scholarly journals Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China

2022 ◽  
Vol 271 ◽  
pp. 112891
Author(s):  
Jingyao Zheng ◽  
Tianjie Zhao ◽  
Haishen Lü ◽  
Jiancheng Shi ◽  
Michael H. Cosh ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin

scholarly journals A Comparison of in Situ, Reanalysis, and Satellite Water Budgets over the Upper Colorado River Basin

2013 ◽  
Vol 14 (3) ◽  
pp. 888-905 ◽  
Author(s):  
Rebecca A. Smith ◽  
Christian D. Kummerow
Keyword(s):  
Soil Moisture ◽  
Interannual Variability ◽  
River Basin ◽  
Water Budget ◽  
Colorado River ◽  
Colorado River Basin ◽  
Upper Colorado River Basin ◽  
Precipitation Estimates

Abstract Using in situ, reanalysis, and satellite-derived datasets, surface and atmospheric water budgets of the Upper Colorado River basin are analyzed. All datasets capture the seasonal cycle for each water budget component. For precipitation, all products capture the interannual variability, though reanalyses tend to overestimate in situ while satellite-derived precipitation underestimates. Most products capture the interannual variability of evapotranspiration (ET), though magnitudes differ among the products. Variability and magnitude among storage volume change products widely vary. With regards to the surface water budget, the strongest connections exist among precipitation, ET, and soil moisture, while snow water equivalent (SWE) is best correlated with runoff. Using in situ precipitation estimates, the Max Planck Institute (MPI) ET estimates, and accumulated runoff, changes in storage are calculated and compare well with estimated changes in storage calculated using SWE, reservoir, and the Climate Prediction Center’s soil moisture. Using in situ precipitation estimates, MPI ET estimates, and atmospheric divergence estimates from the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) results in a long-term atmospheric storage change estimate of −73 mm. Long-term surface storage estimates combined with long-term runoff come close to balancing with long-term atmospheric convergence from ERA-Interim. Increasing the MPI ET by 5% leads to a better balance between surface storage changes, runoff, and atmospheric convergence. It also brings long-term atmospheric storage changes to a better balance at +13 mm.

Validation of AMSR-E Soil Moisture Retrievals over Huaihe River Basin, in China

2014 ◽  
Vol 507 ◽  
pp. 855-858 ◽  
Author(s):  
Xing Mei Xie ◽  
Jing Wen Xu ◽  
Jun Fang Zhao ◽  
Shuang Liu ◽  
Peng Wang
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Temporal Dynamics ◽  
Absolute Error ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Earth Observing System ◽  
X Band ◽  
Precipitation Events

The two soil moisture retrieval methods based on the Advanced Microwave Scanning Radiometer of the Earth Observing System (AMSR-E) data, the standard algorithm by NASA and Land Parameter Retrieval Model (LPRM) have been validated at Xuchang site in Huaihe River basin, in China. The NASA dataset fails to capture main fluctuations of soil moisture, while the LPRM exhibits stronger agreement with the temporal dynamics and precipitation events associated with in situ soil moisture. The LPRM X-band product over ascending pass performs best with correlation coefficient value of 0.42, root mean square error ranging from 0.18 and mean absolute error of 0.14. Generally, the useful soil moisture information can be extracted over HRB from AMSR-E passive microwave data.

In Situ Soil Moisture Dataset in the Zhonggou River Basin of Loess Plateau (2018)

GCdataPR ◽  
2020 ◽  
Author(s):  
li DI ◽  
Xiaoying LI ◽  
Zhini CHEN ◽  
Jun ZHANG ◽  
Haixia HUANG ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Loess Plateau

scholarly journals Downscaling of SMAP Soil Moisture in the Lower Mekong River Basin

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 56 ◽  
Author(s):  
Chelsea Dandridge ◽  
Bin Fang ◽  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
River Basin ◽  
River Basins ◽  
Large River ◽  
Mekong River ◽  
Mekong River Basin ◽  
In Situ Data ◽  
Large River Basins

