scholarly journals Observed Land–Atmosphere Coupling from Satellite Remote Sensing and Reanalysis

2011 ◽  
Vol 12 (6) ◽  
pp. 1221-1254 ◽  
Author(s):  
Craig R. Ferguson ◽  
Eric F. Wood
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Deep Convection ◽  
Weather Prediction ◽  
Remote Sensing Data ◽  
Feedback Signal ◽  
Atmospheric Conditions ◽  
Global Land

Abstract The lack of observational data for use in evaluating the realism of model-based land–atmosphere feedback signal and strength has been deemed a major obstacle to future improvements to seasonal weather prediction by the Global Land–Atmosphere Coupling Experiment (GLACE). To address this need, a 7-yr (2002–09) satellite remote sensing data record is exploited to produce for the first time global maps of predominant coupling signals. Specifically, a previously implemented convective triggering potential (CTP)–humidity index (HI) framework for describing atmospheric controls on soil moisture–rainfall feedbacks is revisited and generalized for global application using CTP and HI from the Atmospheric Infrared Sounder (AIRS), soil moisture from the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E), and the U.S. Climate Prediction Center (CPC) merged satellite rainfall product (CMORPH). Based on observations taken during an AMSR-E-derived convective rainfall season, the global land area is categorized into four convective regimes: 1) those with atmospheric conditions favoring deep convection over wet soils, 2) those with atmospheric conditions favoring deep convection over dry soils, 3) those with atmospheric conditions that suppress convection over any land surface, and 4) those with atmospheric conditions that support convection over any land surface. Classification maps are produced using both the original and modified frameworks, and later contrasted with similarly derived maps using inputs from the National Aeronautics and Space Administration (NASA) Modern Era Retrospective Analysis for Research and Applications (MERRA). Both AIRS and MERRA datasets of CTP and HI are validated using radiosonde observations. The combinations of methods and data sources employed in this study enable evaluation of not only the sensitivity of the classification schemes themselves to their inputs, but also the uncertainty in the resultant classification maps. The findings are summarized for 20 climatic regions and three GLACE coupling hot spots, as well as zonally and globally. Globally, of the four-class scheme, regions for which convection is favored over wet and dry soils accounted for the greatest and least extent, respectively. Despite vast differences among the maps, many geographically large regions of concurrence exist. Through its ability to compensate for the latitudinally varying CTP–HI–rainfall tendency characteristics observed in this study, the revised classification framework overcomes limitations of the original framework. By identifying regions where coupling persists using satellite remote sensing this study provides the first observationally based guidance for future spatially and temporally focused studies of land–atmosphere interactions. Joint distributions of CTP and HI and soil moisture, rainfall occurrence, and depth demonstrate the relevance of CTP and HI in coupling studies and their potential value in future model evaluation, rainfall forecast, and/or hydrologic consistency applications.

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>

A data-driven approach for mapping global surface soil moisture at 100 m using high-resolution remote sensing data and land surface parameters

2021 ◽  
Author(s):  
Jingyi Huang ◽  
Ankur Desai ◽  
Jun Zhu ◽  
Alfred Hartemink ◽  
Paul Stoy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Field Scale ◽  
Land Surface Parameters ◽  
Global Surface

<p>Current in situ soil moisture monitoring networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical global surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100m and performed moderately well under cultivated, herbaceous, forest, and shrub soils (R<sup>2</sup> = 0.524, RMSE = 0.07 m<sup>3</sup> m<sup>−3</sup>). It has a relatively good transferability at the regional scale among different continental and regional networks (mean RMSE = 0.08–0.10 m<sup>3</sup> m<sup>−3</sup>). The global model was then applied to map SSM dynamics at 30–100m across a field-scale network (TERENO-Wüstebach) in Germany and an 80-ha irrigated cropland in Wisconsin, USA. Without local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was also affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and high-resolution soil maps as well as assimilation with process-based water flow models.</p>

scholarly journals The Global Land Surface Satellite (GLASS) Remote Sensing Data Processing System and Products

2013 ◽  
Vol 5 (5) ◽  
pp. 2436-2450 ◽  
Author(s):  
Xiang Zhao ◽  
Shunlin Liang ◽  
Suhong Liu ◽  
Wenping Yuan ◽  
Zhiqiang Xiao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Land Surface ◽  
Processing System ◽  
Remote Sensing Data ◽  
Data Processing System ◽  
Sensing Data ◽  
Global Land

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>

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