Spatio-temporal estimation of soil moisture in a tropical region using a remote sensing algorithm

2011 ◽  
Author(s):  
Liliana Marrufo ◽  
Fernando Gonzalez ◽  
Alejandro Monsivais-Huertero ◽  
Judith Ramos
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Tropical Region ◽  
Temporal Estimation ◽  
Spatio Temporal ◽  
Remote Sensing Algorithm

Sensitivity of a soil-moisture remote sensing algorithm at L band to variations in input parameters

1999 ◽  
Author(s):  
Andrew Manu ◽  
Charles A. Laymon ◽  
Frank Archer ◽  
Tommy L. Coleman
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Input Parameters ◽  
L Band ◽  
Remote Sensing Algorithm

scholarly journals A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

2020 ◽  
Vol 12 (3) ◽  
pp. 455 ◽  
Author(s):  
Yaokui Cui ◽  
Xi Chen ◽  
Wentao Xiong ◽  
Lian He ◽  
Feng Lv ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
The Tibetan Plateau ◽  
Sensing Data ◽  
Spatio Temporal ◽  
Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

Evapotranspiration and soil moisture indexes derived from remote sensing data to identify and investigate the mechanisms of the spatio-temporal patterns of drought in the Ebro-Basin (NE Spain).

2020 ◽  
Author(s):  
Jaime Gaona ◽  
Pere Quintana-Seguí ◽  
Maria José Escorihuela
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Remote Sensing Data ◽  
Temporal Patterns ◽  
Surface Model ◽  
Ebro Basin ◽  
Sensing Data ◽  
Spatio Temporal

<p>The Mediterranean climate of the Iberian Peninsula defines high spatial and temporal variability of drought at multiple scales. These droughts impact human activities such as water management, agriculture or forestry, and may alter valuable natural ecosystems as well. An accurate understanding and monitoring of drought processes are crucial in this area. The HUMID project (CGL2017-85687-R) is studying how remote sensing data and models (Quintana-Seguí et al., 2019; Barella-Ortiz and Quintana-Seguí, 2019) can improve our current knowledge on Iberian droughts, in general, and in the Ebro basin, more specifically.</p><p>The traditional ground-based monitoring of drought lacks the spatial resolution needed to identify the microclimatic mechanisms of drought at sub-basin scale, particularly when considering relevant variables for drought such as soil moisture and evapotranspiration. In situ data of these two variables is very scarce.</p><p>The increasing availability of remote sensing products such as MODIS16 A2 ET and the high-resolution SMOS 1km facilitates the use of distributed observations for the analysis of drought patterns across scales. The data is used to generate standardized drought indexes: the soil moisture deficit index (SMDI) based on SMOS 1km data (2010-2019) and the evapotranspiration deficit index (ETDI) based on MODIS16 A2 ET 500m. The study aims to identify the spatio-temporal mechanisms of drought generation, propagation and mitigation within the Ebro River basin and sub-basins, located in NE Spain where dynamic Atlantic, Mediterranean and Continental climatic influences dynamically mix, causing a large heterogeneity in climates.</p><p>Droughts in the 10-year period 2010-2019 of study exhibit spatio-temporal patterns at synoptic and mesoscale scales. Mesoscale spatio-temporal patterns prevail for the SMDI while the ETDI ones show primarily synoptic characteristics. The study compares the patterns of drought propagation identified with remote sensing data with the patterns estimated using the land surface model SURFEX-ISBA at 5km.  The comparison provides further insights about the capabilities and limitations of both tools, while emphasizes the value of combining approaches to improve our understanding about the complexity of drought processes across scales.</p><p>Additionally, the periods of quick change of drought indexes comprise valuable information about the response of evapotranspiration to water deficits as well as on the resilience of soil to evaporative stress. The lag analysis ranges from weeks to seasons. Results show lags between the ETDI and SMDI ranging from days to weeks depending on the precedent drought status and the season/month of drought’s generation or mitigation. The comparison of the lags observed on remote sensing data and land surface model data aims at evaluating the adequacy of the data sources and the indexes to represent the nonlinear interaction between soil moisture and evapotranspiration. This aspect is particularly relevant for developing drought monitoring aiming at managing the impact of drought in semi-arid environments and improving the adaptation to drought alterations under climate change.</p>

