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

2008 ◽  
Vol 9 (4) ◽  
pp. 712-727 ◽  
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.

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

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.

scholarly journals LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the CONtiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

2018 ◽  
Author(s):  
Clément Albergel ◽  
Simon Munier ◽  
Aymeric Bocher ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
The United States ◽  
Agricultural Drought ◽  
Reference Network ◽  
Surface Model ◽  
Area Index ◽  
Medium Range Weather Forecast ◽  
The Impact

LDAS-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states and fluxes. LDAS-Monde ingests satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) estimates to constrain the Interactions between Soil, Biosphere, and Atmosphere (ISBA) Land Surface Model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (CTRIP) continental hydrological system (ISBA-CTRIP). LDAS-Monde is forced by the ERA-5 atmospheric reanalysis from the European Center For Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a 7-yr, quarter degree spatial resolution offline reanalysis of Land Surface Variables (LSVs) over CONUS. The impact of assimilating LAI and SSM into LDAS-Monde is assessed over North America, by comparison to satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project, and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Also, taking advantage of the relatively dense data networks over CONUS, we also evaluate the impact of the assimilation against in-situ measurements of soil moisture from the USCRN network (US Climate Reference Network) are used in the evaluation, together with river discharges from the United States Geophysical Survey (USGS) and the Global Runoff Data Centre (GRDC). Those data sets highlight the added value of assimilating satellite derived observations compared to an open-loop simulation (i.e. no assimilation). It is shown that LDAS-Monde has the ability not only to monitor land surface variables but also to forecast them, by providing improved initial conditions which impacts persist through time. LDAS-Monde reanalysis has a potential to be used to monitor extreme events like agricultural drought, also. Finally, limitations related to LDAS-Monde and current satellite-derived observations are exposed as well as several insights on how to use alternative datasets to analyze soil moisture and vegetation state.

scholarly journals DROUGHT MONITORING IN SHANDONG PROVINCE BASED ON MODIFIED TEMPERATURE-VEGETATION-SOIL MOISTURE DRYNESS INDEX (TVMDI)

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 911-916
Author(s):  
H. Wan ◽  
Z. D. Wang ◽  
P. Guo ◽  
B. Wang ◽  
X. C. Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Land Surface ◽  
Vegetation Index ◽  
Shandong Province ◽  
Severe Drought ◽  
Average Correlation ◽  
Shandong Peninsula ◽  
Wide Range ◽  
Dryness Index

Abstract. Drought is one of the frequent natural disasters in Shandong province, which is characterized by high frequency and wide range. In response to frequent droughts that are not monitored in time, monitoring the changes of drought is of great significance to agricultural production and social development. This study used the Temperature-Vegetation-soil Moisture Dryness Index (TVMDI) model, combined with the optical MODIS land surface temperature, vegetation index, surface albedo data and microwave FY-3B soil moisture data, to monitor the drought of Shandong province in 2016. The precipitation and temperature data of weather station were used to validate the monitoring results. The results show that, in 2016, the drought in Shandong province mainly occurred in winter and spring, and the drought in summer was alleviated. From the perspective of space, the northern Shandong and the Shandong peninsula areas are relatively humid with less drought time, while the local areas in the central and southern Shandong province suffer from severe drought with longer drought time. From the perspective of correlation with meteorological factors, the average correlation coefficient between TVMDI and precipitation can reach 0.45, and the average correlation coefficient between TVMDI and temperature can reach 0.63.

scholarly journals A SMAP-Based Drought Monitoring Index for the United States

2018 ◽  
Author(s):  
Sara Sadri ◽  
Eric F. Wood ◽  
Ming Pan
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Warm Season ◽  
The United States ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe between the latitude bands of 85.044° N/S in 2–3 days depending on location. SMAP Level 3 passive radiometer product (SPL3SMP) measures the amount of water in the top 5 cm of soil except for regions of heavy vegetation (vegetation water content >4.5 kg/m2) and frozen or snow covered locations. SPL3SMP retrievals are spatially and temporally discontinuous, so the 33 months offers a short SMAP record length and poses a statistical challenge for meaningful assessment of its indices. The SMAP SPL4SMAU data product provides global surface and root zone soil moisture at 9-km resolution based on assimilating the SPL3SMP product into the NASA Catchment land surface model. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. We describe here SMAP-based indices over the continental United States (CONUS) based on both near-surface and root zone soil moisture percentiles. The percentiles are based on fitting a Beta distribution to the retrieved moisture values. To assess the data adequacy, a statistical comparison is made between fitting the distribution to VIC soil moisture values for the days when SPL3SMP are available, versus fitting to a 1979–2017 VIC data record. For the cold season (November–April), 57 % of grids were deemed to be consistent between the periods, and 68 % in the warm season (May–October), based on a Kolmogorov–Smirnov statistical test. It is assumed that if grids passed the consistency test using VIC data, then the grid had sufficient SMAP data. Our near-surface and root zone drought index on maps are shown to be similar to those produced by the U.S. Drought Monitor (from D0-D4) and GRACE. In a similar manner, we extend the index to include pluvial conditions using indices W0-W4. This study is a step forward towards building a national and international soil moisture monitoring system, without which, quantitative measures of drought and pluvial conditions will remain difficult to judge.

