Estimating Soil Temperature Research Articles

A Synergetic Algorithm for Mid-Morning Land Surface Soil and Vegetation Temperatures Estimation Using MSG-SEVIRI Products and TERRA-MODIS Products

Land surface is normally considered as a mixture of soil and vegetation. Many applications, such as drought monitoring and crop-yield estimation, benefit from accurate retrieval of both soil and vegetation temperatures through satellite observation. A preliminary study has been conducted in this study on the estimation of land surface soil and vegetation component temperature using the geostationary satellite data acquired by Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) and TERRA-MODIS data. A synergetic algorithm is proposed to derive soil and vegetation temperatures by using the temporal and spatial information in SEVIRI and MODIS products. The approach is applied to both simulation data and satellite data. For simulation data, the component temperatures are well estimated with root mean squared error (RMSE) close to 0 K. For satellite data application, reasonable spatial distributions of the soil and vegetation temperatures are derived for eight cloud-free days in the Iberian Peninsula from June to August 2009. An evaluation is performed for the estimated vegetation temperature against the near surface air temperature. The correlation analysis between two datasets is found that the R-squareds are from 0.074 to 0.423 and RMSEs are within 4 K. Considering the impact of fraction of vegetation cover (FVC) on the validation, the pixels with FVC less than 30% are excluded in the total data comparison, and an obvious improvement is achieved with R-squared from 0.231 to 0.417 and RMSE from 2.9 K to 2.58 K. The validation indicates that the proposed algorithm is able to provide reasonable estimations of soil and vegetation temperatures. It is a potential way to map soil and vegetation temperature for large areas.

Daily soil temperature modeling using neuro-fuzzy approach

Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models’ performances at arid and semiarid locations showed that the CANFIS models’ performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.

Assimilation of multiple data types for improved heat flux prediction: A one-dimensional field study

Accurate latent (LE) and sensible (H) heat flux partitioning from Land Surface Models (LSMs) is important for numerical weather prediction. Land data assimilation can play a key role in improving heat flux prediction by merging information from a range of remotely sensed products with LSMs. This paper demonstrates this potential for an open grassland site in Australia via one-dimensional experiments spanning a year-long period. With a focus on how a LSM is impacted, in-situ field observations were assimilated. Data types as available from passive microwave and thermal infra-red remote sensors were tested for their impact, with individual and joint assimilation of LE and H, near-surface soil moisture, and skin temperature observations—all on time scales approximating satellite overpass intervals. Assessed against independent data from field observations, the multi-observation approach of joint near-surface soil moisture and skin temperature assimilation made the greatest improvements to LE (expressed as daily evapotranspiration; ET), being slightly better than for joint LE and H assimilation. This result questions the value of using LE and H retrievals from thermal imagery within an assimilation context. Individually, skin temperature assimilation was one of the best performers for soil temperature estimates but with degraded root-zone soil moisture estimates and minimal ET improvements. Likewise, near-surface soil moisture assimilation produced the greatest root-zone soil moisture improvement but with relatively modest ET improvement. Combined near-surface soil moisture and skin temperature assimilation balanced the improvements to both soil moisture and temperature states along with strong improvements to ET estimates, highlighting the benefits of multi-observation assimilation.

Spatiotemporal modeling of monthly soil temperature using artificial neural networks

Soil temperature data are critical for understanding land–atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models

The aim of this study is to estimate the soil temperatures of a target station using only the soil temperatures of neighboring stations without any consideration of the other variables or parameters related to soil properties. For this aim, the soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the earth surface at eight measuring stations in Turkey. Firstly, the multiple nonlinear regression analysis was performed with the "Enter" method to determine the relationship between the values of target station and neighboring stations. Then, the stepwise regression analysis was applied to determine the best independent variables. Finally, an artificial neural network (ANN) model was developed to estimate the soil temperature of a target station. According to the derived results for the training data set, the mean absolute percentage error and correlation coefficient ranged from 1.45% to 3.11% and from 0.9979 to 0.9986, respectively, while corresponding ranges of 1.685-3.65% and 0.9988-0.9991, respectively, were obtained based on the testing data set. The obtained results show that the developed ANN model provides a simple and accurate prediction to determine the soil temperature. In addition, the missing data at the target station could be determined within a high degree of accuracy.

