Space borne Active Microwave Remote Sensing of Soil Moisture: A Review

2017 ◽  
Vol 1 (1) ◽  
pp. 53-86 ◽  
Author(s):  
Vijay Bhagat
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Soil Layer ◽  
Microwave Remote Sensing ◽  
Climate Change Management ◽  
Scientific Challenge ◽  
Active Microwave Remote Sensing ◽  
Interesting Area ◽  
Selection Of

Space-borne active microwave remote sensing is an efficient technique to acquire knowledge of land surface soil moisture (SM). Several studies have reported comparable results of surface SM using space-borne scatterometer responses to backscattering from soil layer. However, detection and measuring of SM using these techniques require an appropriate filtering of data, site-specific calibration of surface roughness parameters, prior knowledge of the study area, specific research purpose, careful selection of model, different suitable datasets with appropriate time series, etc. Reported success studies are very site-, data- and situation-specific and show uncertainty in SM estimations therefore, insufficient to reach global conclusions and applications. Scientific challenge before the community is to develop or modify models and appropriate datasets for SM estimations with simplification and high precision with global applicability for complex bio-physical units. The field is new, active, attractive, challenging and interesting area of research for sustainable land and climate change management.

scholarly journals Towards an integrated soil moisture drought monitor for East Africa

2012 ◽  
Vol 9 (4) ◽  
pp. 4587-4631 ◽  
Author(s):  
W. B. Anderson ◽  
B. F. Zaitchik ◽  
C. R. Hain ◽  
M. C. Anderson ◽  
M. T. Yilmaz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
East Africa ◽  
Remote Monitoring ◽  
Vegetation Cover ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Surface Model ◽  
Monitoring Methods ◽  
Data Scarcity

Abstract. Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010–2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA) to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, and physically-based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the consistency of anomaly estimates across independent methods. This approach holds potential as a remote soil moisture-based drought monitoring system that is robust across the diverse climatic and ecological zones of East Africa.

Characterising recent drought events using current reanalysis and remote-sensing soil moisture products within a common framework

2021 ◽  
Author(s):  
Martin Hirschi ◽  
Bas Crezee ◽  
Sonia I. Seneviratne
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Microwave Remote Sensing ◽  
Agricultural Drought ◽  
Climate Data ◽  
Data Store ◽  
Common Framework ◽  
Long Term Trends

<p>Drought events cause multiple impacts on the environment, the society and the economy. Here, we analyse recent major drought events with different metrics using a common framework. The analysis is based on current reanalysis (ERA5, ERA5-Land, MERRA-2) and merged remote-sensing products (ESA-CCI soil moisture, gridded satellite soil moisture from the Copernicus Climate Data Store), focusing on soil moisture (or agricultural) drought. The events are characterised by their severity, magnitude, duration and spatial extent, which are calculated from standardised daily anomalies of surface and root-zone soil moisture. We investigate the ability of the different products to represent the droughts and set the different events in context to each other. The considered products also offer opportunities for drought monitoring since they are available in near-real time.</p><p>All investigated products are able to represent the investigated drought events. Overall, ERA5 and ERA5-Land often show the strongest, and the remote-sensing products often weaker responses based on surface soil moisture. The weaker severities of the events in the remote-sensing products are both related to shorter event durations as well as less pronounced average negative standardised soil moisture anomalies, while the magnitudes (i.e., the minimum of the standardised anomalies over time) are comparable to the reanalysis products. Differing global distributions of long-term trends may explain some differences in the drought responses of the products. Also, the lower penetration depth of microwave remote sensing compared to the top layer of the involved land surface models could explain the partly weaker negative standardized soil moisture anomalies in the remote-sensing products during the investigated events. In the root zone (based on the reanalysis products), the drought events often show prolonged durations, but weaker magnitudes and smaller spatial extents.</p>

scholarly journals Earth Observations-Based Evapotranspiration in Northeastern Thailand

2019 ◽  
Vol 11 (2) ◽  
pp. 138 ◽  
Author(s):  
Chaolei Zheng ◽  
Li Jia ◽  
Guangcheng Hu ◽  
Jing Lu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Spatiotemporal Pattern ◽  
Monsoon Climate ◽  
Thermal Remote Sensing ◽  
Clear Sky

Thailand is characterized by typical tropical monsoon climate, and is suffering serious water related problems, including seasonal drought and flooding. These issues are highly related to the hydrological processes, e.g., precipitation and evapotranspiration (ET), which are helpful to understand and cope with these problems. It is critical to study the spatiotemporal pattern of ET in Thailand to support the local water resource management. In the current study, daily ET was estimated over Thailand by ETMonitor, a process-based model, with mainly satellite earth observation datasets as input. One major advantage of the ETMonitor algorithm is that it introduces the impact of soil moisture on ET by assimilating the surface soil moisture from microwave remote sensing, and it reduces the dependence on land surface temperature, as the thermal remote sensing is highly sensitive to cloud, which limits the ability to achieve spatial and temporal continuity of daily ET. The ETMonitor algorithm was further improved in current study to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature—vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d−1, which showed better accuracy than the ET product from MODerate resolution Imaging Spectroradiometer (MODIS), especially for the forest sites. Chi and Mun river basins, located in Northeast Thailand, were selected to analyze the spatial pattern of ET. The results indicate that the ET had large fluctuation in seasonal variation, which is predominantly impacted by the monsoon climate.

