scholarly journals Comparison of Contemporary In Situ, Model, and Satellite Remote Sensing Soil Moisture With a Focus on Drought Monitoring

2019 ◽  
Vol 55 (2) ◽  
pp. 1565-1582 ◽  
Author(s):  
Trent W. Ford ◽  
Steven M. Quiring
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Satellite Remote Sensing ◽  
Drought Monitoring

scholarly journals Investigating temporal field sampling strategies for site-specific calibration of three soil moisture–neutron intensity parameterisation methods

2015 ◽  
Vol 19 (7) ◽  
pp. 3203-3216 ◽  
Author(s):  
J. Iwema ◽  
R. Rosolem ◽  
R. Baatz ◽  
T. Wagener ◽  
H. R. Bogena
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Satellite Remote Sensing ◽  
Cosmic Ray ◽  
Sampling Strategies ◽  
Site Specific ◽  
Neutron Intensity ◽  
Soil Wetness ◽  
Semi Arid

Abstract. The Cosmic-Ray Neutron Sensor (CRNS) can provide soil moisture information at scales relevant to hydrometeorological modelling applications. Site-specific calibration is needed to translate CRNS neutron intensities into sensor footprint average soil moisture contents. We investigated temporal sampling strategies for calibration of three CRNS parameterisations (modified N0, HMF, and COSMIC) by assessing the effects of the number of sampling days and soil wetness conditions on the performance of the calibration results while investigating actual neutron intensity measurements, for three sites with distinct climate and land use: a semi-arid site, a temperate grassland, and a temperate forest. When calibrated with 1 year of data, both COSMIC and the modified N0 method performed better than HMF. The performance of COSMIC was remarkably good at the semi-arid site in the USA, while the N0mod performed best at the two temperate sites in Germany. The successful performance of COSMIC at all three sites can be attributed to the benefits of explicitly resolving individual soil layers (which is not accounted for in the other two parameterisations). To better calibrate these parameterisations, we recommend in situ soil sampled to be collected on more than a single day. However, little improvement is observed for sampling on more than 6 days. At the semi-arid site, the N0mod method was calibrated better under site-specific average wetness conditions, whereas HMF and COSMIC were calibrated better under drier conditions. Average soil wetness condition gave better calibration results at the two humid sites. The calibration results for the HMF method were better when calibrated with combinations of days with similar soil wetness conditions, opposed to N0mod and COSMIC, which profited from using days with distinct wetness conditions. Errors in actual neutron intensities were translated to average errors specifically to each site. At the semi-arid site, these errors were below the typical measurement uncertainties from in situ point-scale sensors and satellite remote sensing products. Nevertheless, at the two humid sites, reduction in uncertainty with increasing sampling days only reached typical errors associated with satellite remote sensing products. The outcomes of this study can be used by researchers as a CRNS calibration strategy guideline.

scholarly journals Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries

2021 ◽  
Vol 13 (16) ◽  
pp. 3294
Author(s):  
Muhammad Shahzaman ◽  
Weijun Zhu ◽  
Irfan Ullah ◽  
Farhan Mustafa ◽  
Muhammad Bilal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
South Asia ◽  
Satellite Remote Sensing ◽  
Crop Production ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Vegetation Condition ◽  
Vegetation Condition Index

The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring.

Passive Microwave Remote Sensing of Soil Moisture and Regional Drought Monitoring

2005 ◽  
Author(s):  
Thomas J. Jackson
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Drought Monitoring ◽  
Passive Microwave Remote Sensing ◽  
The Past ◽  
Physically Based ◽  
Effects Of Drought

Mitigating the effects of drought can be improved through better information on the current status, the prediction of occurrence, and the extent of drought. Soil moisture can now be measured using a new generation of microwave remote sensing satellites. These measurements can be used to monitor drought conditions on a daily basis over the entire earth. The quality of these products will continue to improve over time as new sensors are launched. These satellite products, combined with existing in situ observations and models, should be exploited in drought monitoring, assessment, and prediction. Measuring soil moisture on a routine basis has the potential to significantly improve our understanding of climatic processes and strengthen our ability to model and forecast these processes. Leese et al. (2001) concluded that the optimal approach to monitoring soil moisture would be a combination of model-derived estimates using in situ and remotely sensed measurements. In this regard, each method produces soil moisture values that are both unique and complementary. This concept is essentially the process of data assimilation described by Houser et al. (1998). In situ measurements of soil moisture have been made in a few countries over the past 70 years (Robock et al., 2000). However, due to cost and sensor limitations, there are few soil moisture sensor systems available today, especially for automated measurements. A lack of routine observations of soil moisture has led to the use of surrogate measurements (i.e., antecedent precipitation index) and modeled estimates, which limits the possibility of physically based model validation and acceptance. Current tools to predict drought, such as drought indices and Global Climate Models (GCMs), do not include any direct observations of the soil condition, which is critical for agriculture. Passive microwave remote sensing instruments respond to the amount of moisture in the soil. Several methods have the potential to provide both soil moisture and drought information. In the past, the options have been limited by the availability of satellite systems. Even with these limitations, investigators have explored the potential of these data in soil moisture studies with some success.

Drought monitoring in North China using HJ-1 satellite remote sensing data

2009 ◽  
Author(s):  
Bingfeng Yang ◽  
Qiao Wang ◽  
Changzuo Wang ◽  
Huawei Wan ◽  
Yipeng Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
North China ◽  
Remote Sensing Data ◽  
Drought Monitoring ◽  
Sensing Data

scholarly journals Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

2020 ◽  
Vol 13 (3) ◽  
pp. 1267-1284 ◽  
Author(s):  
Theo Baracchini ◽  
Philip Y. Chu ◽  
Jonas Šukys ◽  
Gian Lieberherr ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Lake Ecosystem ◽  
Sensing Data ◽  
Localization Scheme ◽  
Spatiotemporal Scales

Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

scholarly journals Extension of the Hapke bidirectional reflectance model to retrieve soil water content

2011 ◽  
Vol 15 (7) ◽  
pp. 2317-2326 ◽  
Author(s):  
G.-J. Yang ◽  
C.-J. Zhao ◽  
W.-J. Huang ◽  
J.-H. Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Soil Water ◽  
Land Surface ◽  
Ant Colony Algorithm ◽  
Heat Fluxes ◽  
Spatial And Temporal Variation ◽  
Bidirectional Reflectance ◽  
Equivalent Water Thickness

Abstract. Soil moisture links the hydrologic cycle and the energy budget of land surfaces by regulating latent heat fluxes. An accurate assessment of the spatial and temporal variation of soil moisture is important to the study of surface biogeophysical processes. Although remote sensing has proven to be one of the most powerful tools for obtaining land surface parameters, no effective methodology yet exists for in situ soil moisture measurement based on a Bidirectional Reflectance Distribution Function (BRDF) model, such as the Hapke model. To retrieve and analyze soil moisture, this study applied the soil water parametric (SWAP)-Hapke model, which introduced the equivalent water thickness of soil, to ground multi-angular and hyperspectral observations coupled with, Powell-Ant Colony Algorithm methods. The inverted soil moisture data resulting from our method coincided with in situ measurements (R2 = 0.867, RMSE = 0.813) based on three selected bands (672 nm, 866 nm, 2209 nm). It proved that the extended Hapke model can be used to estimate soil moisture with high accuracy based on the field multi-angle and multispectral remote sensing data.

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