scholarly journals Estimating Root Zone Soil Moisture Across the Eastern United States with Passive Microwave Satellite Data and a Simple Hydrologic Model

2019 ◽  
Vol 11 (17) ◽  
pp. 2013 ◽  
Author(s):  
Douglas Baldwin ◽  
Salvatore Manfreda ◽  
Henry Lin ◽  
Erica A.H. Smithwick
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Spatial Model ◽  
Root Zone ◽  
Hydrologic Model ◽  
Passive Microwave ◽  
Data Product ◽  
Data Products ◽  
Shale Hills

Root zone soil moisture (RZSM) affects many natural processes and is an important component of environmental modeling, but it is expensive and challenging to monitor for relatively small spatial extents. Satellite datasets offer ample spatial coverage of near-surface soil moisture content at up to a daily time-step, but satellite-derived data products are currently too coarse in spatial resolution to use directly for many environmental applications, such as those for small catchments. This study investigated the use of passive microwave satellite soil moisture data products in a simple hydrologic model to provide root zone soil moisture estimates across a small catchment over a two year time period and the Eastern U.S. (EUS) at a 1 km resolution over a decadal time-scale. The physically based soil moisture analytical relationship (SMAR) was calibrated and tested with the Advanced Microwave Scanning Radiometer (AMSRE), Soil Moisture Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) data products. The SMAR spatial model relies on maps of soil physical properties and was first tested at the Shale Hills experimental catchment in central Pennsylvania. The model met a root mean square error (RMSE) benchmark of 0.06 cm3 cm−3 at 66% of the locations throughout the catchment. Then, the SMAR spatial model was calibrated at up to 68 sites (SCAN and AMERIFLUX network sites) that monitor soil moisture across the EUS region, and maps of SMAR parameters were generated for each satellite data product. The average RMSE for RZSM estimates from each satellite data product is <0.06 cm3 cm−3. Lastly, the 1 km EUS regional RZSM maps were tested with data from the Shale Hills, which was set aside for validating the regional SMAR, and the RMSE between the RZSM predictions and the catchment average is 0.042 cm3 cm−3. This study offers a promising approach for generating long time-series of regional RZSM maps with the same spatial resolution of soil property maps.

Cosmic Ray Neutron Sensing: Integration with land surface modelling using data assimilation for improved field-scale soil moisture estimates

2020 ◽  
Author(s):  
Amol Patil ◽  
Benjamin Fersch ◽  
Harrie-Jan Hendricks-Franssen ◽  
Harald Kunstmann
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Land Surface ◽  
Root Zone ◽  
Cosmic Ray ◽  
Soil Parameters ◽  
Surface Model ◽  
Field Scale ◽  
Spatio Temporal

&lt;p&gt;Soil moisture is a key variable in atmospheric modelling to resolve the partitioning of net radiation into sensible and latent heat fluxes. Therefore, high resolution spatio-temporal soil moisture estimation is getting growing attention in this decade. The recent developments to observe soil moisture at field scale (170 to 250 m spatial resolution) using Cosmic Ray Neutron Sensing (CRNS) technique has created new opportunities to better resolve land surface atmospheric interactions; however, many challenges remain such as spatial resolution mismatch and estimation uncertainties. Our study couples the Noah-MP land surface model to the Data Assimilation Research Testbed (DART) for assimilating CRN intensities to update model soil moisture. For evaluation, the spatially distributed Noah-MP was set up to simulate the land surface variables at 1 km horizontal resolution for the Rott and Ammer catchments in southern Germany. The study site comprises the TERENO-preAlpine observatory with five CRNS stations and additional CRNS measurements for summer 2019 operated by our Cosmic Sense research group. We adjusted the soil parametrization in Noah-MP to allow the usage of EU soil data along with Mualem-van Genuchten soil hydraulic parameters. We use independent observations from extensive soil moisture sensor network (SoilNet) within the vicinity of CRNS sensors for validation. Our detailed synthetic and real data experiments are evaluated for the analysis of the spatio-temporal changes in updated root zone soil moisture and for implications on the energy balance component of Noah-MP. Furthermore, we present possibilities to estimate root zone soil parameters within the data assimilation framework to enhance standalone model performance.&lt;/p&gt;

scholarly journals A pan-African high-resolution drought index dataset

2019 ◽  
Author(s):  
Jian Peng ◽  
Simon Dadson ◽  
Feyera Hirpa ◽  
Ellen Dyer ◽  
Thomas Lees ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Root Zone ◽  
Environmental Data ◽  
Climatic Research Unit ◽  
Drought Vulnerability ◽  
Coarse Resolution ◽  
Mass Migration