In large river basins where in situ data were limited or absent, satellite-based soil moisture estimates can be used to supplement ground measurements for land and water resource management solutions. Consistent soil moisture estimation can aid in monitoring droughts, forecasting floods, monitoring crop productivity, and assisting weather forecasting. Satellite-based soil moisture estimates are readily available at the global scale but are provided at spatial scales that are relatively coarse for many hydrological modeling and decision-making purposes. Soil moisture data are obtained from NASA’s soil moisture active passive (SMAP) mission radiometer as an interpolated product at 9 km gridded resolution. This study implements a soil moisture downscaling algorithm that was developed based on the relationship between daily temperature change and average soil moisture under varying vegetation conditions. It applies a look-up table using global land data assimilation system (GLDAS) soil moisture and surface temperature data, and advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS LST and NDVI are used to obtain downscaled soil moisture estimates. These estimates are then used to enhance the spatial resolution of soil moisture estimates from SMAP 9 km to 1 km. Soil moisture estimates at 1 km resolution are able to provide detailed information on the spatial distribution and pattern over the regions being analyzed. Higher resolution soil moisture data are needed for practical applications and modelling in large watersheds with limited in situ data, like in the Lower Mekong River Basin (LMB) in Southeast Asia. The 1 km soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring and agricultural productivity predictions for large river basins.

scholarly journals Synergistic Calibration of a Hydrological Model Using Discharge and Remotely Sensed Soil Moisture in the Paraná River Basin

2021 ◽  
Vol 13 (16) ◽  
pp. 3256
Author(s):  
Ayan Santos Fleischmann ◽  
Ahmad Al Bitar ◽  
Aline Meyer Oliveira ◽  
Vinícius Alencar Siqueira ◽  
Bibiana Rodrigues Colossi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
South America ◽  
River Basin ◽  
Paraná River ◽  
Hydrological Models ◽  
Model Optimization ◽  
Parana River ◽  
Multiple Variables ◽  
Paraná River Basin

Hydrological models are useful tools for water resources studies, yet their calibration is still a challenge, especially if aiming at improved estimates of multiple components of the water cycle. This has led the hydrologic community to look for ways to constrain models with multiple variables. Remote sensing estimates of soil moisture are very promising in this sense, especially in large areas for which field observations may be unevenly distributed. However, the use of such data to calibrate hydrological models in a synergistic way is still not well understood, especially in tropical humid areas such as those found in South America. Here, we perform multiple scenarios of multiobjective model optimization with in situ discharge and the SMOS L4 root zone soil moisture product for the Upper Paraná River Basin in South America (drainage area > 900,000 km²), for which discharge data for 136 river gauges are used. An additional scenario is used to compare the relative impacts of using all river gauges and a small subset containing nine gauges only. Across the basin, the joint calibration (CAL-DS) using discharge and soil moisture leads to improved precision and accuracy for both variables. The discharges estimated by CAL-DS (median KGE improvement for discharge was 0.14) are as accurate as those obtained with the calibration with discharge only (median equal to 0.14), while the CAL-DS soil moisture retrieval is practically as accurate (median KGE improvement for soil moisture was 0.11) as that estimated using the calibration with soil moisture only (median equal to 0.13). Nonetheless, the individual calibration with discharge rates is not able to retrieve satisfactory soil moisture estimates, and vice versa. These results show the complementarity between these two variables in the model calibration and highlight the benefits of considering multiple variables in the calibration framework. It is also shown that, by considering only nine gauges instead of 136 in the model optimization, the model is able to estimate reasonable discharge and soil moisture, although relatively less accurately and with less precision than for the entire dataset. In summary, this study shows that, for poorly gauged tropical basins, the joint calibration of SMOS soil moisture and a few in situ discharge gauges is capable of providing reasonable discharge and soil moisture estimates basin-wide and is more preferable than performing only a discharge-oriented optimization process.

scholarly journals Temporal variation of soil moisture over the Wuding River basin assessed with an eco-hydrological model, in-situ observations and remote sensing

2009 ◽  
Vol 13 (7) ◽  
pp. 1375-1398 ◽  
Author(s):  
S. Liu ◽  
X. Mo ◽  
W. Zhao ◽  
V. Naeimi ◽  
D. Dai ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Seasonal Pattern ◽  
Hydrological Process ◽  
Area Index ◽  
Significance Level ◽  
The Wuding River

Abstract. The change pattern and trend of soil moisture (SM) in the Wuding River basin, Loess Plateau, China is explored based on the simulated long-term SM data from 1956 to 2004 using an eco-hydrological process-based model, Vegetation Interface Processes model, VIP. In-situ SM observations together with a remotely sensed SM dataset retrieved by the Vienna University of Technology are used to validate the model. In the VIP model, climate-eco-hydrological (CEH) variables such as precipitation, air temperature and runoff observations and also simulated evapotranspiration (ET), leaf area index (LAI), and vegetation production are used to analyze the soil moisture evolution mechanism. The results show that the model is able to capture seasonal SM variations. The seasonal pattern, multi-year variation, standard deviation and coefficient of variation (CV) of SM at the daily, monthly and annual scale are well explained by CEH variables. The annual and inter-annual variability of SM is the lowest compared with that of other CEH variables. The trend analysis shows that SM is in decreasing tendency at α=0.01 level of significance, confirming the Northern Drying phenomenon. This trend can be well explained by the decreasing tendency of precipitation (α=0.1) and increasing tendency of temperature (α=0.01). The decreasing tendency of runoff has higher significance level (α=0.001). Because of SM's decreasing tendency, soil evaporation (ES) is also decreasing (α=0.05). The tendency of net radiation (Rn), evapotranspiration (ET), transpiration (EC), canopy intercept (EI) is not obvious. Net primary productivity (NPP), of which the significance level is lower than α=0.1, and gross primary productivity (GPP) at α=0.01 are in increasing tendency.