An assessment of surface soil temperature products from numerical weather prediction models using ground‐based measurements

Surface soil temperature estimates at approximately 0.05 m depth are needed to retrieve soil moisture from the planned Soil Moisture Active Passive (SMAP) L‐band (1.4 GHz) satellite. Numerical weather prediction (NWP) systems as operated by various weather centers produce global estimates of soil temperature. In this study in situ data collected over the state of Oklahoma are used to assess surface (soil) temperature from three NWP systems: (1) the integrated forecast system from the European Center for Medium range Weather Forecasts (ECMWF), (2) the modern‐era retrospective analysis for research and applications (MERRA) from the NASA Global Modeling and Assimilation Office, and (3) the global data assimilation system used by the National Center for Environmental Prediction (NCEP). The results are presented by hour of day with specific attention directed to the SMAP early morning overpass time at around 6 A.M. local time, and the period of 1 April to 1 October 2009. It was found that the NWP systems estimate the 0.05 m soil temperature at this time of day with an overall root mean square error of 1.9 to 2.0 K. It is shown that this error can be reduced to 1.6 to 1.8 K when differences between the modeling and measurement depth are accounted for by synchronizing each NWP set to match the mean phase of the in situ data and adjusting the amplitude in accordance with heat flow principles. These results indicate that with little calibration all products meet the SMAP error budget criteria over Oklahoma.

Models for estimation of hourly soil temperature at 5cm depth and for degree-day accumulation from minimum and maximum soil temperature

A model has been developed for the daily variation in soil temperature at 5 cm depth, for use where both the minimum and maximum temperatures are known or can be estimated. The model is based on data from three Australian sites with minute-by-minute data over 3–7 years. The model uses two sine curves; one for the increase from minimum to maximum and another for the relatively rapid decrease in temperature immediately after the maximum. An exponential decay function is used for the slower decrease in temperature until the minimum is reached. The time of the minimum soil temperature is primarily determined by the time of sunrise and therefore varies depending on the day of the year, whereas the time of the maximum temperature is influenced primarily by the time of the middle of the day (midpoint between sunrise and sunset). The time of the transition point between the maximum and the next minimum is related to the time of sunset. Therefore, the model uses latitude, longitude, and the day of the year to determine the time of sunrise and sunset to adjust the shape of the temperature profile throughout the day. The model has been validated using 3-hourly soil temperature data for 35 other sites in Australia, with a correlation of 0.993 between actual 3-hourly temperatures and those predicted. Its use for degree-day calculations has been validated using hourly data from a site in Victoria, where the model’s estimates of degree-days differ <0.7% from the value based on individual hourly temperatures, whereas methods that assume a symmetrical change from maximum to minimum temperature overestimate degree-days by 6–7%.

Artificial neural network model for estimating the soil temperature

Ozturk, M., Salman, O. and Koc, M. 2011. Artificial neural network model for estimating the soil temperature. Can. J. Soil Sci. 91: 551–562. Although soil temperature is a critically important agricultural and environmental factor, it is typically monitored with low spatial resolution and, as a result, methods are required to estimate soil temperature at locations remote from monitoring stations. In this study, cost-effective, feed-forward artificial neural network (ANN) models are developed and tested for estimating soil temperature at 5-, 10-, 20-, 50- and 100-cm depths using standard geographical and meteorological data (i.e., altitude, latitude, longitude, month, year, monthly solar radiation, monthly sunshine duration and monthly mean air temperature). These data plus measured monthly mean soil temperature were collected for 2006–2008 from 66 monitoring stations distributed throughout Turkey to obtain a total of 2376 data records (36 months×66 monitoring stations) for each of the five soil depths. At each soil depth, 1800 randomly selected data records were used to develop and train a separate ANN model, and the remaining 576 records at each depth were used to test and validate the resulting models. Good agreement was obtained between ANN-estimated soil temperature and measured soil temperature, as evidenced by correlation coefficients of 98.91, 97.99, 99.03, 98.26 and 95.37% for the 5-, 10-, 20-, 50- and 100-cm soil depths, respectively. It was concluded that ANN modeling is a reliable method for predicting monthly mean soil temperature in regions of Turkey where soil temperature monitoring stations are not present.

