hongliang; Wang, Mengjia; Liu, Xiangzhuo; Yi, Chuanxiang; Ma, Mingguo; Tang, Xuguang; Wen, Jianguang; Chen, Xiuzhi; Wang, Chong; Wang, Lingxiao; Wang, Guojie; Wigneron, Jean‐Pierre

doi:10.1016/j.rse.2022.113283

Cited by 22 publications

(9 citation statements)

References 73 publications

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“…Second, although we found similar magnitudes and trends in R SM‐NDVI , the uncertainty of subsurface SM could be more pronounced, as this layer has not provided direct satellite data to assimilate (W. Li et al., 2022). Further comparison with multisource SM is needed to confirm the enhancement of dependence of vegetation on SM in future studies (Fan et al., 2022). Third, this study focused on only a limited number of variables in estimating and attributing variations in R SM‐NDVI .…”

Section: Discussionmentioning

confidence: 99%

Enhanced Dependence of China's Vegetation Activity on Soil Moisture Under Drier Climate Conditions

Zhao

Wang

et al. 2023

JGR Biogeosciences

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Vegetation plays a crucial role in modulating water, carbon, and energy interactions between the land and atmosphere (Humphrey et al., 2018;Seddon et al., 2016). At the same time, vegetation also largely determines the status of ecosystems through its provisioning of ecosystem services for living organisms and carbon dioxide (CO 2 ) uptake (Piao et al., 2015). The latter is key for mitigating climate change by absorbing human-emitted CO 2 (Gentine et al., 2019). The growth of vegetation is largely determined by the availability of soil moisture (SM), especially in water-limited regions, such as arid and semiarid regions (Stocker et al., 2019;Zhao et al., 2019). Under a warming climate, SM has declined in many regions of the world due to decreased precipitation and increased water demand by the atmosphere induced by increased air temperature (Deng et al., 2020a;Jung et al., 2010). In this context, less SM combined with increased drought frequency (Dai, 2012;Zhao et al., 2022c) may drive vegetation to be more soil-water dependent (Berg & Sheffield, 2018). Quantifying and understanding the dependence of vegetation on SM under current climate warming will greatly help in ecosystem management when facing future warming climates.Past studies have documented a tight positive relationship between SM and vegetation in water-limited regions (H.

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Section: Discussionmentioning

confidence: 99%

Enhanced Dependence of China's Vegetation Activity on Soil Moisture Under Drier Climate Conditions

Zhao

Wang

et al. 2023

JGR Biogeosciences

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“…The overestimation of reanalysis products mainly depends on uncertainties of the model structure, parameterization, assimilation scheme and forcing data [85]. In addition, the mismatching between the coarse pixel of these products and the effective footprint of in situ observation sites, as well as inconsistent depths between the products and in situ observation sites, may also introduce some uncertainties into data evaluation [45]. Since soil moisture over the TP can be variable in space, there may also be uncertainty in evaluating the performance of soil moisture products in the entire TP using in situ observations of CAMP/Tibet and ZH2021, which are mainly distributed in the central plateau.…”

Section: Discussionmentioning

confidence: 99%

“…Daily data with missing values can be aggregated to monthly averages when at least five valid values are available within a month [26]. Regarding spatial matching, the product data are obtained for corresponding pixels/grids based on site locations (longitude, latitude) [45].…”

Section: Data Pre-processingmentioning

confidence: 99%

“…For satellite-based surface soil moisture, its performance may be affected by the reference parameters used in the retrieval algorithm [26,44], and the accuracy of original microwave observations (subject to dense vegetation, surface water, anthropogenic radio-frequency interference, etc.) [32,45]. For reanalysis soil moisture, its performance can be affected by the assimilated reference observations, model parameterizations (e.g., deep convection and soil properties) as well as the representation of topography [46,47].…”

Section: Introductionmentioning

confidence: 99%

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Long-Term Characteristics of Surface Soil Moisture over the Tibetan Plateau and Its Response to Climate Change

Zhu,

Li,

Hagan

et al. 2023

Remote Sensing

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Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change.

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“…We also used two SSM data sets from ERA5 and GLEAM for uncertainty analysis. Belonging to the same products as ERA RZSM, ERA SSM reflects the soil water content of the first layer (∼7 cm) (Fan et al., 2022). GLEAM estimates land evaporation and corresponding components globally through remote sensing observations as a process‐based semi‐empirical model.…”

Section: Methodsmentioning

confidence: 99%

Uncovering Current and Future Variations of Irrigation Water Use Across China Using Machine Learning

Liu,

Bo,

et al. 2024

Earth's Future

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Accurately characterizing changes in irrigation water use (IWU) is crucial for formulating optimal water resource allocation policies, particularly in the context of climate change. However, existing IWU estimation methods suffer from uncertainties due to limited data availability and model constraints, restricting their applicability on a national scale and under future climate change scenarios. We present a robust framework leveraging machine learning and multiple data sets to estimate IWU across China. Forced with an ensemble of climate and socio‐economic projections, we appraise future trends and additional costs of IWU. Our model shows high accuracy in reproducing IWU, with coefficient of determination (R2) ranging from 0.86 to 0.91 and root mean square error from 0.261 to 0.361 km3/yr when compared to reported values in Chinese prefectures. Independent validation at 11 cropland sites further confirms the model's predictive power (R2 = 0.67). Under different emissions scenarios, China's IWU is projected to increase by 8.5%–17.1% (6.8%–34.8%) by 2050s (2100s) compared to the historical period (1981–2010), with higher emissions leading to more significant increases. This rise in IWU by 2050s (2100s) comes with an estimated additional cost of US $1.65–3.91 ($2.28–6.5) billion/year, highlighting the urgency for sustainable water management. Our study provides an effective approach for estimating current and future IWU using machine learning techniques, transferable to other countries facing increasing irrigation demands.

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Evaluation of satellite and reanalysis estimates of surface and root-zone soil moisture in croplands of Jiangsu Province, China

Cited by 22 publications

References 73 publications

Enhanced Dependence of China's Vegetation Activity on Soil Moisture Under Drier Climate Conditions

Enhanced Dependence of China's Vegetation Activity on Soil Moisture Under Drier Climate Conditions

Long-Term Characteristics of Surface Soil Moisture over the Tibetan Plateau and Its Response to Climate Change

Uncovering Current and Future Variations of Irrigation Water Use Across China Using Machine Learning

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