scholarly journals A 1-km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019

2022 ◽  
Author(s):  
Peilin Song ◽  
Yongqiang Zhang ◽  
Jianping Guo ◽  
Jiancheng Shi ◽  
Tianjie Zhao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Weather Conditions ◽  
Passive Microwave ◽  
Primary Data ◽  
Hydrological Process ◽  
Surface Soil Moisture ◽  
Agricultural Industry ◽  
Quantitative Studies

Abstract. Surface soil moisture (SSM) is crucial for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary tool for estimating global SSM from the view of satellite, while the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, we developed one such SSM product in China with all these characteristics. The product was generated through downscaling the AMSR-E/AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003–2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that had been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, so that the “all-weather” quality was achieved for the 1-km SSM. Daily images from this developed SSM product have quasi-complete coverage over the country during April–September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations, through a specifically developed sub-model for filling the gap between seams of neighboring PM swaths during the downscaling procedure. The product is well compared against in situ soil moisture measurements from 2000+ meteorological stations, indicated by station averages of the unbiased RMSD ranging from 0.052 vol/vol to 0.059 vol/vol. Moreover, the evaluation results also show that the developed product outperforms the SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km, with a correlation coefficient of 0.55 achieved against that of 0.40 for the latter product. This indicates the new product has great potential to be used for hydrological community, agricultural industry, water resource and environment management.

scholarly journals Assimilation of vegetation optical depth retrievals from passive microwave radiometry

2020 ◽  
Vol 24 (7) ◽  
pp. 3431-3450
Author(s):  
Sujay V. Kumar ◽  
Thomas R. Holmes ◽  
Rajat Bindlish ◽  
Richard de Jeu ◽  
Christa Peters-Lidard
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Optical Sensors ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Weather Conditions ◽  
Passive Microwave ◽  
Surface Model ◽  
Surface Soil Moisture

Abstract. Vegetation optical depth (VOD) retrievals from passive microwave sensors provide analog estimates of above-ground canopy biomass. This study presents the development and analysis of assimilating VOD retrievals from X-, C-, and L-band passive microwave instruments within the Noah-MP land surface model over the Continental U.S. The results from this study demonstrate that the assimilation of VOD retrievals have a significant beneficial impact on the simulation of evapotranspiration and GPP, particularly over the agricultural areas of the U.S. The improvements in the water and carbon fluxes from the assimilation of VOD from X- and C-band sensors are found to be comparable to those obtained from the assimilation of vegetation indices from optical sensors. The study also quantifies the relative and joint impacts of assimilating surface soil moisture and VOD from the Soil Moisture Active Passive (SMAP) mission. The utility of soil moisture assimilation for improving evapotranspiration (ET) is more significant over water-limited regions, whereas VOD DA is more impactful over areas where soil moisture is not the primary controlling factor on ET. The results also indicate that the information on moisture and vegetation states from SMAP can be simultaneously exploited through the joint assimilation of surface soil moisture and VOD. Since passive microwave-based VOD retrievals are available in nearly all weather conditions, their use within data assimilation systems offers the ability to extend and improve the utility obtained from the use of optical/infrared-based vegetation retrievals.

scholarly journals Assimilation of vegetation optical depth retrievals from passive microwave radiometry

2020 ◽  
Author(s):  
Sujay V. Kumar ◽  
Thomas R. Holmes ◽  
Rajat Bindlish ◽  
Richard de Jeu ◽  
Christa Peters-Lidard
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Optical Sensors ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Weather Conditions ◽  
Passive Microwave ◽  
Surface Model ◽  
Surface Soil Moisture

Abstract. Vegetation optical depth (VOD) retrievals from passive microwave sensors provide estimates of above-ground canopy biomass. This study presents the development and analysis of assimilating VOD retrievals from X-, C-, and L-band passive microwave instruments within the Noah-MP land surface model, over the Continental U.S. The results from this study demonstrate that the assimilation of VOD retrievals have a significant beneficial impact on the simulation of evapotranspiration and GPP, particularly over the agricultural areas of the U.S. The improvements in the water and carbon fluxes from the assimilation of VOD from X- and C-band sensors are found to be comparable to those obtained from the assimilation of vegetation indices from optical sensors. The study also quantifies the relative and joint impact of assimilating surface soil moisture and VOD from SMAP. The utility of soil moisture assimilation for improving ET is more significant over water limited regions, whereas VOD DA is more impactful over energy limited domains. The results also indicate that the independent information on moisture and vegetation states from SMAP can be simultaneously exploited through the joint assimilation of surface soil moisture and VOD. Since the passive microwave-based VOD retrievals are available in nearly all weather conditions, their use within data assimilation systems offers the ability to extend and improve the utility obtained from the use of optical/infrared based vegetation retrievals.