Artificial neural network model for estimating the soil temperature

Ozturk, M., Salman, O. and Koc, M. 2011. Artificial neural network model for estimating the soil temperature. Can. J. Soil Sci. 91: 551-562. Although soil temperature is a critically important agricultural and environmental factor, it is typically monitored with low spatial resolution and, as a result, methods are required to estimate soil temperature at locations remote from monitoring stations. In this study, cost-effective, feed-forward artificial neural network (ANN) models are developed and tested for estimating soil temperature at 5-, 10-, 20-, 50- and 100-cm depths using standard geographical and meteorological data (i.e., altitude, latitude, longitude, month, year, monthly solar radiation, monthly sunshine duration and monthly mean air temperature). These data plus measured monthly mean soil temperature were collected for 2006-2008 from 66 monitoring stations distributed throughout Turkey to obtain a total of 2376 data records (36 months×66 monitoring stations) for each of the five soil depths. At...

A weighted coefficient model for estimation of Australian daily soil temperature at depths of 5cm to 100cm based on air temperature and rainfall

Soil temperatures are related to air temperature and rainfall on the current day and preceding days, and this can be expressed in a non-linear relationship to provide a weighted value for the effect of air temperature or rainfall based on days lag and soil depth. The weighted minimum and maximum air temperatures and weighted rainfall can then be combined with latitude and a seasonal function to estimate soil temperature at any depth in the range 5–100 cm. The model had a root mean square deviation of 1.21–1.85°C for minimum, average, and maximum soil temperature for all weather stations in Australia (mainland and Tasmania), except for maximum soil temperature at 5 and 10 cm, where the model was less precise (3.39° and 2.52°, respectively). Data for this analysis were obtained from 32–40 Bureau of Meteorology weather stations throughout Australia and the proposed model was validated using 5-fold cross-validation.

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (TS) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of TS from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily TS at six depths (5, 10, 20, 30, 50 and 100 cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (Ta), solar radiation (RS), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted TS values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that Ta, RH, RS and P were reasonably correlated with TS at various depths, but the most effective parameters influencing TS at different depths were Ta and RH.

AMSR-E data inversion for soil temperature estimation under snow cover

Climate warming is the focus of several studies where the soil temperature plays an essential role as a state variable for the surface energy balance of the Earth. Many methods have been developed to determine summer surface temperature, but the determination in presence of snow is an ill-conditioned problem for microwave techniques because snow changes the emissivity of the surface. This project aims to improve the estimation of soil temperature, within the top 5 cm of the ground, under the snowpack using passive microwave remote sensing. Results show the potential of the passive microwave brightness temperature inversion at 10 GHz (derived from the Advanced Microwave Scanning Radiometer—Earth Observing System, AMSR-E) for the estimation of soil temperature using a physical multilayer snow–soil model (SNTHERM) coupled with a snow emission model (HUT). The snow model is driven with meteorological measurements from ground-based stations as well as data generated from reanalysis. The proposed iterative retrieval method minimizes the difference between the simulated and measured brightness temperature using the soil temperature as a free parameter given by SNTHERM. Results are validated against ground-based measurements at several sites across Canada through several winter seasons. The overall root mean square error and bias in the retrieved soil temperature is respectively 3.29 K and 0.56 K, lower than the error derived from the snow–soil model without the use of remote sensing. The accuracy in detection of frozen/unfrozen soil under the snowpack is 78%, which is improved up to 81% if the spring melting period is not considered. This original procedure constitutes a very promising tool to characterize the soil (frozen or not) under snow cover, as well as its evolution in northern remote locations where measurements are unavailable.

