Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to cloud free ...conditions, whereas SAR imagery is plagued by noise-like speckle. Prior studies that use combinations of MS and SAR data to overcome individual limitations of these sensors have not fully examined sensitivity of flood mapping performance to different combinations of SAR and MS derived spectral indices or band transformations in color space. This study explores the use of diverse bands of Sentinel 2 (S2) through well-established water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to assess their capability for generating accurate flood inundation maps. The robustness in performance of S-1 and S-2 band combinations was evaluated using 446 hand labeled flood inundation images spanning across 11 flood events from Sen1Floods11 dataset which are highly diverse in terms of land cover as well as location. A modified K-fold cross validation approach is used to evaluate the performance of 32 combinations of S1 and S2 bands using a fully connected deep convolutional neural network known as U-Net. Our results indicated that usage of elevation information has improved the capability of S1 imagery to produce more accurate flood inundation maps. Compared to a median F1 score of 0.62 when using only S1 bands, the combined use of S1 and elevation information led to an improved median F1 score of 0.73. Water extraction indices based on S2 bands have a statistically significant superior performance in comparison to S1. Among all the band combinations, HSV (Hue, Saturation, Value) transformation of S2 bands provides a median F1 score of 0.9, outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Additionally, U-Net algorithm was able to learn the relationship between raw S2 based water extraction indices and their corresponding raw S2 bands, but not of HSV owing to relatively complex computation involved in the latter. Results of the paper establishes important benchmarks for the extension of S1 and S2 data-based flood inundation mapping efforts over large spatial extents.
Assimilating remotely sensed surface soil moisture (SSM) into land surface models (LSM) is widely used to improve model representations of soil moisture (SM). However, the efficacy of SSM data ...assimilation (DA) has been found to be limited, particularly in resolving root‐zone soil moisture (RZSM). This study investigates how the representation of vegetation phenology, modulates the efficacy of SSM DA in enhancing the realism of RZSM simulations. To this end, two sets of climatological leaf area index (LAI) are implemented in Noah‐MP LSM over the state of Texas: (a) Noah‐MP default based on long‐term MODIS observations, (b) an alternative LAI adapted from AVHRR products. The former are found to exhibit conspicuous phase errors whereas the latter are more consistent with observed seasonal cycle. Two sets of DA experiments were performed accordingly, wherein SMAP L3 SSM is assimilated into Noah‐MP equipped with each LAI product from 2015 to 2019, Validation of the resulting products against in‐situ data reveals that (a) using the AVHRR‐based LAI, the Noah‐MP outperforms the baseline in reproducing the dynamics of RZSM, and the outperformance is particularly evident over the warm season and water‐stressed western Texas; (b) using the alternative LAI enhances the ability of DA to improve the accuracy of Noah‐MP RZSM, and to a lesser extent, SSM; and (c) gains in SM attained through improvement of LAI and application of DA is most pronounced over regions featuring tight vertical SM coupling. Additional model mechanistic limitations that need to be overcome to improve efficacy of DA are discussed.
Plain Language Summary
Blending remote sensing data into land surface models, known as data assimilation, is a common approach to enhance the accuracy of SM simulation. However, the effectiveness of this approach varies. Vegetation canopy is an important land surface variable that regulates transpiration, soil evaporation, and soil moisture. Errors in its representation can lead to distorted estimates of transpiration and root‐zone soil moisture. This study examines the impact of incorporating improved vegetation growth cycle on the ability of a land surface model to transfer information from the surface to the root‐zone after assimilating remotely sensed soil moisture. Experiments are performed to integrate a satellite‐based surface soil moisture product into a land surface model using both the default and improved vegetation growth cycle. The results show that using the latter improves not only the ability of model to reproduce the dynamics of root‐zone soil moisture, but also enhances the efficacy of data assimilation. The enhancements are the most pronounced in regions with strong vertical coupling of surface and root‐zone soil moisture.
Key Points
Vegetation phenology plays a key role in regulating dynamics of SM, and this role is especially prominent in the water‐stressed regions
Improving climatology of phenology as represented by LAI alone leads to more accurate estimates of RZSM by a land surface model
Improved vegetation phenology enhances the efficacy of DA in improving RZSM and SSM; its impact varies with SM vertical coupling tightness
This study quantifies the contribution of rivers and floodplains to terrestrial water storage (TWS) variability. We use state‐of‐the‐art models to simulate land surface processes and river dynamics ...and to separate TWS into its main components. Based on a proposed impact index, we show that surface water storage (SWS) contributes 8% of TWS variability globally, but that contribution differs widely among climate zones. Changes in SWS are a principal component of TWS variability in the tropics, where major rivers flow over arid regions and at high latitudes. SWS accounts for ~22–27% of TWS variability in both the Amazon and Nile Basins. Changes in SWS are negligible in the Western U.S., Northern Africa, Middle East, and central Asia. Based on comparisons with Gravity Recovery and Climate Experiment‐based TWS, we conclude that accounting for SWS improves simulated TWS in most of South America, Africa, and Southern Asia, confirming that SWS is a key component of TWS variability.
