Sensitivity Analysis of Navigation with Indian Constellation (NavIC) Derived Multipath Phase Towards Surface Soil Moisture Over Agricultural Land

2020 ◽  
Author(s):  
Sushant Shekhar ◽  
Rishi Prakash ◽  
Anurag Vidyarthi ◽  
Dharmendra Kumar Pandey
Keyword(s):  
Sensitivity Analysis ◽  
Soil Moisture ◽  
Agricultural Land ◽  
Surface Soil ◽  
Surface Soil Moisture

scholarly journals Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations

2021 ◽  
Author(s):  
Shannon de Roos ◽  
Gabriëlle J. M. De Lannoy ◽  
Dirk Raes
Keyword(s):  
Soil Moisture ◽  
Biomass Production ◽  
Crop Production ◽  
Agricultural Land ◽  
Surface Soil ◽  
Processing System ◽  
Surface Soil Moisture ◽  
Aquacrop Model ◽  
Global Land

Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world’s growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires good insight in land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1, to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30 arcseconds (~1 km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database, version 1.2 (HWSDv1.2), and generic crop information. Daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM) and 0.52 (CGLS-SSM), respectively. The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the interannual variability, with a mean anomaly correlation of 0.46 for daily biomass, and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model proves to be useful in assessing crop production and soil moisture at various scales and could serve as a bridge between point-based and global models.

scholarly journals Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations

2021 ◽  
Vol 14 (12) ◽  
pp. 7309-7328
Author(s):  
Shannon de Roos ◽  
Gabriëlle J. M. De Lannoy ◽  
Dirk Raes
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Biomass Production ◽  
Temporal Variability ◽  
Agricultural Land ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Aquacrop Model ◽  
Global Land

Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30 arcsec (∼1 km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active–Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.

Emulation of Community Land Model Version 5 (CLM5) to Quantify Sensitivity of Soil Moisture to Uncertain Parameters

2020 ◽  
Author(s):  
Xiang Gao ◽  
Alexander Avramov ◽  
Eri Saikawa ◽  
C. Adam Schlosser
Keyword(s):  
Sensitivity Analysis ◽  
Soil Moisture ◽  
Parameter Space ◽  
Shape Parameter ◽  
Surface Soil ◽  
Root Zone ◽  
Model Parameters ◽  
Surface Soil Moisture ◽  
Individual Parameter ◽  
Community Land Model

AbstractLand surface models (LSMs) are limited in their ability to reproduce observed soil moisture partially due to uncertainties in model parameters. Here we conduct a variance-based sensitivity analysis to quantify the relative contribution of different model parameters and their interactions to the uncertainty in the surface and root-zone soil moisture in the Community Land Model 5.0 (CLM5). We focus on soil texture-related parameters (porosity, saturated matric potential, saturated hydraulic conductivity, shape parameter of soil-water retention model) and organic matter fraction. A Gaussian process emulator is constructed based on CLM5 simulations and used to estimate soil moisture across the five-dimensional parameter space for sensitivity analysis. The procedure is demonstrated for four seasons across various U.S. sites of distinct soil and vegetation types. We find that the emulator captures well the CLM5 behavior across the parameter space for different soil textures and seasons. The uncertainties of surface and root-zone soil moisture are dominated by the uncertainties in porosity and shape parameter with negligible parametric interactions. However, relative importance of porosity versus shape parameter varies with soil textures (sites), depths (surface versus root-zone), and seasons. At most of the sites, surface soil moisture uncertainty is attributed largely to shape parameter uncertainty, while porosity uncertainty is more important for the root-zone soil moisture uncertainty. All individual parameter and interaction effects demonstrate less variability across different soil textures and seasons for root-zone than for surface soil moisture. These results provide scientific guidance to prioritize reducing the uncertainty of sensitive parameters for improving soil moisture modeling with CLM.

ALOS-2 and Sentinel-1 SAR data sensitivity analysis to surface soil moisture over bare and vegetated agricultural fields

2020 ◽  
Vol 171 ◽  
pp. 105303 ◽  
Author(s):  
Aliihsan Sekertekin ◽  
Aycan Murat Marangoz ◽  
Saygin Abdikan
Keyword(s):  
Sensitivity Analysis ◽  
Soil Moisture ◽  
Surface Soil ◽  
Agricultural Fields ◽  
Surface Soil Moisture ◽  
Sar Data ◽  
Data Sensitivity

Evaluation of Deterministic Models for Near Surface Soil Moisture Prediction

1987 ◽  
Author(s):  
M. G. Anderson ◽  
J. E. Cochrane
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Deterministic Models ◽  
Near Surface

scholarly journals Simultaneously estimating surface soil moisture and roughness of bare soils by combining optical and radar data

2021 ◽  
Vol 100 ◽  
pp. 102345
Author(s):  
Xingming Zheng ◽  
Zhuangzhuang Feng ◽  
Lei Li ◽  
Bingzhe Li ◽  
Tao Jiang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Radar Data ◽  
Surface Soil Moisture ◽  
Bare Soils

Controls of surface soil moisture spatial patterns and their temporal stability in a semi-arid steppe

2010 ◽  
Vol 24 (18) ◽  
pp. 2507-2519 ◽  
Author(s):  
Y. Zhao ◽  
S. Peth ◽  
X. Y. Wang ◽  
H. Lin ◽  
R. Horn
Keyword(s):  
Soil Moisture ◽  
Spatial Patterns ◽  
Surface Soil ◽  
Temporal Stability ◽  
Surface Soil Moisture ◽  
Arid Steppe ◽  
Semi Arid
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