Investigating the Effects of Meteorological Data Rainfall and Temperature on GNSS-R Soil Moisture Inversion

2021 ◽  
Author(s):  
Yajie Shi ◽  
Yueji Liang ◽  
Chao Ren ◽  
Jianmin Lai ◽  
Qin Ding ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Meteorological Data ◽  
Soil Moisture Inversion

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

scholarly journals Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

scholarly journals A comparison of ASCAT and modelled soil moisture over South Africa, using TOPKAPI in land surface mode

2010 ◽  
Vol 14 (4) ◽  
pp. 613-626 ◽  
Author(s):  
S. Sinclair ◽  
G. G. S. Pegram
Keyword(s):  
South Africa ◽  
Soil Moisture ◽  
Land Surface ◽  
Meteorological Data ◽  
Saturation Index ◽  
Significant Proportion ◽  
Surface Mode ◽  
Good Correspondence ◽  
Percentage Saturation ◽  
Soil Saturation

Abstract. In this paper we compare two independent soil moisture estimates over South Africa. The first estimate is a Soil Saturation Index (SSI) provided by automated real-time computations of the TOPKAPI hydrological model, adapted to run as a collection of independent 1 km cells with centres on a grid with a spatial resolution of 0.125°, at 3 h intervals. The second set of estimates is the remotely sensed ASCAT Surface Soil Moisture product, temporally filtered to yield a Soil Wetness Index (SWI). For the TOPKAPI cells, the rainfall forcing used is the TRMM 3B42RT product, while the evapotranspiration forcing is based on a modification of the FAO56 reference crop evapotranspiration (ET0). ET0 is computed using forecast fields of meteorological variables from the Unified Model (UM) runs done by the South African Weather Service (SAWS); the UM forecast fields were used, because reanalysis is not done by SAWS. To validate these ET0 estimates we compare them with those computed using observed meteorological data at a network of weather stations; they were found to be unbiased with acceptable scatter. Using the rainfall and evapotranspiration forcing data, the percentage saturation of the TOPKAPI soil store is computed as a Soil Saturation Index (SSI), for each of 6984 unconnected uncalibrated TOPKAPI cells at 3 h time-steps. These SSI estimates are then compared with the SWI estimates obtained from ASCAT. The comparisons indicate a good correspondence in the dynamic behaviour of SWI and SSI for a significant proportion of South Africa.

GNSS-IR Soil Moisture Inversion Method Based on Random Forest

2021 ◽  
pp. 133-144
Author(s):  
Yuhua Zhang ◽  
Lili Jing ◽  
Yanmin Zhao ◽  
Hongliang Ruan ◽  
Lei Yang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Inversion Method ◽  
Soil Moisture Inversion

scholarly journals Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 705 ◽  
Author(s):  
Haekyung Park ◽  
Kyungmin Kim ◽  
Dong kun Lee
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Meteorological Data ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Absolute Error ◽  
Severe Drought ◽  
Drought Forecasting ◽  
Moisture Index ◽  
Training Area

The uncertainty of drought forecasting based on past meteorological data is increasing because of climate change. However, agricultural droughts, associated with food resources and determined by soil moisture, must be predicted several months ahead for timely resource allocation. Accordingly, we designed a severe drought area prediction (SDAP) model for short-term drought without meteorological data. The predictions of our proposed SDAP model indicate a forecast of serious drought areas assuming non-rainfall, not a probability prediction of drought occurrence. Furthermore, this prediction provides more practical information to help with rapid water allocation during a real drought. The model structure using remote sensing data consists of two parts. First, the drought function f(x) from the training area by random forest (RF) learned the changes in the pattern of soil moisture index (SMI) from the past drought and the training performance was found to be root mean square error (RMSE) = 0.052, mean absolute error (MAE) = 0.039, R2 = 0.91. Second, derived f(x) predicted the SMI of the study area, which is 20 times larger than the training area, of the same season of another year as RMSE = 0.382, MAE = 0.375, R2 = 0.58. We also obtained the variable importance stemming from RF and discussed its meaning along with the advantages and limitations of the model, training areas selection, and prediction coverage.

scholarly journals Soil, snow, weather, and sub-surface storage data from a mountain catchment in the rain–snow transition zone

2013 ◽  
Vol 6 (2) ◽  
pp. 811-835 ◽  
Author(s):  
P. R. Kormos ◽  
D. Marks ◽  
C. J. Williams ◽  
H. P. Marshall ◽  
P. Aishlin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Transition Zone ◽  
Soil Texture ◽  
Soil Depth ◽  
Meteorological Data ◽  
Dew Point ◽  
Soil Profiles ◽  
Data Set ◽  
Hydrologic Condition ◽  
Wide Range

Abstract. A comprehensive hydroclimatic data set is presented for the 2011 water year to improve understanding of hydrologic processes in the rain-snow transition zone. This type of dataset is extremely rare in scientific literature because of the quality and quantity of soil depth, soil texture, soil moisture, and soil temperature data. Standard meteorological and snow cover data for the entire 2011 water year are included, which include several rain-on-snow events. Surface soil textures and soil depths from 57 points are presented as well as soil texture profiles from 14 points. Meteorological data include continuous hourly shielded, unshielded, and wind corrected precipitation, wind speed, air temperature, relative humidity, dew point temperature, and incoming solar and thermal radiation data. Sub-surface data included are hourly soil moisture data from multiple depths from 7 soil profiles within the catchment, and soil temperatures from multiple depths from 2 soil profiles. Hydrologic response data include hourly stream discharge from the catchment outlet weir, continuous snow depths from one location, intermittent snow depths from 5 locations, and snow depth and density data from ten weekly snow surveys. Though it represents only a single water year, the presentation of both above and below ground hydrologic condition makes it one of the most detailed and complete hydro-climatic datasets from the climatically sensitive rain-snow transition zone for a wide range of modeling and descriptive studies. Data are available at doi:10.1594/PANGAEA.819837.

