SCATSAT-1 backscattering coefficient over distinct land surfaces and its dependence on soil moisture and vegetation dynamics

2021 ◽  
Vol 42 (17) ◽  
pp. 6481-6501
Author(s):  
Manoj Kumar Mishra ◽  
Nizy Mathew ◽  
R Renju
Keyword(s):  
Soil Moisture ◽  
Vegetation Dynamics ◽  
Backscattering Coefficient ◽  
Land Surfaces

scholarly journals Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

2020 ◽  
Vol 12 (13) ◽  
pp. 2148 ◽  
Author(s):  
Adnan Rajib ◽  
I Luk Kim ◽  
Heather E. Golden ◽  
Charles R. Lane ◽  
Sujay V. Kumar ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Dynamics ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Area Index ◽  
Daily Streamflow ◽  
Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

scholarly journals Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China

2018 ◽  
Vol 10 (10) ◽  
pp. 1577 ◽  
Author(s):  
Chao Wang ◽  
Zhengjia Zhang ◽  
Simonetta Paloscia ◽  
Hong Zhang ◽  
Fan Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Winter Season ◽  
Tibet Plateau ◽  
Permafrost Soil ◽  
Permafrost Region ◽  
Backscattering Coefficient ◽  
Sar Images ◽  
Northern Tibet ◽  
Variation Patterns

Global change has significant impact on permafrost region in the Tibet Plateau. Soil moisture (SM) of permafrost is one of the most important factors influencing the energy flux, ecosystem, and hydrologic process. The objectives of this paper are to retrieve the permafrost SM using time-series SAR images, without the need of auxiliary survey data, and reveal its variation patterns. After analyzing the characteristics of time-series radar backscattering coefficients of different landcover types, a two-component SM retrieval model is proposed. For the alpine meadow area, a linear retrieving model is proposed using the TerraSAR-X time-series images based on the assumption that the lowest backscattering coefficient is measured when the soil moisture is at its wilting point and the highest backscattering coefficient represents the water-saturated soil state. For the alpine desert area, the surface roughness contribution is eliminated using the dual SAR images acquired in the winter season with different incidence angles when retrieving soil moisture from the radar signal. Before the model implementation, landcover types are classified based on their backscattering features. 22 TerraSAR-X images are used to derive the soil moisture in Beiluhe, Northern Tibet with different incidence angles. The results obtained from the proposed method have been validated using in-situ soil moisture measurements, thus obtaining RMSE and Bias of 0.062 cm3/cm3 and 4.7%, respectively. The retrieved time-series SM maps of the study area point out the spatial and temporal SM variation patterns of various landcover types.

Mapping surface soil moisture over wheat crops in southern Mediterranean regions using the backscattering coefficient and the interferometric coherence derived from Sentinel-1

2020 ◽  
Author(s):  
Nadia Ouaadi ◽  
Lionel Jarlan ◽  
Jamal Ezzahar ◽  
Saïd Khabba ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
Surface Soil ◽  
Cloud Model ◽  
Microwave Remote Sensing ◽  
Radar Data ◽  
Optical Data ◽  
Surface Soil Moisture ◽  
Backscattering Coefficient ◽  
New Approach

<p>High spatial and temporal resolution products of Sentinel-1 are used for surface soil moisture (SSM) mapping over wheat fields in semi-arid areas. Within these regions, monitoring the water-use is a critical aspect for optimizing the management of the limited water resources via irrigation monitoring. SSM is one of the principal quantities affecting microwave remote sensing. This sensitivity has been exploited to estimate SSM from radar data, which has the advantages of providing data independent of illumination and weather conditions. In addition, with the use of Sentinel-1 products, the spatial and temporal resolution is greatly improved. Within this context, the main objective of this work is estimate SSM over wheat fields using an approach based on the use of C-band Sentinel-1 radar data only. Over the study site, field measurement are collected during 2016-2017 and 2017-2018 growing seasons over two fields of winter wheat with drip irrigation located in the Haouz plain in the center of Morocco. Data of other sites in Morocco and Tunisia are taken for validation purposes. The validation database contains a total number of 20 plots divided between irrigated and rainfed wheat plots. Two different information extracted from Sentinel-1 products are used: the backscattering coefficient and the interferometric coherence. A total number of 408 GRD and 419 SLC images were processed for computing the backscattering coefficient and the interferometric coherence, respectively. The analysis of Sentinel-1 time series over the study site show that coherence is sensitive to the development of wheat, while the backscatter coefficient is widely linked to changes in surface soil moisture. Later on, the Water Cloud Model coupled with the Oh et al, 1992 model were used for better understand the backscattering mechanism of wheat canopies. The coupled model is calibrated and validated over the study site and it proved to goodly enough reproduce the Sentinel-1 backscatter with RMSE ranging from 1.5 to 2.52 dB for VV and VH using biomass as a descriptor of wheat. On the other side, the analysis show that coherence is well correlated to biomass. Thus, the calibrated model is used in an inversion algorithm to retrieve SSM using the Sentinel-1 backscatter and coherence as inputs. The results of inversion show that the proposed new approach is able to retrieve the surface soil moisture at 35.2° for VV, with R=0.82, RMSE=0.05m<sup>3/</sup>m<sup>3 </sup>and no bias. Using the validation database of Morocco and Tunisia, R is always greater than 0.7 and RMSE and bias are less than 0.008 m<sup>3/</sup>m<sup>3</sup> and 0.03 m<sup>3/</sup>m<sup>3</sup>, respectively even that the incidence angle is higher (40°). In order to assess its quality, the approach is compared to four SSM retrieval methods that use radar and optical data in empirical and semi-empirical approaches. Results indicate that the proposed approach shows an improvement of SSM retrieval between 17% and 42% compared to other methods. Finally, the validated new approach is used for SSM mapping, with a spatial resolution of 10*10 m, over irrigated perimeters of wheat in Morocco.</p>

