Search

Satellite SAR-based soil moisture retrieval over agricultural fields, under crop overlain conditions, is a challenging exercise. This is so because the overlying crop volume interacts with both the incoming and the backscattered radar signal. Therefore, the soil moisture linked solely to the top layer (0–5 cm) of the soil cannot be reliably retrieved under such conditions without avoiding the obscuring effect of growing crop volume. In this investigation, we demonstrated a proof-of-concept for a time-series approach to retrieve soil moisture during crop growth cycle. Contrary to the use of the single-scene approach, the novelty of the proposed approach lies in exploiting the satellite SAR time series acquired during a cropping cycle. The proposed time-series approach is effective for capturing the nuances in the crop phenological stages while calibrating the Dubois–water cloud model (WCM) soil moisture retrieval model. By employing this approach, we achieved the 0.04 $\text{m}^{3}\;\text{m}^{-3}$ soil moisture retrieval root-mean-square error benchmark at a high spatial resolution and addressed the issue of solving for the Dubois–WCM model constants under data-constrained conditions. Furthermore, we observed that the combination of temporally non-overlapping vegetation descriptors (optical and SAR) resulted in degradation in the performance of the retrievals and under such circumstances single polarimetric descriptor performed better.

In this investigation, optical (SPOT-7 NAOMI), airborne LiDAR, and PolInSAR L-band data, along with forest inventories, were employed to develop models for estimating total dry aboveground biomass (AGB) over the tropical forests in the Congo Basin (Gabon) of Central Africa. Remote sensing-based variables like texture (from SPOT), median canopy height (from LiDAR), and backscattering coefficient along with canopy surface heights (from PolInSAR) were used to estimate the AGB. These variables were used individually (or combined) to develop the AGB models based on the multivariate adaptive regression splines (MARS) approach. Validation indicated that in case of the single variable models, the LiDAR-based model yielded the lowest estimation root-mean-square error (RMSE = 28%). The error decreased further when the median canopy height was combined with the texture or with the radar variables (RMSE

Accurate estimation and regular monitoring of soil moisture is very important for many agricultural, hydrological, or climatological applications. Our objective was to evaluate potential contributions of polarimetry to soil moisture estimation during crop growing cycles using RADARSAT-2 C-band images. The research focused on wheat field data collected during Soil Moisture Active Passive Validation Experiment (SMAPVEX12) conducted in 2012 in Manitoba (Canada). A sensitivity analysis was performed to select the most relevant non-polarimetric and polarimetric variables extracted from RADARSAT-2, and statistical models were developed to estimate soil moisture. In fine, three models were developed and validated: a non-polarimetric model based on cross-polarized backscattering coefficient σHV0; a polarimetric mixed model using six polarimetric and non-polarimetric retained variables after the sensitivity analysis; and a simplified polarimetric mixed model considering only the phase difference (ϕHH−VV) and the co-polarized backscattering coefficient σHH0. The validation reveals significant positive contributions of polarimetry. It shows that the non-polarimetric model has a much larger error (RMSE = 0.098 m3/m3) and explains only 19% of observed soil moisture variation compared to the polarimetric mixed model, which has an error of 0.087 m3/m3, with an explained variance of 44%. The simplified model has the lowest error (0.074 m3/m3) and explains 53.5% of soil moisture variation.

The Saskatchewan River Basin (SRB) of central Canada plays a crucial role in the Canadian Prairies. Yet, climate change and human action constitute a real threat to its hydrological processes. This study aims to evaluate and analyze groundwater spatial and temporal dynamics in the SRB. Groundwater information was derived and compared using two different approaches: (1) a mathematical modeling framework coupling the Soil and Water Assessment Tool (SWAT) and the Modular hydrologic model (MODFLOW) and (2) gravimetric satellite observations from the Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on (GRACE-FO). Both methods show generalized groundwater depletion in the SRB that can reach −1 m during the study period (2002–2019). Maximum depletion appeared especially after 2011. The water balance simulated by SWAT-MODFLOW showed that SRB could be compartmented roughly into three main zones. The mountainous area in the extreme west of the basin is the first zone, which is the most dynamic zone in terms of recharge, reaching +0.5 m. The second zone is the central area, where agricultural and industrial activities predominate, as well as potable water supplies. This zone is the least rechargeable and most intensively exploited area, with depletion ranging from +0.2 to −0.4 m during the 2002 to 2011 period and up to −1 m from 2011 to 2019. Finally, the third zone is the northern area that is dominated by boreal forest. Here, exploitation is average, but the soil does not demonstrate significant storage power. Briefly, the main contribution of this research is the quantification of groundwater depletion in the large basin of the SRB using two different methods: process-oriented and satellite-oriented methods. The next step of this research work will focus on the development of artificial intelligence approaches to estimate groundwater depletion from a combination of GRACE/GRACE-FO and a set of multisource remote sensing data.