Abstract. Droughts in Africa cause severe problems such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security over Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectorial perspective that includes crops, hydrological systems, rangeland, and environmental systems. Such assessments are essential for policy makers, their advisors, and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time-Series (TS) datasets, and Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project, as well as with root zone soil moisture modelled by GLEAM. Agreement found between coarse resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with average correlation coefficient (R) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information to study drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) with link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a)

A follow-up for the Soil Moisture and Ocean Salinity mission

2021 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Yann Kerr ◽  
Eric Anterrieu ◽  
Francois Cabot ◽  
Jacqueline Boutin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Root Zone ◽  
Drought Indices ◽  
Weather Forecasts ◽  
Alternative Approach ◽  
Phase 0 ◽  
Ocean Salinity ◽  
Precipitation Estimates ◽  
L Band

&lt;p&gt;The Soil Moisture and Ocean Salinity (SMOS) satellite, launched in 2009 by ESA, has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space with a spatial resolution of ~ 40 km. SMOS data are an essential component of the ESA Climate Change Initiative (CCI) for ocean salinity and soil moisture and they are used by the CCI biomass. A specific SMOS neural network soil moisture product is assimilated operationally at the European Centre for Medium Range Weather Forecasts (ECMWF). L-band surface SM measurements have also been used to estimate root zone soil moisture, to derive drought indices, to enable food security monitoring and to improve satellite precipitation estimates. SMOS data have also been used to detect frozen soils, thin ice-sheets over the ocean and ice melting in Antarctica and Greenland.&lt;/p&gt;&lt;p&gt;Different studies on scientific and operational applications of L-band radiometry have shown the need of the continuity of L-band observations with an increased resolution with respect to the current generation of sensors. Resolutions from 1 km to 10 km would be a breakthrough for many applications over ocean, land and ice. One approach to obtain those resolutions could be downscaling coarse resolution data using an auxiliary dataset with higher resolution. However, using airborne data, we will show that the accuracy of the data downscaled to 1 km decreases significantly when the initial native resolution is 40 km with respect to downscaling from initial resolutions of 5-10 km. We will present two instrumental concepts to reach native resolutions of 5-10 km.&lt;/p&gt;&lt;p&gt;The SMOS-HR mission project, completed the Phase 0 study at the French Centre National d&amp;#8217;Etudes Spatiales (CNES) with contributions from Airbus Defence &amp; Space and CESBIO. The goal was to ensure the continuity of L-band measurements while increasing the spatial resolution to ~10 km, which requires a typical antenna size of ~18 meters. Taking into account the difficulty of deploying a real aperture of this size in space and the successful alternative approach used by SMOS, SMOS-HR will perform aperture synthesis using an array of 230 small antennas distributed in a cross with four 12 m arms. During the Phase A study (ongoing at CNES) a mission concept with a central carrier surrounded by a swarm of nanosatellites will also be studied.&lt;/p&gt;

scholarly journals Mapping Root-Zone Soil Moisture Using a Temperature–Vegetation Triangle Approach with an Unmanned Aerial System: Incorporating Surface Roughness from Structure from Motion

2018 ◽  
Vol 10 (12) ◽  
pp. 1978 ◽  
Author(s):  
Sheng Wang ◽  
Monica Garcia ◽  
Andreas Ibrom ◽  
Jakob Jakobsen ◽  
Christian Josef Köppl ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Roughness ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Structure From Motion ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Root Zone ◽  
Unmanned Aerial Systems ◽  
Natural Ecosystems

High resolution root-zone soil moisture (SM) maps are important for understanding the spatial variability of water availability in agriculture, ecosystems research and water resources management. Unmanned Aerial Systems (UAS) can flexibly monitor land surfaces with thermal and optical imagery at very high spatial resolution (meter level, VHR) for most weather conditions. We modified the temperature–vegetation triangle approach to transfer it from satellite to UAS remote sensing. To consider the effects of the limited coverage of UAS mapping, theoretical dry/wet edges were introduced. The new method was tested on a bioenergy willow short rotation coppice site during growing seasons of 2016 and 2017. We demonstrated that by incorporating surface roughness parameters from the structure-from-motion in the interpretation of the measured land surface-atmosphere temperature gradients, the estimates of SM significantly improved. The correlation coefficient between estimated and measured SM increased from not significant to 0.69 and the root mean square deviation decreased from 0.045 m3∙m−3 to 0.025 m3∙m−3 when considering temporal dynamics of surface roughness in the approach. The estimated SM correlated better with in-situ root-zone SM (15–30 cm) than with surface SM (0–5 cm) which is an important advantage over alternative remote sensing methods to estimate SM. The optimal spatial resolution of the triangle approach was found to be around 1.5 m, i.e. similar to the length scale of tree-crowns. This study highlights the importance of considering the 3-D fine scale canopy structure, when addressing the links between surface temperature and SM patterns via surface energy balances. Our methodology can be applied to operationally monitor VHR root-zone SM from UAS in agricultural and natural ecosystems.