scholarly journals Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review

2020 ◽  
Vol 12 (18) ◽  
pp. 2915 ◽  
Author(s):  
Frédéric Frappart ◽  
Jean-Pierre Wigneron ◽  
Xiaojun Li ◽  
Xiangzhuo Liu ◽  
Amen Al-Yaari ◽  
...  
Keyword(s):  
Optical Depth ◽  
Vegetation Dynamics ◽  
Canopy Layer ◽  
Wide Range ◽  
Vegetation Water ◽  
Carbon Balances ◽  
Land Surfaces ◽  
Earth Observation Satellites ◽  
Content Status ◽  
New Sensors

Vegetation is a key element in the energy, water and carbon balances over the land surfaces and is strongly impacted by climate change and anthropogenic effects. Remotely sensed observations are commonly used for the monitoring of vegetation dynamics and its temporal changes from regional to global scales. Among the different indices derived from Earth observation satellites to study the vegetation, the vegetation optical depth (VOD), which is related to the intensity of extinction effects within the vegetation canopy layer in the microwave domain and which can be derived from both passive and active microwave observations, is increasingly used for monitoring a wide range of ecological vegetation variables. Based on different frequency bands used to derive VOD, from L- to Ka-bands, these variables include, among others, the vegetation water content/status and the above ground biomass. In this review, the theoretical bases of VOD estimates for both the passive and active microwave domains are presented and the global long-term VOD products computed from various groups in the world are described. Then, major findings obtained using VOD are reviewed and the perspectives offered by methodological improvements and by new sensors onboard satellite missions recently launched or to be launched in a close future are presented.

scholarly journals The Capability of Integrating Optical and Microwave Data for Detecting Soil Moisture in an Oasis Region

2020 ◽  
Vol 12 (9) ◽  
pp. 1358 ◽  
Author(s):  
Shuai Huang ◽  
Jianli Ding ◽  
Bohua Liu ◽  
Xiangyu Ge ◽  
Jinjie Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Empirical Relationship ◽  
Vegetation Indices ◽  
Coefficient Of Determination ◽  
Ratio Model ◽  
Retrieval Model ◽  
Backscattering Coefficient ◽  
Strong Control

In the earth ecosystem, surface soil moisture is an important factor in the process of energy exchange between land and atmosphere, which has a strong control effect on land surface evapotranspiration, water migration, and carbon cycle. Soil moisture is particularly important in an oasis region because of its fragile ecological environment. Accordingly, a soil moisture retrieval model was conducted based on Dubois model and ratio model. Based on the Dubois model, the in situ soil roughness was used to simulate the backscattering coefficient of bare soil, and the empirical relationship was established with the measured soil moisture. The ratio model was used to eliminate the backscattering contribution of vegetation, in which three vegetation indices were used to characterize vegetation growth. The results were as follows: (1) the Dubois model was used to calibrate the unknown parameters of the ratio model and verified the feasibility of the ratio model to simulate the backscattering coefficient. (2) All three vegetation indices (Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Enhanced Vegetation Index (EVI)) can represent the scattering characteristics of vegetation in an oasis region, but the VWC vegetation index is more suitable than the others. (3) Based on the Dubois model and ratio model, the soil moisture retrieval model was conducted, and the in situ soil moisture was used to analyze the accuracy of the simulated soil moisture, which found that the soil moisture retrieval accuracy is the highest under VWC vegetation index, and the coefficient of determination is 0.76. The results show that the soil moisture retrieval model conducted on the Dubois model and ratio model is feasible.

scholarly journals Retrieval of Soil Moisture by Integrating Sentinel-1A and MODIS Data over Agricultural Fields

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1726 ◽  
Author(s):  
Yizhi Han ◽  
Xiaojing Bai ◽  
Wei Shao ◽  
Jie Wang
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Vegetation Index ◽  
Coupled Model ◽  
Agricultural Drought ◽  
Agricultural Fields ◽  
Backscattering Coefficient ◽  
Modis Data ◽  
Vegetation Parameters ◽  
Scattering Contribution