Key Points
SWS contributes to TWS primarily in the tropics and in major rivers flowing over arid regions or at high latitudes
SWS has low impact in Western U.S., Northern Africa, Middle East and central Asia, and most of Australia
Rivers and floodplains store 2,400 km3 with an annual variability of 2,700 km3, contributing to 7% of TWS change globally
Soil moisture performs a key function in the hydrologic process and understanding the global-scale water cycle. However, estimations of soil moisture taken from current sun-synchronous orbit (SSO) ...satellites are limited in that they are neither spatially nor temporally continuous. This limitation creates discontinuous soil moisture observation from space and hampers our understanding of the fundamental processes that control the surface hydrologic cycle across both time and space domains. Here, we propose to use frequent soil moisture observations from NASA’s constellation of eight micro-satellites called the Cyclone Global Navigation Satellite System(CYGNSS) together with the Soil Moisture Active Passive (SMAP) to assimilate subdaily-scale soil moisture intoa land surface model(LSM). Our results, which are based on triple collocation analysis(TCA), show how current scientific advances in satellite systems can fill previous gaps in soil moisture observations in subdaily scale bypast observations, and eventually adds value to improvements in global scale soil moisture estimates in LSMs. Overall, TCA-based fractional mean square errors (fMSE) of LSM soil moisture are improved by 61% with the synergetic assimilation of CYGNSS data with SMAP soil moisture observations. However, assimilating satellite-based soil moisture over dense vegetation areas can degrade the performance of LSMs as these areas propagate erroneous soil moisture information to LSMs. To our knowledge, this study isthe first global assimilation of GNSS-based soil moisture observations in land surface models.
Human and climate induced land surface changes resulting from irrigation, snow cover decreases, and greening impact the surface albedo over High Mountain Asia (HMA). Here we use a partial information ...decomposition approach and remote sensing data to quantify the effects of the changes in leaf area index, soil moisture, and snow cover on the surface albedo in HMA, home to over a billion people, from 2003 to 2020. The study establishes strong evidence of anthropogenic agricultural water use over irrigated lands (e.g., Ganges-Brahmaputra) which causes the highest surface albedo decreases (≤1%/year). Greening and decreased snow cover from warming also drive changes in visible and near-infrared surface albedo in different areas of HMA. The significant role of irrigation and greening in influencing albedo suggests the potential of a positive feedback cycle where albedo decreases lead to increased evaporative demand and increased stress on water resources.
Accurate representation of vegetation states is required for the modeling of terrestrial water–energy–carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation ...changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing–based leaf area index (LAI) retrievals over the continental United States in the Noah-MP land surface model, during a time period of 2000–17. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms, such as soil moisture, evapotranspiration, snow depth, terrestrial water storage, and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of gross primary production (GPP) and net ecosystem exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the United States, where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high-quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.
Irrigation can influence weather and climate, but the magnitude, timing, and spatial extent of irrigation are poorly represented in models, as are the resulting impacts of irrigation on the coupled ...land‐atmosphere system. One way to improve irrigation representation in models is to assimilate soil moisture observations that reflect an irrigation signal to improve model states. Satellite remote sensing is a promising avenue for obtaining these needed observations on a routine basis, but to date, irrigation detection in passive microwave satellites has proven difficult. In this study, results show that the new enhanced soil moisture product from the Soil Moisture Active Passive satellite is able to capture irrigation signals over three semiarid regions in the western United States. This marks an advancement in Earth‐observing satellite skill and the ability to monitor human impacts on the water cycle.
Plain Language Summary
When farmers use irrigation over large areas, it can make the air cooler and more humid, sometimes even changing how clouds form and where rain falls. For this reason, it is important to know where and when irrigation is used, how wet the soil becomes, and how long it stays artificially wet. This information is critical for improving weather models, and therefore forecasts, in the food baskets of the world. However, until now it has been difficult to find accurate and consistent irrigation practice information over time and for large areas. In this paper, we show that a NASA satellite that measures soil moisture routinely across the globe is able to detect wet soil resulting from irrigation in naturally dry environments. This marks an advancement in Earth‐observing satellite skill and improves our ability to monitor and predict human impacts on the water cycle.
Key Points
To date, irrigation detection from passive microwave satellites has proven difficult even over well‐known, expansive regions of agriculture
The new enhanced soil moisture product from the Soil Moisture Active Passive satellite can detect irrigation signals in three regions
Satellite detection of irrigation increases our ability to understand, monitor, and predict human impacts on the water cycle
Earth's vegetation has been increasing over the past decades, altering water and energy cycles by changing evapotranspiration (ET). Greening, caused by climatic and anthropogenic factors, has high ...rates in High Mountain Asia (HMA). Here we focus on two HMA basins (the Yangtze and the Ganges-Brahmaputra) to contrast the impacts of climate- and human-induced greening on ET. Though the rate of greening is similar in both basins, anthropogenic influences lead to dissimilar responses in ET. In the Yangtze, climate-induced greening increases ET, with the increase in moisture being high enough to meet the ET demand. In the Ganges-Brahmaputra, irrigation-induced greening does not alter annual ET, only pre-monsoon ET increases. The dry season declines in water storage due to pumping decrease ET, while laboriously meeting the demand. This study provides a representative example of the contrasting influences of climate induced and anthropogenic driven processes on the seasonality of ET.
•Utility of LiDAR snow depth retrievals and its errors are systematically evaluated.•Modeled SWE and runoff are improved using LiDAR snow depth retrieval assimilation.•A maximum error standard ...deviation of 40 cm is suggested for spaceborne LiDAR.
This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments (OSSEs). The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface model with multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE) estimates and water budget variables such as runoff. Results from the OSSE runs show that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when the LiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm. Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case, this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates via assimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWE root mean square errors and more than a 15% increase in correlation coefficients are achieved when σerror ≤ 40 cm using a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.
This study presents an evaluation of the impact of vegetation conditions on a land surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes ...and states, which are transformed into drought categories using percentiles over the continental United States from 1979 to 2017. Leaf area index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model’s ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation toward improved simulation of agricultural drought.
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