scholarly journals Impact of Soil Moisture Data Resolution on Soil Moisture and Surface Heat Flux Estimates through Data Assimilation: A Case Study in the Southern Great Plains

2019 ◽  
Vol 20 (4) ◽  
pp. 715-730 ◽  
Author(s):  
Yang Lu ◽  
Jianzhi Dong ◽  
Susan C. Steele-Dunne
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Heat Flux ◽  
Root Zone ◽  
Sampling Frequency ◽  
Surface Heat ◽  
Heat Fluxes ◽  
Soil Moisture Data ◽  
Temporal Sampling

Abstract The spatial heterogeneity and temporal variation of soil moisture and surface heat fluxes are key to many geophysical and environmental studies. It has been demonstrated that they can be mapped by assimilating soil thermal and wetness information into surface energy balance models. The aim of this work is to determine whether enhancing the spatial resolution or temporal sampling frequency of soil moisture data could improve soil moisture or surface heat flux estimates. Two experiments are conducted in an area mainly covered by grassland, and land surface temperature (LST) observations from the Geostationary Operational Environmental Satellite (GOES) mission are assimilated together with either an enhanced L-band passive soil moisture product (9 km, 2–3 days) from the Soil Moisture Active Passive (SMAP) mission or a merged product (36 km, quasi-daily) from the SMAP and the Soil Moisture Ocean Salinity (SMOS) mission. The results suggest that the availability of soil moisture observations is increased by 41% after merging data from the SMAP and the SMOS missions. A comparison with results from a previous study that assimilated a coarser SMAP soil moisture product (36 km, 2–3 days) suggests that enhancing the temporal sampling frequency of soil moisture observations leads to improved soil moisture estimates at both the surface and root zone, and the largest improvement is seen in the bias metric (0.008 and 0.007 m3 m−3 on average at the surface and root zone, respectively). Enhancing the spatial resolution, however, does not significantly improve soil moisture estimates, particularly at the surface. Surface heat flux estimates from assimilating soil moisture data of different spatial or temporal resolutions are very similar.

scholarly journals Relationship between soil moisture and vegetation in the Kairouan plain region of Tunisia using low spatial resolution satellite data

2010 ◽  
Vol 46 (6) ◽  
Author(s):  
M. Zribi ◽  
T. Paris Anguela ◽  
B. Duchemin ◽  
Z. Lili ◽  
W. Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Plain Region

Impact of Assimilating Passive Microwave Observations on Root-Zone Soil Moisture Under Dynamic Vegetation Conditions

2012 ◽  
Vol 50 (11) ◽  
pp. 4279-4291 ◽  
Author(s):  
Karthik Nagarajan ◽  
Jasmeet Judge ◽  
Alejandro Monsivais-Huertero ◽  
Wendy D. Graham
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Passive Microwave

scholarly journals A fine-resolution soil moisture dataset for China in 2002–2018

2020 ◽  
Author(s):  
Xiangjin Meng ◽  
Kebiao Mao ◽  
Fei Meng ◽  
Jiancheng Shi ◽  
Jiangyuan Zeng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Matching Problem ◽  
Change Models ◽  
Fine Resolution ◽  
Microwave Data

Abstract. Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture over large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05°, monthly) for China from 2002–2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products (including the AMSR-E/2 Level 3 products and the SMOS-INRA-CESBIO (SMOS-IC) products) calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: −0.024, −0.030 and −0.016 m3/m3, unbiased root mean square error (ubRMSE): 0.051, 0.048 and 0.042, correlation coefficient (R): 0.82, 0.88, and 0.90 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a downward trend (slope = −0.167, R = 0.750) and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in the Zenodo at http://doi.org/10.5281/zenodo.4049958 (Meng et al., 2020).

scholarly journals Seeing Soil Moisture from the Sky

Eos ◽  
2017 ◽  
Vol 98 ◽  
Author(s):  
Jian Peng
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Satellite Data

A recent paper in Reviews of Geophysics describes techniques for improving the spatial resolution of satellite data on soil moisture.

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