scholarly journals Advances in Quantitative Earth Remote Sensing: Past, Present and Future

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5399 ◽  
Author(s):  
Ghassem R. Asrar
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Polar Regions ◽  
Earth System ◽  
Earth Remote Sensing ◽  
Earth’S System

A combination of multispectral visible, infra-red and microwave sensors on the constellation of international Earth-observing satellites are providing unprecedented observations for all Earth domains over multiple decades (i.e., atmosphere, land, oceans and polar regions). This Special Issue of Sensors is dedicated to papers that describe such advances in the field of Earth remote sensing and their applications to advance understanding of Earth’s planetary system and applying the resulting knowledge and information to meet the societal needs during recent decades. The papers accepted and published in this issue convey the exciting scientific and technical challenges and opportunities for remote sensing of all domains of Earth system, including terrestrial, aquatic and coastal ecosystems; bathymetry of coasts and islands; oceans and lakes; measurement of soil moisture and land surface temperature that affects both water resources and food production; and advances in use of sun-induced fluorescence (SIF) in measuring and monitoring the contribution of terrestrial vegetation in the cycling of carbon in Earth’s system. Measurements of SIF, for example, has had a profound impact on the field of terrestrial ecosystems research and modelling. The Earth Polychromatic Imaging Camera (EPIC) instrument on the Deep Space Climate Observatory (DSCVR) satellite located at the Sun–Earth Lagrange Point One, about 1.5 million miles away from Earth, is providing unique observations of the Earth’s full sun-lit disk from pole-to-pole and minute-by-minute, which overcomes a major limitation in temporal coverage of Earth by other polar-orbiting Earth-observing satellites. Active and passive microwave remote sensing instruments allow all-weather measurements and monitoring of clouds, weather phenomena, land-surface temperature and soil moisture by overcoming the presence of clouds that affect measurements by visible and infrared sensors. The use of powerful in-space lasers is allowing scientists and engineers to measure and monitor rapidly changing ice sheets in polar regions and mountain glaciers. These sensors and their measurements that are deployed on major space-based observatories and small- and micro-satellites, and the scientific knowledge they provide, are enhancing our understanding of planet Earth and development of Earth system models that are used increasingly to project future conditions due to Earth’s rapidly changing environmental conditions. Such knowledge and information are benefiting people, businesses and governments worldwide.

Evaluation of Assumptions in Soil Moisture Triple Collocation Analysis

2014 ◽  
Vol 15 (3) ◽  
pp. 1293-1302 ◽  
Author(s):  
M. Tugrul Yilmaz ◽  
Wade T. Crow
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Microwave Remote Sensing ◽  
Surface Model ◽  
Correlated Errors ◽  
Cross Covariance ◽  
Mutual Independence ◽  
The Impact

Abstract Triple collocation analysis (TCA) enables estimation of error variances for three or more products that retrieve or estimate the same geophysical variable using mutually independent methods. Several statistical assumptions regarding the statistical nature of errors (e.g., mutual independence and orthogonality with respect to the truth) are required for TCA estimates to be unbiased. Even though soil moisture studies commonly acknowledge that these assumptions are required for an unbiased TCA, no study has specifically investigated the degree to which errors in existing soil moisture datasets conform to these assumptions. Here these assumptions are evaluated both analytically and numerically over four extensively instrumented watershed sites using soil moisture products derived from active microwave remote sensing, passive microwave remote sensing, and a land surface model. Results demonstrate that nonorthogonal and error cross-covariance terms represent a significant fraction of the total variance of these products. However, the overall impact of error cross correlation on TCA is found to be significantly larger than the impact of nonorthogonal errors. Because of the impact of cross-correlated errors, TCA error estimates generally underestimate the true random error of soil moisture products.

scholarly journals Use of microwave remote sensing data to monitor spatio temporal characteristics of surface soil moisture at local and regional scales

2005 ◽  
Vol 5 ◽  
pp. 49-56 ◽  
Author(s):  
A. Löw ◽  
R. Ludwig ◽  
W. Mauser
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Electromagnetic Waves ◽  
Regional Scale ◽  
Soil Layer ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Electromagnetic Spectrum ◽  
Envisat Asar ◽  
Spatio Temporal

Abstract. Hydrologic processes, such as runoff production or evapotranspiration, largely depend on the variation of soil moisture and its spatial pattern. The interaction of electromagnetic waves with the land surface can be dependant on the water content of the uppermost soil layer. Especially in the microwave domain of the electromagnetic spectrum, this is the case. New sensors as e.g. ENVISAT ASAR, allow for frequent, synoptically and homogeneous image acquisitions over larger areas. Parameter inversion models are therefore developed to derive bio- and geophysical parameters from the image products. The paper presents a soil moisture inversion model for ENVISAT ASAR data for local and regional scale applications. The model is validated against in situ soil moisture measurements. The various sources of uncertainties, being related to the inversion process are assessed and quantified.

Sign in / Sign up

Export Citation Format

Share Document