Mapping soil moisture (SM) at high spatial resolution assists to trigger important agricultural management, such as irrigation, to enhance crop yields. This study investigates disaggregation of SMAP brightness temperature (TB) using RADARSAT-2 polarimetric decompositions to retrieve high-resolution SM. Compared to Sentinel-1 backscattering coefficients used in the SMAP baseline active–passive SM retrieval algorithms, the RADARSAT-2 surface scattering power Ps with a reduced vegetation influence was hypothesized to be more relevant to disaggregate the SMAP TB. Different polarimetric decompositions were evaluated to extract an optimal Ps, followed by an incidence angle normalization. Then, the optimal Ps parameter was aggregated to the same spatial resolution as the SMAP TB to develop empirical relationships between Ps and TB. Furthermore, the airborne TB data collected by Passive Active l-band Sensor (PALS) were analyzed in terms of the Ps across multiple spatial resolutions, to account for the scale effect on the Ps/TB relationships. Finally, the τ-ω emission model was used to retrieve SM at multiple spatial resolutions (10 km, 1 km, 500 m, 100 m, and 50 m). The impacts of spatial resolution on retrieval accuracy were analyzed to determine the best spatial resolution for SM retrievals. The results indicated that the An polarimetric decomposition with the de-orientation provided the highest surface scattering powers, which may benefit the SM estimation. In contrast to the traditional cosine algorithms, the incidence angle normalization of Ps with span resulted in a temporally decreasing surface scattering power, because of the increasing vegetation attenuation as the crop grows. The sensitivity of TB to Ps decreases as the resolution scale varies from 36 km to 50 m. The SM retrievals across multiple resolutions obtained marginal differences in retrieval accuracy. Although slightly better results were obtained with 1 km spatial resolution which is close to the nominal size of agricultural fields in the study area (R = 0.68–0.8 and RMSE = 0.039–0.062 m3/m3), the retrievals at 50 m spatial resolution (R = 0.63–0.76 and RMSE = 0.046–0.067 m3/m3) capture the spatial heterogeneity of SM within and across different fields which could be very helpful for the precision agriculture.

Accurately estimating hydrological parameters is crucial for comprehending global water resources and climate dynamics. This study addresses the challenge of quantifying uncertainties in the global land data assimilation system (GLDAS) model and enhancing the accuracy of downscaled gravity recovery and climate experiment (GRACE) data. Although the GLDAS models provide valuable information on hydrological parameters, they lack uncertainty quantification. To enhance the resolution of GRACE data, a spectral downscaling approach can be employed, leveraging uncertainty estimates. In this study, we propose a novel approach, referred to as method 2, which incorporates parameter magnitudes to estimate uncertainties in the GLDAS model. The proposed method is applied to downscale GRACE data over Alberta, with a specific focus on December 2003. The groundwater storage extracted from the downscaled terrestrial water storage (TWS) are compared with measurements from piezometric wells, demonstrating substantial improvements in accuracy. In approximately 80% of the wells, the root mean square (RMS) and standard deviation (STD) were improved to less than 5 mm. These results underscore the potential of the proposed approach to enhance downscaled GRACE data and improve hydrological models.

Hydrosystems in the Saskatchewan River Basin of the Canadian Prairies are subject to natural and socioeconomic pressures. Increasingly, these strong pressures are exacerbating problems of water resource accessibility and depletion. Unfortunately, the geometric heterogeneity of the aquifers and the presence of lithologically varied layers complicate groundwater flow studies, hydrodynamic characterization, and aquifer storativity calculations. Moreover, in recent hydrogeological studies, hydraulic conductivity has been the subject of much more research than storativity. It is in this context that the present research was conducted, to establish a 3D hydrostratigraphic model that highlights the geological (lithology, thickness, and depth) and hydrodynamic characteristics of the aquifer formations and proposes a new uncertainty framework for groundwater storage estimation. The general methodology is based on collecting and processing a very fragmentary and diverse multi-source database to develop the conceptual model. Data were harmonized and entered into a common database management system. A large quantity of geological information has been implemented in a 3D hydrostratigraphic model to establish the finest geometry of the SRB aquifers. Then, the different sources of uncertainty were controlled and considered in the modeling process by developing a randomized modeling system based on spatial random bagging simulation (SRBS). The results of the research show the following: Firstly, the distribution of aquifer levels is controlled by tectonic activity and erosion, which further suggests that most buried valleys on the Prairies have filled over time, likely during multiple glaciations in several depositional environments. Secondly, the geostatistical study allowed us to choose optimal interpolation variographic parameters. Finally, the final storativity maps of the different aquifer formations showed a huge potential of groundwater in SRB. The SRBS method allowed us to calculate the optimal storativity values for each mesh and to obtain a final storativity map for each formation. For example, for the Paskapoo Formation, the distribution grid of groundwater storage shows that the east part of the aquifer can store up to 5920 × 103 m3/voxel, whereas most areas of the west aquifer part can only store less than 750 × 103 m3/voxel. The maximum storativity was attributed to the Horseshoe Canyon Formation, which contains maximal geological reserves ranging from 107 to 111 × 109 m3. The main contribution of this research is the proposed 3D geological model with hydrogeological insights into the study area, as well as the use of a new statistical method to propagate the uncertainty over the modeling domain. The next step will focus on the hydrodynamic modeling of groundwater flow to better manage water resources in the Saskatchewan River Basin.

