The Effects of Satellite-Derived Vegetation Cover Variability on Simulated Land–Atmosphere Interactions in the NAMS

Abstract Substantial evolution of Normalized Difference Vegetation Index (NVDI)-derived vegetation cover (Fg) exists in the southwestern United States and Mexico. The intraseasonal and wet-/dry-year fluctuations of Fg are linked to observed precipitation in the North American monsoon system (NAMS). The manner in which the spatial and temporal variability of Fg influences the land–atmosphere energy and moisture fluxes, and associated likelihood of moist convection in the NAMS regions, is examined. For this, the regional climate model (RCM) is employed, with three different Fg boundary conditions to examine the influence of intraseasonal and wet-/dry-year vegetation variability. Results show that a strong link exists between evaporative fraction (EF), surface temperature, and relative humidity in the boundary layer (BL), which is consistent with a positive soil moisture feedback. However, contrary to expectations, higher Fg does not consistently enhance EF across the NAMS region. This is because the low soil moisture values simulated by the land surface model (LSM) yield high canopy resistance values throughout the monsoon season. As a result, the experiment with the lowest Fg yields the greatest EF and precipitation in the NAMS region, and also modulates regional atmospheric circulation that steers the track of tropical cyclones. In conclusion, the simulated influence of vegetation on land–atmosphere exchanges depends strongly on the canopy stress index parameterized in the LSM. Therefore, a reliable dataset, at appropriate scales, is needed to calibrate transpiration schemes and to assess simulated and realistic vegetation–atmosphere interactions in the NAMS region.

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Towards an integrated soil moisture drought monitor for East Africa

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-4587-2012 ◽

2012 ◽

Vol 9 (4) ◽

pp. 4587-4631 ◽

Cited By ~ 6

Author(s):

W. B. Anderson ◽

B. F. Zaitchik ◽

C. R. Hain ◽

M. C. Anderson ◽

M. T. Yilmaz ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

East Africa ◽

Remote Monitoring ◽

Vegetation Cover ◽

Land Surface ◽

Microwave Remote Sensing ◽

Surface Model ◽

Monitoring Methods ◽

Data Scarcity

Abstract. Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010–2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA) to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, and physically-based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the consistency of anomaly estimates across independent methods. This approach holds potential as a remote soil moisture-based drought monitoring system that is robust across the diverse climatic and ecological zones of East Africa.

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A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

10.31219/osf.io/65xkt ◽

2020 ◽

Author(s):

Toby N. Carlson ◽

George Petropoulos

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Heat Fluxes ◽

Surface Model ◽

Landsat 8 ◽

Simple Method ◽

Practical Applications ◽

Infrared Measurements

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developedscheme is not tied to any particular sensor, it can also beimplemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

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Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

10.5194/hess-2017-338 ◽

2017 ◽

Author(s):

Peter J. Shellito ◽

Eric E. Small

Keyword(s):

Soil Moisture ◽

Soil Texture ◽

Vegetation Cover ◽

Land Surface ◽

Surface Soil ◽

Surface Model ◽

Soil Drying ◽

Potential Evaporation ◽

Surface Soil Moisture ◽

Drying Rates

Abstract. Drydown periods that follow precipitation events provide an opportunity to assess the mechanisms by which soil moisture dissipates from the land surface. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates. Data cover the domain of the North American Land Data Assimilation System phase 2 and span the first 1.8 years of SMAP's operation. Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

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Assessment of the JULES model surface soil moisture using in-situ observations over the Brazilian North East semiarid region

10.5194/egusphere-egu2020-2373 ◽

2020 ◽

Author(s):

Marcelo Zeri ◽

Karina Williams ◽

Eleanor Blyth ◽

Ana Paula Cunha ◽

Toby Marthews ◽

...

