Assessment of Four Model-Based Surface Soil Temperature Products Unsing Global Dense in Situ Observations

2021 ◽  
Author(s):  
Hongliang Ma ◽  
Jiangyuan Zeng ◽  
Jean-Pierre Wigneron ◽  
Xiang Zhang ◽  
Nengcheng Chen ◽  
...  
Keyword(s):  
Soil Temperature ◽  
Surface Soil ◽  
Model Based ◽  
Surface Soil Temperature ◽  
In Situ Observations

scholarly journals Evaluation of six satellite- and model-based surface soil temperature datasets using global ground-based observations

2021 ◽  
Vol 264 ◽  
pp. 112605
Author(s):  
Hongliang Ma ◽  
Jiangyuan Zeng ◽  
Xiang Zhang ◽  
Peng Fu ◽  
Donghai Zheng ◽  
...  
Keyword(s):  
Soil Temperature ◽  
Surface Soil ◽  
Model Based ◽  
Surface Soil Temperature

Relation between soil temperature and biophysical parameters in Indian mustard seeds

2013 ◽  
Vol 27 (4) ◽  
pp. 359-367 ◽  
Author(s):  
T. Adak ◽  
N.V.K. Chakravarty
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Soil Temperature ◽  
Surface Soil ◽  
Linear Regression Analysis ◽  
Indian Mustard ◽  
Biophysical Parameters ◽  
Oil Accumulation ◽  
Area Index ◽  
Surface Soil Temperature

Abstract Temporal changes in surface soil temperature were studied in winter crop. Significant changes in bare and cropped soil temperature were revealed. Air temperature showed a statistically positive and strong relationship (R2 = 0.79** to 0.92**) with the soil temperature both at morning and afternoon hours. Linear regression analysis indicated that each unit increase in ambient temperature would lead to increase in minimum and maximum soil temperatures by 1.04 and 1.02 degree, respectively. Statistically positive correlation was revealed among biophysical variables with the cumulative surface soil temperature. Linear and non-linear regression analysis indicated 62-69, 72-86 and 72-80% variation in Leaf area index, dry matter production and heat use efficiency in Indian mustard crop as a function of soil degree days. Below 60% variation in yield in Indian mustard was revealed as a function of soil temperature. In contrast, non-significant relationship between oil content and soil temperature was found, which suggests that oil accumulation in oilseed crops was not affected significantly by the soil temperature as an independent variable.

Interval of days required for determining efficient relations between climatic factors and cotton flower and boll production

2002 ◽  
Vol 82 (3) ◽  
pp. 499-506 ◽  
Author(s):  
Zakaria M Sawan ◽  
Louis I Hanna ◽  
Willis L McCuistion
Keyword(s):  
Soil Temperature ◽  
Air Temperature ◽  
Surface Soil ◽  
Cultural Practices ◽  
Agricultural Research ◽  
Climatic Factors ◽  
Sunshine Duration ◽  
Field Trials ◽  
Cotton Production ◽  
Surface Soil Temperature

The cotton plant (Gossypium spp.) is sensitive to numerous environmental factors. This study was aimed at predicting effects of climatic factors grouped into convenient intervals (in days) on cotton flower and boll production compared with daily observations. Two uniformity field trials using the cotton (G. barbadense L.) cv. Giza 75 were conducted in 1992 and 1993 at the Agricultural Research Center, Giza, Egypt. Randomly chosen plants were used to record daily numbers of flowers and bolls during the reproductive stage (60 days). During this period, daily air temperature, temperature magnitude, evaporation, surface soil temperature, sunshine duration, humidity, and wind speed were recorded. Data, grouped into intervals of 2, 3, 4, 5, 6, and 10 d, were correlated with cotton production variables using regression analysis. Evaporation was found to be the most important climatic variable affecting flower and boll production, followed by humidity and sunshine duration. The least important variables were surface soil temperature at 0600 and minimum air temperature. The 5-d interval was found to provide the best correlation with yield parameters. Applying appropriate cultural practices that minimize the deleterious effects of evaporation and humidity could lead to an important improvement in cotton yield in Egypt. Key words: Cotton, flower production, boll production, boll retention

Cover crop effects on soil temperature in a clay loam soil in southwestern Ontario

2021 ◽  
pp. 1-10
Author(s):  
X.M. Yang ◽  
W.D. Reynolds ◽  
C.F. Drury ◽  
M.D. Reeb
Keyword(s):  
Soil Temperature ◽  
Cover Crops ◽  
Environmental Performance ◽  
Cover Crop ◽  
Crop Production ◽  
Surface Soil ◽  
Clay Loam ◽  
Near Surface ◽  
Soil Temperatures ◽  
Surface Soil Temperature