Soil moisture is an essential variable in the land surface ecosystem, which plays an important role in agricultural drought monitoring, crop status monitoring, and crop yield prediction. High-resolution radar data can be combined with optical remote-sensing data to provide a new approach to estimate high-resolution soil moisture over vegetated areas. In this paper, the Sentinel-1A data and the Moderate Resolution Imaging Spectroradiometer (MODIS) data are combined to retrieve soil moisture over agricultural fields. The advanced integral equation model (AIEM) is utilized to calculate the scattering contribution of the bare soil surface. The water cloud model (WCM) is applied to model the backscattering coefficient of vegetated areas, which use two vegetation parameters to parameterize the scattering and attenuation properties of vegetation. Four different vegetation parameters extracted from MODIS products are combined to predict the scattering contribution of vegetation, including the leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI). The effective roughness parameters are chosen to parameterize the AIEM. The Sentinel-1A and MODIS data in 2017 are used to calibrate the coupled model, and the datasets in 2018 are used for soil moisture estimation. The calibration results indicate that the Sentinel-1A backscattering coefficient can be accurately predicted by the coupled model with the Pearson correlation coefficient (R) ranging from 0.58 to 0.81 and a root mean square error (RMSE) ranging from 0.996 to 1.401 dB. The modeled results show that the retrieved soil moisture can capture the seasonal dynamics of soil moisture with R ranging from 0.74 to 0.81. With the different vegetation parameter combinations used for parameterizing the scattering contribution of the canopy, the importance of suitable vegetation parameters for describing the scattering and attenuation properties of vegetation is confirmed. The LAI is recommended to characterize the scattering properties. There is no obvious clue for selecting vegetation descriptors to characterize the attenuation properties of vegetation. These promising results confirm the feasibility and validity of the coupled model for soil moisture retrieval from the Sentinel-1A and MODIS data.

Sentinel-1 application to rainfall response and geomorphic mapping

2021 ◽  
Author(s):  
Stephanie Olen ◽  
Bodo Bookhagen
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Snow Cover ◽  
Rainfall Event ◽  
Rainfall Events ◽  
Northwestern Argentina ◽  
Standing Water ◽  
Land Surfaces ◽  
High Infiltration ◽  
Semi Arid

<p>Rainfall is one of the primary geomorphic drivers on Earth’s surface. How a surface responds to rainfall directly impacts erosional, geomorphic, and natural hazard processes. In the absence of vegetation, whether a land surface retains rainfall as soil moisture or whether rainfall is quickly infiltrated or run off is largely a function of geomorphologic and geologic conditions. In this study, we combine a time series of synthetic aperture radar (SAR) backscatter with daily precipitation to analyze the response of arid and semi-arid land surfaces to rainfall from the event to seasonal scale. The study focuses on northwestern Argentina, where we have extensive field knowledge of local geomorphic features, and is implemented using the cloud computing capacities of Google Earth Engine (GEE).</p><p>Th Sentinel-1 satellites provide high spatial (10 m) and temporal resolution images of Earth’s surface, irrespective of cloud cover. We created a 3 year time series from 2018 through 2020 of Sentinel-1 sigma-naught (σ<sub>0</sub>) backscatter from Ground Range Detected (GRD) products available on GEE. Combining the ascending and descending orbits of the Sentinel-1A and -1B satellites into a single time series provides 3 to 6 day temporal resolution in our area of interest. The Global Precipitation Measurement Mission (GPM) was aggregated to daily and monthly precipitation measurements to identify single rainfall events and the seasonal rainfall signal.</p><p>The response and recovery of SAR backscatter to individual rainfall events across different land surfaces was calculated over 4 to 6 week periods centered on and following a specific rainfall date, respectively. The temporal trend of the backscatter data in these time windows is calculated for every pixel in the backscatter stack to create a map how the surface responds to a large rainfall event. The location of standing water, increased soil moisture, and high infiltration surfaces are detectable in the response maps. The recovery maps provide a proxy for the rate of drying following the rainfall event.</p><p>In the monsoon-dominated region of northwestern Argentina, both precipitation and SAR backscatter show a clear, periodic seasonal signal over our three-year time series. By aggregating all data to monthly resolution, we can calculate pixel-wise linear regressions and correlation coefficients between precipitation and SAR backscatter. Regressions and correlation analysis are done at the resolution of the Sentinel-1 data and are used to identify whether a surface retains soil moisture, has high infiltration, or experiences seasonal standing water or snow cover. Areas dominated by highly weathered granites and sandstones that can retain soil moisture, for example, have strong positive correlation between rainfall and backscatter due to the increased dielectric constant of wet sediment. In contrast, gravel terraces where rainfall can easily infiltrate the surface show little correlation between backscatter and precipitation. The result is a high resolution map characterizing the propensity for soil moisture retention, high infiltration, and standing water and snow cover. Future work will focus on using these relationships to classify geomorphic surfaces across the arid and semi-arid central Andes.</p>

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