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Land Surface ◽

Semiarid Region ◽

Drought Indices ◽

Surface Model ◽

Spatial And Temporal Variability ◽

Drought Risk ◽

Rainfed Agriculture ◽

North East

Monitoring of soil water is essential to assess drought risk over rainfed agriculture. Soil water indicates the onset or progress of dry spells, the start of the rainy season and good periods for sowing or harvesting. Monitoring soil water over rainfed agriculture can be a valuable tool to support field activities and the knowledge of climate risks.A network of soil moisture sensors was established over the Brazilian North East semiarid region in 2015 with measurements at 10 and 20 cm, together with rainfall and other variables in a subset of locations. The data are currently being used to assess the available water over the region in monthly bulletins and reports of potential impacts on yields.In this work, we present a comparison of a dataset of observations from 2015 to 2019 with the soil water estimated by the JULES land surface model (the Joint UK Land Environment Simulator). Overall, the model captures the spatial and temporal variability observed in the measured data well, with an average correlation coefficient of 0.6 across the domain. The performance was compared for each station, resulting in a selection of locations with significant correlation.Based on the regression results, we derive modelled soil moisture for the time span of the JULES run (1979 to 2016). The modeled data enabled the calculation of a standardized soil moisture anomaly (SSMA). The values of SSMA in the period were in agreement with the patterns of drought in the region, especially the recent long-term drought in the Brazilian semiarid region, with significant dry years in 2012, 2013 and 2015. Further analysis will focus on comparisons with other drought indices and measures of impacts on yields at the municipality level.

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Soil moisture–atmosphere feedback dominates land carbon uptake variability

Nature ◽

10.1038/s41586-021-03325-5 ◽

2021 ◽

Vol 592 (7852) ◽

pp. 65-69

Author(s):

Vincent Humphrey ◽

Alexis Berg ◽

Philippe Ciais ◽

Pierre Gentine ◽

Martin Jung ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Climate Models ◽

Climate Model ◽

Dominant Role ◽

Vapour Pressure Deficit ◽

Terrestrial Ecosystems ◽

Surface Model ◽

Carbon Uptake ◽

Model Simulations

AbstractYear-to-year changes in carbon uptake by terrestrial ecosystems have an essential role in determining atmospheric carbon dioxide concentrations1. It remains uncertain to what extent temperature and water availability can explain these variations at the global scale2–5. Here we use factorial climate model simulations6 and show that variability in soil moisture drives 90 per cent of the inter-annual variability in global land carbon uptake, mainly through its impact on photosynthesis. We find that most of this ecosystem response occurs indirectly as soil moisture–atmosphere feedback amplifies temperature and humidity anomalies and enhances the direct effects of soil water stress. The strength of this feedback mechanism explains why coupled climate models indicate that soil moisture has a dominant role4, which is not readily apparent from land surface model simulations and observational analyses2,5. These findings highlight the need to account for feedback between soil and atmospheric dryness when estimating the response of the carbon cycle to climatic change globally5,7, as well as when conducting field-scale investigations of the response of the ecosystem to droughts8,9. Our results show that most of the global variability in modelled land carbon uptake is driven by temperature and vapour pressure deficit effects that are controlled by soil moisture.

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Soil Moisture–Precipitation Feedback Processes in the Indian Summer Monsoon Season

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-06.1 ◽

2012 ◽

Vol 13 (5) ◽

pp. 1461-1474 ◽

Cited By ~ 53

Author(s):

Shakeel Asharaf ◽

Andreas Dobler ◽

Bodo Ahrens

Keyword(s):

Soil Moisture ◽

Summer Monsoon ◽

Indian Summer Monsoon ◽

Land Surface ◽

Climate Model ◽

Monsoon Season ◽

Regional Climate ◽

Precipitable Water ◽

Small Scale ◽

Summer Monsoon Season

Abstract Soil moisture can influence precipitation through a feedback loop with land surface evapotranspiration. A series of numerical simulations, including soil moisture sensitivity experiments, have been performed for the Indian summer monsoon season (ISM). The simulations were carried out with the nonhydrostatic regional climate model Consortium for Small-Scale Modeling (COSMO) in climate mode (COSMO-CLM), driven by lateral boundary conditions derived from the ECMWF Interim reanalysis (ERA-Interim). Positive as well as negative feedback processes through local and remote effects are shown to be important. The regional moisture budget studies have exposed that changes in precipitable water and changes in precipitation efficiency vary in importance, in time, and in space in the simulations for India. Overall, the results show that the premonsoonal soil moisture has a significant influence on the monsoonal precipitation, and thus confirmed that modeling of soil moisture is essential for reliable simulation and forecasting of the ISM.