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

2005 ◽  
Vol 18 (1) ◽  
pp. 21-40 ◽  
Author(s):  
Toshihisa Matsui ◽  
Venkataraman Lakshmi ◽  
Eric E. Small
Keyword(s):  
Soil Moisture ◽  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Climate Model ◽  
Monsoon Season ◽  
Stress Index ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Moist Convection

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.

scholarly journals Temporal Calibration of an Evaporation-Based Spatial Disaggregation Method of SMOS Soil Moisture Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1671
Author(s):  
Vivien-Georgiana Stefan ◽  
Olivier Merlin ◽  
Maria-José Escorihuela ◽  
Beatriz Molero ◽  
Jamal Chihrane ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Linear Regression ◽  
Linear Model ◽  
Correlation Coefficient ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remotely Sensed ◽  
Current Version ◽  
Soil Moisture Data

The resolution of current satellite surface soil moisture (SM) estimates is very low, of tens of kilometers, which proves to be insufficient for various agricultural and hydrological applications. Amongst the existing downscaling approaches of remotely sensed SM, DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) improves the resolution of SMOS (Soil Moisture and Ocean Salinity) soil moisture data using soil evaporative efficiency (SEE) estimates at high resolution (HR) and a SEE(SM) model implemented at low resolution (LR). Defined as the ratio of actual to potential soil evaporation, SEE can be derived from the remotely sensed land surface temperature (LST) and normalized difference vegetation index (NDVI). The current version of DISPATCH uses a linear SEE(SM) model. This study aims at improving the SEE(SM) model and testing different calibration strategies, to ultimately have more robust and better downscaled SM products. A nonlinear SEE(SM) model is introduced and its influence on the derived HR SM products is studied over a range of conditions. Each model, linear and nonlinear, is calibrated from remote sensing data on a daily and a multi-date basis. The approaches were tested over two mixed dry and irrigated areas in Catalonia, Spain, and over one dry area in Morocco. When using the linear model, better statistical results were generally obtained using a daily calibration (current version of DISPATCH), most notably over one Spanish site. However, the best results were systematically obtained for an annually calibrated nonlinear model, in terms of all metrics considered: correlation coefficient, slope of the linear regression, bias, unbiased root mean square error. In particular, when using the annually calibrated nonlinear SEE (SM) model, the temporal slope of the linear regression between disaggregated and in situ soil moisture increased to 1.16 and 0.75 for one Spanish site and for the Moroccan site (as opposed to 0.44 and 0.58, respectively, when using the linear model with a daily calibration). The temporal correlation coefficient increased to 0.47 and 0.54 over the Spanish sites (as opposed to 0.18 and 0.27, respectively, when using the linear model with a daily calibration). Those contrasted results indicate compensation effects between the model type and the calibration strategy. Taking into account studies that report the strong nonlinear behavior of the SEE with respect to SM, the introduction of the nonlinear SEE(SM) model in DISPATCH, combined with a multi-date calibration, is proven to perform significantly better under various conditions, leading to more robust disaggregated SM products. The SEE modeling based on the nonlinear SM model, with a multi-date calibration, could be integrated into the CATDS—Centre Aval de Traitement des Données SMOS as a future product, as well as into existing evapotranspiration models, which are based on a combination of thermal and microwave data.

scholarly journals Simulation and Analysis of Land-Surface Processes in the Taklimakan Desert Based on Noah LSM

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Huoqing Li ◽  
Ali Mamtimin ◽  
Chenxiang Ju
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Land Surface ◽  
Exchange Process ◽  
Model Performance ◽  
Surface Model ◽  
Taklimakan Desert ◽  
Observation Data ◽  
Net Radiation ◽  
The Taklimakan Desert