A NEW VERSATILE SOIL MOISTURE BUDGET

1966 ◽  
Vol 46 (3) ◽  
pp. 299-315 ◽  
Author(s):  
W. Baier ◽  
Geo. W. Robertson
Keyword(s):  
Soil Moisture ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Available Soil Moisture ◽  
Different Types ◽  
Demand Rates ◽  
Different Depths ◽  
A New Technique ◽  
Drying Curves ◽  
Pe Ratio

A new technique for the estimation of daily soil moisture on a zone-by-zone basis from standard meteorological data is herewith presented. The method, which is more versatile than existing meteorological budgets and therefore called "versatile budget" (VB), makes use of some basic concepts employed in the modulated budget, such as taking potential evapotranspiration (PE) as a possible maximum of actual evapotranspiration (AE) and subdividing the total available soil moisture into several zones of different capacities. The VB facilitates moisture withdrawal simultaneously from different depths of the soil profile permeated by roots in relation to the rate of PE and the available soil moisture in each zone. Adjustments for runoff, drainage, different types of soil-drying curves and the effect of different atmospheric demand rates on the AE/PE ratio are also incorporated.Comparisons between daily soil moisture readings from Colman blocks with estimates from the modulated budget and from the VB showed the feasibility of estimating daily soil moisture from standard meteorological data. The estimates of the VB were superior to those from the modulated budget on a zone-by-zone basis. The application of soil moisture statistics obtained from meteorological budgets is discussed.

scholarly journals Modelling soil moisture for a grassland and a woodland site in south-east England

2002 ◽  
Vol 6 (1) ◽  
pp. 39-48 ◽  
Author(s):  
E. Blyth
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Meteorological Data ◽  
Field Capacity ◽  
Runoff Generation ◽  
Surface Exchange ◽  
Soil Moisture Model ◽  
Meteorological Observations ◽  
The Uk ◽  
Grassland Site

Abstract. This paper describes a comparison between two soil moisture prediction models. One is MORECS (Met Office Rainfall and Evaporation Calculation Scheme), the Met Office soil moisture model that is used by agriculture, flood modellers and weather forecasters to initialise their models. The other is MOSES (Met Office Surface Exchange Scheme), modified with a runoff generation module. The models are made compatible by increasing the vegetation information available to MOSES. Both models were run with standard parameters and were driven using meteorological observations at Wallingford (1995-1997). Detailed soil moisture measurements were available at a grassland site and a woodland site in this area. The comparison between the models and the observed soil moisture indicated that, for the grassland site, MORECS dried out too quickly in the spring and, for the woodland site, was too wet. Overall, the performance of MOSES was superior. The soil moisture predicted by the new, modified MOSES will be included as a product of Nimrod - the 5 km x 5km gridded network of observed meteorological data across the UK. Keywords: Soil moisture, model, observation, field capacity

scholarly journals Assimilation of Disaggregated Microwave Soil Moisture into a Hydrologic Model Using Coarse-Scale Meteorological Data

2006 ◽  
Vol 7 (6) ◽  
pp. 1308-1322 ◽  
Author(s):  
O. Merlin ◽  
A. Chehbouni ◽  
G. Boulet ◽  
Y. Kerr
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Meteorological Data ◽  
Microwave Radiometer ◽  
Hydrologic Model ◽  
Observation Time ◽  
Coupling Scheme ◽  
Surface Soil Moisture ◽  
Coarse Scale ◽  
Near Surface

Abstract Near-surface soil moisture retrieved from Soil Moisture and Ocean Salinity (SMOS)-type data is downscaled and assimilated into a distributed soil–vegetation–atmosphere transfer (SVAT) model with the ensemble Kalman filter. Because satellite-based meteorological data (notably rainfall) are not currently available at finescale, coarse-scale data are used as forcing in both the disaggregation and the assimilation. Synthetic coarse-scale observations are generated from the Monsoon ‘90 data by aggregating the Push Broom Microwave Radiometer (PBMR) pixels covering the eight meteorological and flux (METFLUX) stations and by averaging the meteorological measurements. The performance of the disaggregation/assimilation coupling scheme is then assessed in terms of surface soil moisture and latent heat flux predictions over the 19-day period of METFLUX measurements. It is found that the disaggregation improves the assimilation results, and vice versa, the assimilation of the disaggregated microwave soil moisture improves the spatial distribution of surface soil moisture at the observation time. These results are obtainable regardless of the spatial scale at which solar radiation, air temperature, wind speed, and air humidity are available within the microwave pixel and for an assimilation frequency varying from 1/1 day to 1/5 days.

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