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An Improved Method for Estimating Global Evapotranspiration Based on Satellite Determination of Surface Net Radiation, Vegetation Index, Temperature, and Soil Moisture

Journal of Hydrometeorology ◽

10.1175/2007jhm911.1 ◽

2008 ◽

Vol 9 (4) ◽

pp. 712-727 ◽

Cited By ~ 119

Author(s):

Kaicun Wang ◽

Shunlin Liang

Keyword(s):

Soil Moisture ◽

Correlation Coefficient ◽

Land Surface ◽

Vegetation Index ◽

The United States ◽

Ratio Method ◽

Daily Maximum ◽

Surface Model ◽

Net Radiation ◽

Improved Method

Abstract A simple and accurate method to estimate regional or global latent heat of evapotranspiration (ET) from remote sensing data is essential. The authors proposed a method in an earlier study that utilized satellite-determined surface net radiation (Rn), a vegetation index, and daytime-averaged/daily maximum air temperature (Ta) or land surface temperature (Ts) data. However, the influence of soil moisture (SM) on ET was not considered and is addressed in this paper by incorporating the diurnal Ts range (DTsR). ET, measured by the energy balance Bowen ratio method at eight enhanced facility sites on the southern Great Plains in the United States and by the eddy covariance method at four AmeriFlux sites during 2001–06, is used to validate the improved method. Site land cover varies from grassland, native prairie, and cropland to deciduous forest and evergreen forest. The correlation coefficient between the measured and predicted 16-day daytime-averaged ET using a combination of Rn, enhanced vegetation index (EVI), daily maximum Ts, and DTsR is about 0.92 for all the sites, the bias is −1.9 W m−2, and the root-mean-square error (RMSE) is 28.6 W m−2. The sensitivity of the revised method to input data error is small. Implemented here is the revised method to estimate global ET using diurnal Ta range (DTaR) instead of DTsR because DTsR data are not available yet, although DTaR-estimated ET is less accurate than DTsR-estimated ET. Global monthly ET is calculated from 1986 to 1995 at a spatial resolution of 1° × 1° from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary monthly dataset and is compared with the 15 land surface model simulations of the Global Soil Wetness Project-2. The results of the comparison of 118 months of global ET show that the bias is 4.5 W m−2, the RMSE is 19.8 W m−2, and the correlation coefficient is 0.82. Incorporating DTaR distinctively improves the accuracy of the estimate of global ET.

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Sensitivity study of the Regional Climate Model RegCM4 to different convective schemes over West Africa

10.5194/esd-2018-44 ◽

2018 ◽

Author(s):

Brahima Koné ◽

Arona Diedhiou ◽

N'datchoh Evelyne Touré ◽

Mouhamadou Bamba Sylla ◽

Filippo Giorgi ◽

...

Keyword(s):

West Africa ◽

Land Surface ◽

Climate Model ◽

West African ◽

Climate System ◽

Surface Model ◽

Spatial And Temporal Variability ◽

The West ◽

Taylor Diagram ◽

African Climate

Abstract. The latest version of RegCM4 with CLM4.5 as land surface scheme was used to assess the performance and the sensitivity of the simulated West African climate system to different convection schemes. The sensitivity studies were performed over the West Africa domain from November 2002 to December 2004, at spatial resolution of 50 km × 50 km and involved five (5) convective schemes: (i) Emanuel; (ii) Grell; (iii) Emanuel over land and Grell over ocean (Mix1); (iv) Grell over land and Emanuel over ocean (Mix2); and (v) Tiedtke. All simulations were forced with ERA-Interim data. Validation of surface temperature at 2 m and precipitation were conducted using respectively data from the Climate Research Unit (CRU) and Global Precipitation Climatology Project (GPCP) during June to September (rainy season). Quantitative assessment of the sensitivity tests were carried out using the mean bias, the pattern correlation coefficient, the root mean square difference, the probability density function of the temperature bias and the Taylor diagram. Results revealed a better performance of the configuration with Emanuel convection scheme to simulate the spatial and temporal variability of the temperature and the precipitation. Therefore, the configuration of RegCM4 with CLM4.5 as land surface model and implementing Emanuel convective scheme is recommended for the study of the West African climate system.