Construction of Drought Indices from Passive Microwave Remote Sensing AMSR-E Data in Huaihe River Basin

2013 ◽  
Vol 397-400 ◽  
pp. 2503-2506
Author(s):  
Rui Wang ◽  
Jing Wen Xu ◽  
Dan Wang ◽  
Xing Mei Xie ◽  
Peng Wang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
River Basin ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Drought Indices ◽  
Huaihe River Basin ◽  
Huaihe River ◽  
Passive Microwave Remote Sensing ◽  
Spatio Temporal

On the basis of previous work, this paper aims to build several proper drought indices based on passive microwave remote sensing AMSR-E data in Huaihe River Basin. Compared with measured soil moisture, optimal drought indices have been selected to explore the spatio-temporal variation of drought conditions. The results indicate that there are satisfactory negative correlations between MPDIs (Microwave Polarization Index) and observed soil moisture. Moreover, MPDIs calculated by bands of 69GHz and 187GHz are much closer to variation trend of soil moisture than those obtained by other bands.

Bayesian Spatio-temporal Geostatistics-based Method for Multiple Satellite Products Fusion and Downscaling

2020 ◽  
Author(s):  
Yanchen Bo
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Hydrological Cycle ◽  
Microwave Remote Sensing ◽  
Earth Surface ◽  
Spatio Temporal ◽  
High Level

<p>High-level satellite remote sensing products of Earth surface play an irreplaceable role in global climate change, hydrological cycle modeling and water resources management, environment monitoring and assessment. Earth surface high-level remote sensing products released by NASA, ESA and other agencies are routinely derived from any single remote sensor. Due to the cloud contamination and limitations of retrieval algorithms, the remote sensing products derived from single remote senor are suspected to the incompleteness, low accuracy and less consistency in space and time. Some land surface remote sensing products, such as soil moisture products derived from passive microwave remote sensing data have too coarse spatial resolution to be applied at local scale. Fusion and downscaling is an effective way of improving the quality of satellite remote sensing products.</p><p>We developed a Bayesian spatio-temporal geostatistics-based framework for multiple remote sensing products fusion and downscaling. Compared to the existing methods, the presented method has 2 major advantages. The first is that the method was developed in the Bayesian paradigm, so the uncertainties of the multiple remote sensing products being fused or downscaled could be quantified and explicitly expressed in the fusion and downscaling algorithms. The second advantage is that the spatio-temporal autocorrelation is exploited in the fusion approach so that more complete products could be produced by geostatistical estimation.</p><p>This method has been applied to the fusion of multiple satellite AOD products, multiple satellite SST products, multiple satellite LST products and downscaling of 25 km spatial resolution soil moisture products. The results were evaluated in both spatio-temporal completeness and accuracy.</p>

scholarly journals MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

2016 ◽  
Vol 8 (12) ◽  
pp. 990 ◽  
Author(s):  
Sat Tomer ◽  
Ahmad Al Bitar ◽  
Muddu Sekhar ◽  
Mehrez Zribi ◽  
Soumya Bandyopadhyay ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Passive Microwave Remote Sensing ◽  
Spatio Temporal

scholarly journals A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7406
Author(s):  
Nitu Ojha ◽  
Olivier Merlin ◽  
Abdelhakim Amazirh ◽  
Nadia Ouaadi ◽  
Vincent Rivalland ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Radar Data ◽  
Passive Microwave ◽  
Field Scale ◽  
Data Set ◽  
Thermal Data ◽  
Spatio Temporal

Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

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