Soil moisture (SM) is a key variable in ecology, environment, agriculture, and hydrology. The Soil Moisture Active Passive (SMAP) satellite provides global SM products with reliable accuracy since 2015. However, significant gaps of SMAP SM appeared over Tibetan Plateau. Considering the important role of the Tibetan Plateau in global climate and environment, it is essential to develop methods to infill the gaps to generate seamless SMAP SM data. To address this issue, we proposed two methods, machine learning and geostatistics technique. For the machine learning technique, we train a Random Forest algorithm which aims to match the output of available SMAP L3 SM using a series of input variables such as SMAP brightness temperature (TBH and TBV) in ascending orbits (6:00 PM local time), surface temperature, MODIS NDVI, land cover, DEM, and other auxiliary data. Then, the established RF estimators were applied to the SMAP brightness temperature from descending orbits (6:00 AM local time) to reconstruct complete SM data over the Tibetan Plateau. For the geostatistics technique, the Ordinary kriging was applied to the available SMAP L3 SM pixels to interpolate complete SM data. To cross-validate the performances of the algorithms, we assume certain areas with available SMAP SM values as missing, and then compared the gap-filling results with the actual ones. The cross-validations show that the gap-filling results from two algorithms were highly correlated to the official SMAP SM products with high coefficients of determination (R2RF = 0.97 and R2OK = 0.85) and low RMSE (RMSERF = 0.015 cm3/cm3 and RMSEOK = 0.036 cm3/cm3). Furthermore, the gap-filling SM data present a better correlation with the Soil Moisture and Ocean Salinity SM data (R = 0.55–0.7) than the Global Land Data Assimilation System simulations (R = 0.18–0.62). The reconstructed SM from RF (R = 0.71) and OK (R = 0.55) algorithms are well related to the Maqu network measurements. Thus, the machine learning and geostatistics algorithms have the potential to reproduce the missing SMAP SM products over the Tibetan Plateau.

La présence de cibles hétérogènes, comme la glace, reste un défi majeur pour l’utilisation des données altimétriques au-dessus des plans d’eau continentaux. Les satellites Jason-2 et SARAL/Altika utilisent des algorithmes de retraitement conçus pour traiter les formes d’onde non continentales afin d’obtenir des estimations améliorées. Dans cette étude, nous analysons le potentiel des produits dérivés de ces algorithmes pour estimer le niveau d’eau de 20 plans d’eau couverts par la glace répartis à travers le Canada. Les estimations de niveaux d’eau des algorithmes de retraitement sont comparées aux mesures in situ pendant deux périodes: la période entièrement couverte par chacun des deux satellites dans l’étude (2008–2016 pour Jason-2, et 2013–2016 pour SARAL/Altika); ainsi que les périodes de gel-dégel incluses dans les séries chronologiques. Les algorithmes produisent des incertitudes très variables, en fonction de la taille des cours d’eau et des conditions de la glace. Dans l’ensemble, l’algorithme ICE-1 utilisé par Jason-2 fournit les meilleures estimations de niveau d’eau, avec des erreurs RMSE non biaisées ≤0.3 m et des R2 ≥ 0.8 pour 90% des plans d’eau. Tous les algorithmes de retraitement utilisés par SARAL/Altika donnent des résultats très comparables aux observations in situ, démontrant les bonnes performances de la technologie SARAL.

This study proposes a new mathematical approach to downscale monthly terrestrial water storage anomalies (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) and estimates groundwater storage anomalies (GWSA) at a daily temporal resolution and a spatial resolution of 0.25° × 0.25°, simultaneously. The method combines monthly 3° GRACE gravity models and daily 0.25° hydrological model outputs and their uncertainties in the spectral domain by minimizing the mean-square error (MSE) of their estimator to enhance the quality of both low and high frequency signals in the estimated TWSA and GWSA. The Global Land Data Assimilation System (GLDAS) was the hydrological model considered in this study. The estimator was tested over Alberta, Saskatchewan, and Manitoba (Canada), especially over the Province of Alberta, using data from 65 in-situ piezometric wells for 2003. Daily minimum and maximum GWS varied from 14 mm to 32 mm across the study area. A comparison of the estimated GWSA with the corresponding in-situ wells showed significant and consistent correlations in most cases, with r = 0.43–0.92 (mean r = 0.73). Correlations were >0.70 for approximately 70% of the wells, with root mean square errors