Comparison of Six Algorithms to Determine the Soil Apparent Thermal Diffusivity at a Site in the Loess Plateau of China

Abstract Soil apparent thermal diffusivity is a crucial physical parameter that affects soil temperature. Six prevalent algorithms to calculate soil apparent thermal diffusivity are intercompared by using soil temperature data collected at the depths of 0.05 m and 0.10 m at a bare site in the China Loess Plateau from day of year 201 through day of year 207 in 2005. Five (i.e., Amplitude, Phase, Arctangent, Logarithm, and Harmonic [HM] algorithms) of the six algorithms are developed from the traditional one-dimensional heat conduction equation. The other algorithm is based on the one-dimensional heat conduction-convection equation that considers the vertical heterogeneity of thermal diffusivity in soil and couples thermal conduction and convection processes (hereinafter referred to as the Conduction-convection algorithm). To assess these six algorithms, we (i) calculate the soil apparent thermal diffusivities by using each of the algorithms, (ii) use the soil apparent thermal diffusivities to predict soil temperature at the 0.10-m depth, and (iii) compare the estimated soil temperature against direct measurements. Results show that (i) HM algorithm gives the most reliable estimates among the traditional five algorithms; and (ii) generally, the Conduction-convection algorithm provides the second-best estimates. Among all of the algorithms, the HM algorithm has the best description of the upper boundary temperature with time, but it only includes conduction heat transfer in the soil. Compared with the HM algorithm, the Conduction-convection algorithm has a less accurate description of the upper boundary temperature, but by accounting for the vertical gradient of soil diffusivity and the water flux density, it includes more physics in the soil heat transfer process. The Conduction-convection algorithm has potential application within land surface models, but future effort should be made to combine the HM and Conduction-convection algorithms to make use of the advantages of each.

COMPARATIVE ANALYSIS OF SPLIT-WINDOW ALGORITHMS FOR ESTIMATING SOIL TEMPERATURE

The ability to estimate soil temperature (Ts) from satellite information is highly useful, since this is one of the main input variables in various models designed for estimating biophysical parameters. A comparative analysis is made of various Split-Window algorithms used to estimate soil temperature from data provided by the Advanced Very High Resolution Radiometer (AVHRR) sensor on board of satellites of the National Oceanic and Atmospheric Administration (NOAA). The algorithms compared are those proposed by: Prata and Platt (1991); Uliveri et al. (1992); Sobrino et al. (1993); Caselles et al. (1997); Sobrino and Raissouni (2000). The temperature estimates were validated with data of the soil temperature in situ recorded in a data logger installed in a meteorological station belonging to La Araucania Region, Chile. The results showed that the algorithm proposed by Sobrino and Raissouni (2000) come the closest to the in situ data. However, there are no statistically significant differences between the different algorithms evaluated.

Introduction to the Special Issue on Airborne Field Campaigns for Soil Moisture

The 52 papers in this special issue make use of airborne and/or ground data to deal with questions such as: 1) the estimation of effective soil temperature and vegetation water content from remote sensing data; 2) the impact of physical parameters such as soil texture, topography, vegetation type. and surface roughness on surface soil moisture retrieval; 3) the transferability of current retrieval equations across scales ranging from tens of kilometers; and 4) issues related to downscaling of low-resolution passive-microwave observations of surface soil moisture.

Haar wavelet in estimating depth profile of soil temperature

A Haar wavelet based method for the estimation of soil temperature at different depths is described in this paper. Diurnal variation in the hourly soil temperature is estimated at different depths varying from 0 to 45 cm. This estimation is compared with observed data available for Trombay site at the depths 5, 10, and 20 cm. This Haar technique can be interpreted from incremental and multi-resolution viewpoint. The estimated values show and excellent agreement with the observed values. The Haar based estimated values are found to be more accurate than the values obtained by FDM approach. More accurate solutions can be obtained by changing the time scale in Haar wavelet; at the same time main features of the solution are preserved. Sensitivity analyses indicate that soil temperature was no so sensitive to changes of soil thermal parameters. Moreover the use of Haar wavelets is found to be accurate, simple, fast, flexible, convenient, small computation costs and computationally attractive.