scholarly journals Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2875
Author(s):  
Yuanyuan Wen ◽  
Jun Zhao ◽  
Guofeng Zhu ◽  
Ri Xu ◽  
Jianxia Yang
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Northwest China ◽  
Moisture Distribution ◽  
Heihe River ◽  
Mountain Areas ◽  
Downscaling Method ◽  
Residual Correction ◽  
Better Than

Passive microwave surface soil moisture (SSM) products tend to have very low resolution, which massively limits their application and validation in regional or local-scale areas. Many climate and hydrological studies are urgently needed to evaluate the suitability of satellite SSM products, especially in alpine mountain areas where soil moisture plays a key role in terrestrial atmospheric exchanges. Aiming to overcome this limitation, a downscaling method based on random forest (RF) was proposed to disaggregate satellite SSM products. We compared the ability of the downscaled soil moisture active passive (SMAP) SSM and soil moisture and ocean salinity satellite (SMOS) SSM products to capture soil moisture information in upstream of the Heihe River Basin by using in situ measurements, the triple collocation (TC) method and temperature vegetation dryness index (TVDI). The results showed that the RF downscaling method has strong applicability in the study area, and the downscaled results of the two products after residual correction have more details, which can better represent the spatial distribution of soil moisture. The validation with the in situ SSM measurements indicates that the correlation between downscaled SMAP and in situ SSM is better than downscaled SMOS at both point and watershed scales in the Babaohe River Basin. From the TC method, the root mean square error (RMSE) of the CLDAS (CMA land data assimilation system), downscaled SMAP and downscaled SMOS were 0.0265, 0.0255 and 0.0317, respectively, indicating that the downscaled SMAP has smaller errors in the study area than others. However, the soil moisture distribution in the study area shown by the SMOS downscaled results is closer than the downscaled SMAP to the degree of drought reflected by TVDI. Overall, this study suggests that the proposed RF-based downscaling method can capture the variation of SSM well, and the downscaled SMAP products perform significantly better than the downscaled SMOS products after the accuracy verification and error analysis of the downscaled results, and it should be helpful to facilitate applications for satellite SSM products at small scales.

scholarly journals Temporal variation of soil moisture over the Wuding River Basin assessed with an eco-hydrological model, in-situ observations and remote sensing

2008 ◽  
Vol 5 (6) ◽  
pp. 3557-3604
Author(s):  
S. Liu ◽  
X. Mo ◽  
W. Zhao ◽  
V. Naeimi ◽  
D. Dai ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Seasonal Pattern ◽  
Ecological Factors ◽  
Area Index ◽  
The Wuding River ◽  
Soil And Water ◽  
Integrative Management

Abstract. For integrative management of soil and water in the Wuding River basin, Loess plateau, China, where severe soil erosion damages are incurred, the ecohydrological behavior of the region is needed to be explored. In this study we focus on the evolution of soil moisture (SM) in the basin. Since there are only twelve years in-situ SM measurements available at two stations from 1992 to 2004, an eco-hydrological processes-based model (VIP, Vegetation Interface Processes model) is employed to simulate the long-term SM, evapotranspiration (ET), vegetation cover and production variation from 1956 to 2004, for the mechanical analysis of SM change. In-situ SM observations and a remotely sensed SM dataset retrieved by the Vienna University of Technology are used to validate the model. The results show that the model is able to capture seasonal SM variations. The seasonal pattern, multi-year variation, standard deviation and CV (coefficient of the variation) of SM at the daily, monthly and annual scale are well explained by the climatic and ecological factors such as precipitation, temperature, net radiation, evapotranspiration, and Leaf Area Index (LAI, denoted as LAI). The annual and inter-annual variability of SM is the lowest comparing with that for other 11-ecohydrological variables. The trend analysis shows that SM is in decreasing tendency at ∝=0.01 level of significance. Its significance is lower than that of runoff and that of temperature (∝=0.001), whereas higher than that of precipitation (∝=0.1). The products of these long-term SM data aim to help integrative management of soil and water resources.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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