This study evaluated the Noah land-surface model performance to simulate the land-surface process during different weather conditions in the hinterland of the Taklimakan Desert. This study is based on observation data from the Taklimakan Desert Meteorology Field Experiment Station in 2014. The results illustrated that the energy-exchange process between the land surface and the atmosphere in the drifting desert can be simulated by Noah effectively. However, the effects of soil moisture and latent heat flux were very poor. For sunny days, the soil temperature and heat flux were underestimated significantly in the nighttime and overestimated in the daytime. The simulation results are very good in sand-dust weather. The simulation of heat flux and net radiation is very consistent with the observation during cloudy days. For rainy days, the model can successfully model the diurnal variation of soil moisture, but it has obvious deviations in the net radiation, heat flux, and soil heat flux.

scholarly journals Capability of the variogram to quantify the spatial patterns of surface fluxes and soil moisture simulated by land surface models

2021 ◽  
Vol 45 (2) ◽  
pp. 279-293
Author(s):  
S Garrigues ◽  
A Verhoef ◽  
E Blyth ◽  
A Wright ◽  
B Balan-Sarojini ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Spatial Patterns ◽  
Land Surface ◽  
Heat Fluxes ◽  
Surface Model ◽  
Sensible Heat ◽  
Net Radiation ◽  
Land Surface Models ◽  
Surface Models

Up to now, relatively little effort has been dedicated to the quantitative assessment of the differences in spatial patterns of model outputs. In this paper, we employed a variogram-based methodology to quantify the differences in the spatial patterns of root-zone soil moisture, net radiation, and latent and sensible heat fluxes simulated by three land surface models (SURFEX/ISBA, JULES and CHTESSEL) over three European geographic domains – namely, UK, France and Spain. The model output spatial patterns were quantified through two metrics derived from the variogram: i) the variogram sill, which quantifies the degree of spatial variability of the data; and ii) the variogram integral range, which represents the spatial length scale of the data. The higher seasonal variation of the spatial variability of sensible and latent heat fluxes over France and Spain, compared to the UK, is related to a more frequent occurrence of a soil-moisture-limited evapotranspiration regime during summer dry spells in the south of France and Spain. The small differences in spatial variability of net radiation between models indicate that the spatial patterns of net radiation are mostly driven by the climate forcing data set. However, the models exhibit larger differences in latent and sensible heat flux spatial variabilities, which are related to their differences in i) soil and vegetation ancillary datasets and ii) physical process representation. The highest discrepancies in spatial patterns between models are observed for soil moisture, which is mainly related to the type of soil hydraulic function implemented in the models. This work demonstrates the capability of the variogram to enhance our understanding of the spatiotemporal structure of the uncertainties in land surface model outputs. Therefore, we strongly encourage the implementation of the variogram metrics in model intercomparison exercises.

scholarly journals Global Assimilation of Multiangle and Multipolarization SMOS Brightness Temperature Observations into the GEOS-5 Catchment Land Surface Model for Soil Moisture Estimation

2016 ◽  
Vol 17 (2) ◽  
pp. 669-691 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Land Surface Model ◽  
The United States ◽  
Open Loop ◽  
Surface Model ◽  
Surface Soil Temperature

Abstract Multiangle and multipolarization L-band microwave observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated into the Goddard Earth Observing System Model, version 5 (GEOS-5), using a spatially distributed ensemble Kalman filter. A variant of this system is also used for the Soil Moisture Active Passive (SMAP) Level 4 soil moisture product. The assimilation involves a forward simulation of brightness temperatures (Tb) for various incidence angles and polarizations and an inversion of the differences between Tb forecasts and observations into updates to modeled surface and root-zone soil moisture, as well as surface soil temperature. With SMOS Tb assimilation, the unbiased root-mean-square difference between simulations and gridcell-scale in situ measurements in a few U.S. watersheds during the period from 1 July 2010 to 1 July 2014 is 0.034 m3 m−3 for both surface and root-zone soil moisture. A validation against gridcell-scale measurements and point-scale measurements from sparse networks in the United States, Australia, and Europe demonstrates that the assimilation improves both surface and root-zone soil moisture results over the open-loop (no assimilation) estimates in areas with limited vegetation and terrain complexity. At the global scale, the assimilation of SMOS Tb introduces mean absolute increments of 0.004 m3 m−3 to the profile soil moisture content and 0.7 K to the surface soil temperature. The updates induce changes to energy fluxes and runoff amounting to about 15% of their respective temporal standard deviation.

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