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The Soil Moisture–Atmosphere Coupling Experiment (SMACEX): Background, Hydrometeorological Conditions, and Preliminary Findings

Journal of Hydrometeorology ◽

10.1175/jhm456.1 ◽

2005 ◽

Vol 6 (6) ◽

pp. 791-804 ◽

Cited By ~ 84

Author(s):

William P. Kustas ◽

Jerry L. Hatfield ◽

John H. Prueger

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Vegetation Cover ◽

Land Surface ◽

Surface States ◽

Spatial And Temporal Variability ◽

Special Issue ◽

The Impact ◽

Hydrometeorological Conditions ◽

Coupling Experiment

Abstract The Soil Moisture–Atmosphere Coupling Experiment (SMACEX) was conducted in conjunction with the Soil Moisture Experiment 2002 (SMEX02) during June and July 2002 near Ames, Iowa—a corn and soybean production region. The primary objective of SMEX02 was the validation of microwave soil moisture retrieval algorithms for existing and new prototype satellite microwave sensor systems under rapidly changing crop biomass conditions. The SMACEX study was designed to provide direct measurement/remote sensing/modeling approaches for understanding the impact of spatial and temporal variability in vegetation cover, soil moisture, and other land surface states on turbulent flux exchange with the atmosphere. The unique dataset consisting of in situ and aircraft measurements of atmospheric, vegetation, and soil properties and fluxes allows for a detailed and rigorous analysis, and the validation of surface states and fluxes being diagnosed using remote sensing methods at various scales. Research results presented in this special issue have illuminated the potential of satellite remote sensing algorithms for soil moisture retrieval, land surface flux estimation, and the assimilation of surface states and diagnostically modeled fluxes into prognostic land surface models. Ground- and aircraft-based remote sensing of the land surface and atmospheric boundary layer properties are used to quantify heat fluxes at the tower footprint and regional scales. Tower- and aircraft-based heat and momentum fluxes are used to evaluate local and regional roughness. The spatial and temporal variations in water, energy, and carbon fluxes from the tower network and aircraft under changing vegetation cover and soil moisture conditions are evaluated. An overview of the experimental site, design, data, hydrometeorological conditions, and results is presented in this introduction, and serves as a preface to this special issue highlighting the SMACEX results.

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Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach

Remote Sensing ◽

10.3390/rs13163293 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3293

Author(s):

Nicola Montaldo ◽

Laura Fois ◽

Roberto Corona

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Vegetation Index ◽

Cloud Model ◽

Land Surface Model ◽

Optical Observations ◽

Surface Model ◽

Parameter Estimates ◽

Major Advance ◽

Simplified Approach

The new constellation of synthetic aperture radar (SAR) satellite, Sentinel-1, provides images at a high spatial resolution (up to 10 m) typical of radar sensors, but also at high time resolutions (6–12 revisit days), representing a major advance for the development of operational soil moisture mapping at a plot scale. Our objective was to develop and test an operational approach to assimilate Sentinel 1 observations in a land surface model, and to demonstrate the potential of the use of the new satellite sensors in soil moisture predictions in a grass field. However, for soil moisture retrievals from Sentinel 1 observations in grasslands, there is still the need to identify robust and parsimonious solutions, accounting for the effects of vegetation attenuation and their seasonal variability. In a grass experimental site in Sardinia, where field measurements of soil moisture were available for the 2016–2018 period, three common retrieval methods have been compared to estimate soil moisture from Sentinel 1 data, with increasing complexity and physical interpretation of the processes: the empirical change detection method, the semi-empirical Dubois model, and the physically-based Fung model. In operational approaches for soil moisture mapping from remote sensing, the parameterization simplification of soil moisture retrieval techniques is encouraged, looking for parameter estimates without a priori information. We have proposed a simplified approach for estimating a key parameter of retrieval methods, the surface roughness, from the normalized difference vegetation index (NDVI) derived by simultaneous Sentinel 2 optical observations. Soil moisture was estimated better using the proposed approach and the Dubois model than by using the other methods, which accounted vegetation effects through the common water cloud model. Furthermore, we successfully merged radar-based soil moisture observations and a land surface model, through a data assimilation approach based on the Ensemble Kalman filter, providing robust predictions of soil moisture.

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