The Computation of the Effective Temperature of the Land Surface Covered with Canopy

2014 ◽  
Vol 607 ◽  
pp. 830-834
Author(s):  
Hong Zhang Ma ◽  
Su Mei Liu
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Effective Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Passive Microwave ◽  
Vegetation Canopy ◽  
Surface Soil Moisture ◽  
Remote Sensors ◽  
Energy Exchanges

—Surface soil moisture is an important parameter in describing the water and energy exchanges at the land surface/atmosphere interface. Passive microwave remote sensors have great potential for monitoring surface soil moisture over land surface. The objective of this study is going to establish a model for estimating the effective temperature of land surface covered with vegetation canopy and to investigate how to compute the microwave radiative brightness temperature of land surface covered with vegetation canopy in considering of the canopy scatter effect.

Exploiting the information in soil moisture and vegetation optical depth retrievals from passive microwave radiometry.

2020 ◽  
Author(s):  
Sujay Kumar ◽  
Thomas Holmes ◽  
Rajat bindlish ◽  
Richard de Jeu ◽  
Christa Peters-Lidard
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Land Surface ◽  
Surface Soil ◽  
Passive Microwave ◽  
Microwave Radiometry ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Passive Microwave Radiometry ◽  
The Impact

<p>Historically, microwave radiometry has usually been used for retrieving estimates of soil moisture. As these measurements are also sensitive to vegetation, the attenuation of the microwave signal from vegetation, described by the vegetation optical depth (VOD) parameter can be used an analog of above-ground canopy biomass. This study explores the relative and joint utility of assimilating soil moisture and VOD retrievals from passive microwave radiometry within the NoahMP land surface model. The impact of assimilation on key water and carbon budget terms are quantified through comparisons against reference datasets. The results indicate that the assimilation of soil moisture retrievals has a positive impact on the simulation of surface soil moisture and little impact on evaporative fluxes. In contrast, VOD assimilation has significant impacts on the simulation of vegetation conditions, root zone soil moisture, and evapotranspiration (ET). Over water limited domains with sparse vegetation where soil moisture is the primary control on ET, the assimilation of surface soil moisture is more beneficial than VOD DA. In contrast, over regions with dense vegetation and where water availability is not limiting, transpiration has a significant influence on evapotranspiration. The assimilation of VOD is more beneficial in developing improvements in ET over such areas. The results of this study confirm that soil moisture and VOD retrievals provide independent information that can be jointly exploited through their simultaneous assimilation.</p><p> </p><p> </p>

scholarly journals Monitoring land surface soil moisture: coinversion of visible, infrared and passive microwave sensing data

2012 ◽  
Vol 31 (2) ◽  
pp. 137-142 ◽  
Author(s):  
Jie-Peng ZHAO ◽  
Xian-Feng ZHANG ◽  
Hui-Yi BAO ◽  
Qing-Xi TONG ◽  
Xu-Yang WANG ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Passive Microwave ◽  
Surface Soil Moisture ◽  
Sensing Data

scholarly journals The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

2011 ◽  
Vol 8 (4) ◽  
pp. 6683-6719 ◽  
Author(s):  
R. M. Parinussa ◽  
T. R. H. Holmes ◽  
W. T. Crow
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Passive Microwave ◽  
Surface Soil Moisture ◽  
Ka Band ◽  
L Band ◽  
Soil Moisture Anomaly

Abstract. For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the modern Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and Windsat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and Windsat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and Windsat to obtain surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer. Because of this, ancillary – and potentially less accurate – sources of surface temperature information (e.g. re-analysis data from operational weather prediction centers) must be sought to produce surface soil moisture retrievals. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the R value data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature predictions on the accuracy of Windsat and AMSR-E surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of Ka-band radiometric land surface temperature leads to better soil moisture anomaly estimates compared to those retrieved using MERRA land surface temperature predictions. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates retrieved using MERRA land surface temperature are superior. In addition, the surface temperature phase shifting approach is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a high degree of consistency is noted between evaluation results produced by the TC and Rvalue soil moisture verification approaches.

scholarly journals Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

2016 ◽  
Vol 20 (12) ◽  
pp. 4895-4911 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Incidence Angle ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Errors ◽  
Surface Soil Moisture ◽  
Brightness Temperatures

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

scholarly journals An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data

2019 ◽  
Author(s):  
Bouchra Ait Hssaine ◽  
Olivier Merlin ◽  
Jamal Ezzahar ◽  
Nitu Ojha ◽  
Salah Er-raki ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Energy Balance ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Wide Range ◽  
The Mean

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1 km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014–2018). The field was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015–2016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated αPT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved αPT remains at a mostly constant value (∼ 0.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62 W/m2 for S1, S2, S3 and B1 respectively.

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