Evidence for Higher Soil Temperature and Potassium Promoting Invasion of the Common Dandelion, <em>Taraxacum officinale</em>, in Denali National Park and Preserve, Alaska

Common Dandelion, Taraxacum officinale ssp. officinale (dandelion) is expanding its range in Alaska and is of particular concern in National Park Service units. This study investigated the influence of estimated soil temperature, available potassium (K), available phosphorous (P), and total nitrogen (N) on dandelion cover and density on a site near the elevational limit of dandelion. The study site in Denali National Park had been disturbed by construction and was revegetated with native plants 12 years before the study. Seed input to the study site was abundant. In a multiple regression analysis, higher levels of estimated soil temperature and available K accounted for 79% and 73% of the variation in dandelion cover and density, respectively. Practical control methods include not fertilizing disturbed areas with K, and countering continued expansion of dandelion by monitoring human use areas and undisturbed habitats where soil temperatures are likely to be relatively warm.

Satellite Microwave Remote Sensing of Boreal and Arctic Soil Temperatures From AMSR-E

Methods are developed and evaluated to retrieve surface soil temperature information for the advanced microwave scanning radiometer on earth observing system for seven boreal forest and Arctic tundra biophysical monitoring sites across Alaska and Northern Canada. A multiple-band iterative radiative transfer process-based method producing dynamic vegetation and snow cover correction quantities and an empirical multiple regression method using several frequencies are employed. The seasonal pattern of microwave emission and relative accuracy of the soil temperature retrievals are influenced strongly by landscape properties, including the presence of open water, vegetation type and seasonal phenology, snow cover, and freeze-thaw transitions. The retrieval of soil temperature is similar for the two methods with an overall root-mean-square error of 3.1-3.9 K during summer thawed conditions, with a larger error occurring in winter during periods of dynamic snow cover and freeze-thaw state. These results indicate that at high latitudes, the influence of the atmosphere may be less important than that of surface conditions in determining the relative accuracy of the estimated soil temperature. Impacts of surface conditions on surface emissivity, observed brightness temperature, and estimated soil temperature are discussed.

Determination of soil temperature in an arid region

The soil temperature is very important in desert surface energy balance study of aid environment. Previous studies, however, reveal that it is difficult to realistically estimate soil temperature in both amplitude and phase. In this paper, a recent approach is tested focusing on a high temporal resolution soil temperature data read from literature. The resulting soil temperature is found to be in satisfactory agreement with direct measurements. Meanwhile, using this approach, we determine the soil thermal diffusivity and the water flux density for four soil layer depths. Results show that both of them change with increasing depth, which means that the soil thermal properties are heterogeneous in vertical column at this site. The results suggest that this approach could be used as a reference soil temperature estimate.

Assessing the Use of Poplar Tree Systems as a Landfill Evapotranspiration Barrier with the SHAW Model

The use of poplar tree systems (PTS) as evapotranspiration barriers on decommissioned landfills is gaining attention as an option for leachate management. This study involved field-testing the Simultaneous Heat and Water (SHAW) model for its ability to reliably estimate poplar transpiration, volumetric soil water content, and soil temperature at a landfill located in southern Ontario, Canada. The model was then used to estimate deep drainage and to ascertain the influence of a young PTS on the soil water balance of the landfill cover. The SHAW model tended to underestimate poplar transpiration [mean difference (MD) ranged from 0.33 to 3.55 mm on a daily total basis] and overestimate volumetric soil water content by up to 0.10 m3 m(-3). The model estimated soil temperature very well, particularly in the upper 1 m of the landfill cover (MD ranged from -0.1 to 1.6 x degrees C in this layer). The SHAW model simulations showed that deep drainage decreased appreciably with the presence of a young PTS largely through increased interception of rainfall, and that PTS have a good potential to act as effective evapotranspiration barriers in northern temperate climate zones.