Although it is well established that soil temperature has substantial effects on the agri-environmental performance of crop production, little is known of soil temperatures under living cover crops. Consequently, soil temperatures under a crimson clover and white clover mix, hairy vetch, and red clover were measured for a cool, humid Brookston clay loam under a corn–soybean–winter wheat/cover crop rotation. Measurements were collected from August (after cover crop seeding) to the following May (before cover crop termination) at 15, 30, 45, and 60 cm depths during 2018–2019 and 2019–2020. Average soil temperatures (August–May) were not affected by cover crop species at any depth, or by air temperature at 60 cm depth. During winter, soil temperatures at 15, 30, and 45 cm depths were greater under cover crops than under a no cover crop control (CK), with maximum increase occurring at 15 cm on 31 January 2019 (2.5–5.7 °C) and on 23 January 2020 (0.8–1.9 °C). In spring, soil temperatures under standing cover crops were cooler than the CK by 0.1–3.0 °C at 15 cm depth, by 0–2.4 °C at the 30 and 45 cm depths, and by 0–1.8 °C at 60 cm depth. In addition, springtime soil temperature at 15 cm depth decreased by about 0.24 °C for every 1 Mg·ha−1 increase in live cover crop biomass. Relative to bare soil, cover crops increased near-surface soil temperature during winter but decreased near-surface soil temperature during spring. These temperature changes may have both positive and negative effects on the agri-environmental performance of crop production.

scholarly journals From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

2008 ◽  
Vol 12 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
T. Pellarin ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Synthetic Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Water Index ◽  
In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

2010 ◽  
Vol 2 (2) ◽  
Author(s):  
Diandong Ren
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Initial Guess ◽  
Surface Model ◽  
Soil Moisture Condition ◽  
Wide Range ◽  
Surface Soil Temperature

AbstractBased on a 2-layer land surface model, a rather general variational data assimilation framework for estimating model state variables is developed. The method minimizes the error of surface soil temperature predictions subject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables are performed and the results verified against model simulated data as well as real observations for the Oklahoma Atmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect to a wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (within the range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce the initial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initial guess error is usually reduced by over four orders of magnitude.Using synthetic data, the robustness of the retrieval scheme as related to information content of the data and the physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Through sensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether or not the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes. This reconciles two recent studies.With the real data experiments, it is shown that observations during the daytime period are the most effective for the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining the importance of information quantity, especially for schemes assimilating noisy observations.

scholarly journals An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US

2019 ◽  
Vol 11 (5) ◽  
pp. 478 ◽  
Author(s):  
Jostein Blyverket ◽  
Paul Hamer ◽  
Laurent Bertino ◽  
Clément Albergel ◽  
David Fairbairn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Open Loop ◽  
Surface Zone ◽  
Surface Soil Moisture ◽  
Atmospheric Forcing ◽  
Significance Level ◽  
Soil Moisture Data ◽  
In Situ Observations

A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite soil moisture data from the Soil Moisture Active Passive (SMAP) mission, we compare the EnKF with the computationally cheaper ensemble Optimal Interpolation (EnOI) method over the contiguous United States (CONUS). The background error–covariance in the EnOI is sampled in two ways: (i) by using the stochastic spread from an ensemble open-loop run, and (ii) sampling from the model spinup climatology. Our results indicate that the EnKF is only marginally superior to one version of the EnOI. Furthermore, the assimilation of SMAP data using the EnKF and EnOI is found to improve the surface zone correlation with in situ observations at a 95 % significance level. The EnKF assimilation of SMAP data is also found to improve root-zone correlation with independent in situ data at the same significance level; however this improvement is dependent on which in situ network we are validating against. We evaluate how the quality of the atmospheric forcing affects the analysis results by prescribing the land surface data assimilation system with either observation corrected or model derived precipitation. Surface zone correlation skill increases for the analysis using both the corrected and model derived precipitation, but only the latter shows an improvement at the 95 % significance level. The study also suggests that assimilation of satellite derived surface soil moisture using the EnOI can correct random errors in the atmospheric forcing and give an analysed surface soil moisture close to that of an open-loop run using observation derived precipitation. Importantly, this shows that estimates of soil moisture could be improved using a combination of assimilating SMAP using the computationally cheap EnOI while using model derived precipitation as forcing. Finally, we assimilate three different Level-2 satellite derived soil moisture products from the European Space Agency Climate Change Initiative (ESA CCI), SMAP and SMOS (Soil Moisture and Ocean Salinity) using the EnOI, and then compare the relative performance of the three resulting analyses against in situ soil moisture observations. In this comparison, we find that all three analyses offer improvements over an open-loop run when comparing to in situ observations. The assimilation of SMAP data is found to perform marginally better than the assimilation of SMOS data, while assimilation of the ESA CCI data shows the smallest improvement of the three analysis products.

scholarly journals The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

2015 ◽  
Vol 19 (12) ◽  
pp. 4831-4844 ◽  
Author(s):  
C. Draper ◽  
R. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
In Situ Observations ◽  
The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

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