scholarly journals Flow Prediction Using Remotely Sensed Soil Moisture in Irish Catchments

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2202
Author(s):  
Chanyu Yang ◽  
Fiachra E. O’Loughlin
Keyword(s):  
Soil Moisture ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Coefficient Of Determination ◽  
Energy Budgets ◽  
Care Needs ◽  
Soil Moisture Accounting ◽  
Model Soil ◽  
Ungauged Basin ◽  
Behavioural Parameter

Owing to a scarcity of in situ streamflow data in ungauged or poorly gauged basins, remote sensing data is an ideal alternative. It offers a valuable perspective into the dynamic patterns that can be difficult to examine in detail with point measurements. For hydrology, soil moisture is one of the pivotal variables which dominates the partitioning of the water and energy budgets. In this study, nine Irish catchments were used to demonstrate the feasibility of using remotely sensed soil moisture for discharge prediction in ungagged basins. Using the conceptual hydrological model “Soil Moisture Accounting and Routing for Transport” (SMART), behavioural parameter sets (BPS) were selected using two different objective functions: the Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) for the calibration period. Good NSE scores were obtained from hydrographs produced using the satellite soil moisture BPS. While the mean performance shows the feasibility of using remotely sensed soil moisture, some outliers result in negative NSE scores. This highlights that care needs to be taken with parameterization of hydrological models using remotely sensed soil moisture for ungauged basin.

scholarly journals Kalibrasi Model Soil Moisture Accounting dengan Software HEC-HMS di Stasiun Debit Pasir Pangaraian

2021 ◽  
Vol 1 (1) ◽  
pp. 13-21
Author(s):  
Yohanna Lilis Handayani ◽  
Gopal Adya Ariska ◽  
David Imannuel Ketaren
Keyword(s):  
Soil Moisture ◽  
Model Calibration ◽  
Daily Rainfall ◽  
Automatic Calibration ◽  
Calibration Data ◽  
Discharge Data ◽  
Climatological Data ◽  
Soil Moisture Accounting ◽  
Model Soil ◽  
Daily Discharge

This research aims to compare the results of the calibration of the Soil Moisture Accounting (SMA) model using Percent Error in Volume (PEV) and Peak Weighted Root Mean Square Error (RMSE). The SMA model calibration uses the HEC-HMS (Hydrologic Engineering Center – Hydrologic Modeling System). There are 12 calibrated parameters by automatic calibration. The input data are the area of ​​the watershed, daily rainfall, daily discharge data and climatological data. The data used is data from 2008 to 2017. The results show that PEV performance shows good results. While the RMSE showed poor results. PEV results are best at 7 years of calibration and 3 years of verification. The length of the calibration data has not affected the verification results.

scholarly journals Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

2021 ◽  
Vol 25 (4) ◽  
pp. 1827-1847
Author(s):  
Noemi Vergopolan ◽  
Sitian Xiong ◽  
Lyndon Estes ◽  
Niko Wanders ◽  
Nathaniel W. Chaney ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Scale ◽  
Crop Yields ◽  
Remotely Sensed ◽  
Drought Monitoring ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Field Scale ◽  
Smallholder Farm

Abstract. Soil moisture is highly variable in space and time, and deficits (i.e., droughts) play an important role in modulating crop yields. Limited hydroclimate and yield data, however, hamper drought impact monitoring and assessment at the farm field scale. This study demonstrates the potential of using field-scale soil moisture simulations to support high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field scale. We present a multiscale modeling approach that combines HydroBlocks – a physically based hyper-resolution land surface model (LSM) – with machine learning. We used HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3 h 30 m resolution. These simulations, along with remotely sensed vegetation indices, meteorological data, and descriptors of the physical landscape (related to topography, land cover, and soils) were combined with district-level maize data to train a random forest (RF) model to predict maize yields at district and field scales (250 m). Our model predicted yields with an average testing coefficient of determination (R2) of 0.57 and mean absolute error (MAE) of 310 kg ha−1 using year-based cross-validation. Our predicted maize losses due to the 2015–2016 El Niño drought agreed well with losses reported by the Food and Agriculture Organization (FAO). Our results reveal that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Soil moisture was also a more effective indicator of drought impacts on crops than precipitation, soil and air temperatures, and remotely sensed normalized difference vegetation index (NDVI)-based drought indices. This study demonstrates how field-scale modeling can help bridge the spatial-scale gap between drought monitoring and agricultural impacts.

THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

2017 ◽  
Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Environmental Changes ◽  
Rapid Development ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Gulf Of Finland ◽  
Coastal Protection ◽  
Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

scholarly journals Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

2015 ◽  
Vol 19 (9) ◽  
pp. 3845-3856 ◽  
Author(s):  
F. Todisco ◽  
L. Brocca ◽  
L. F. Termite ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Soil Loss ◽  
Large Scale ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Loss Estimation ◽  
Balance Model ◽  
Estimation Accuracy ◽  
Soil Losses

Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008–2013. The results showed that including soil moisture observations in the event rainfall–runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha−1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

scholarly journals A soil moisture accounting-procedure with a Richards' equation-based soil texture-dependent parameterization

2015 ◽  
Vol 51 (1) ◽  
pp. 506-523 ◽  
Author(s):  
Simon A. Mathias ◽  
Todd H. Skaggs ◽  
Simon A. Quinn ◽  
Sorcha N. C. Egan ◽  
Lucy E. Finch ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Texture ◽  
Richards Equation ◽  
Soil Moisture Accounting ◽  
Accounting Procedure

Disaggrregation of remotely sensed soil moisture using neural networks

2003 ◽  
Author(s):  
M.P. Schamschula ◽  
W.L. Crosson ◽  
C. Laymon ◽  
R. Inguva ◽  
A. Steward
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Remotely Sensed

An advanced soil moisture accounting procedure for SCS curve number method

2007 ◽  
Vol 21 (21) ◽  
pp. 2872-2881 ◽  
Author(s):  
R. K. Sahu ◽  
S. K. Mishra ◽  
T. I. Eldho ◽  
M. K. Jain
Keyword(s):  
Soil Moisture ◽  
Curve Number ◽  
Soil Moisture Accounting ◽  
Scs Curve Number ◽  
Curve Number Method ◽  
Accounting Procedure
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