Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment exhibit a well-known summertime warm bias in mid-latitude land regions - most notably in the ...central contiguous United States (CUS). The dominant source of this bias is still under debate. Using validated datasets and both coupled and off-line modeling, we find that the CUS summertime warm bias is driven by the incorrect partitioning of evapotranspiration (ET) into its canopy transpiration and soil evaporation components. Specifically, CMIP6 ESMs do not effectively use available rootzone soil moisture for summertime transpiration and instead rely excessively on shallow soil and canopy-intercepted water storage to supply ET. As such, expected summertime precipitation deficits in CUS induce a negative ET bias into CMIP6 ESMs and a corresponding positive temperature bias via local land-atmosphere coupling. This tendency potentially biases CMIP6 projections of regional water stress and summertime air temperature variability under elevated CO
conditions.
Soil moisture (SM) derived from satellite-based remote sensing measurements plays a vital role for understanding Earth’s land and near-surface atmosphere interactions. Bistatic Global Navigation ...Satellite System (GNSS) Reflectometry (GNSS-R) has emerged in recent years as a new domain of microwave remote sensing with great potential for SM retrievals, particularly at high spatio-temporal resolutions. In this work, a machine learning (ML)-based framework is presented for obtaining SM data products over the International Soil Moisture Network (ISMN) sites in the Continental United States (CONUS) by leveraging spaceborne GNSS-R observations provided by NASA’s Cyclone GNSS (CYGNSS) constellation alongside remotely sensed geophysical data products. Three widely-used ML approaches—artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—are compared and analyzed for the SM retrieval through utilizing multiple validation strategies. Specifically, using a 5-fold cross-validation method, overall RMSE values of 0.052, 0.061, and 0.065 cm3/cm3 are achieved for the RF, ANN, and SVM techniques, respectively. In addition, both a site-independent and a year-based validation techniques demonstrate satisfactory accuracy of the proposed ML model, suggesting that this SM approach can be generalized in space and time domains. Moreover, the achieved accuracy can be further improved when the model is trained and tested over individual SM networks as opposed to combining all available SM networks. Additionally, factors including soil type and land cover are analyzed with respect to their impacts on the accuracy of SM retrievals. Overall, the results demonstrated here indicate that the proposed technique can confidently provide SM estimates over lightly-vegetated areas with vegetation water content (VWC) less than 5 kg/m2 and relatively low spatial heterogeneity.
The Soil Moisture Active Passive (SMAP) Level-4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, an unbiased ...qualitative estimate of the relative skill of SMAP Level-4 and model-only surface soil moisture (versus true soil moisture) is derived using only one additional noisy (but independent) soil moisture product. The method is applied globally and verified using high-quality, ground-based measurements where available. Results demonstrate that assimilating SMAP brightness temperature has relatively little impact in data-rich areas like the United States and Europe. In contrast, much larger improvement is observed in data-sparse regions, including much of Africa and central Australia, where model-only simulations are disproportionately impacted by low-quality model forcing. Therefore, ground validation conducted in data-rich areas does not adequately sample the added value of SMAP data assimilation for data-sparse regions and substantially underestimates the added skill provided by the SMAP Level-4 system.
This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high ...spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.
Model‐based estimates of soil moisture (SM)‐evapotranspiration (ET) coupling strength(ρ) vary widely and are prone to bias. Here we apply numerical modeling and remote sensing to identify the ...process‐level source of modeledρbias with the goal of improving the fidelity of current Earth system models. Results illustrate that modeledρis most strongly determined by soil evaporation (E) stress, and (generally positive)ρmodeling bias is attributable to the oversimplification of soil texture impacts on E stress. Based on new remotely sensed estimates ofρ, we demonstrate that removingρbias via a single optimized E stress parameter leads to improved ET accuracy and resolves a well‐known modeling bias in the partitioning of ET into E and T. As such, we highlight the importance of the stress function relating E and SM and its central role in regulating land‐atmosphere coupling processes impacting local climate.
SMAP satellite has provided us the first 9-km global soil moisture (SM) product, which is retrieved from the combined L-band radiometer and radar observations with a balance between accuracy and ...resolution. However, SMAP's radar failed on July 7, 2015, making the continuous production of the 9-km SM impossible, which has considerably affected the application of SMAP in hydrological monitoring. This study was aimed at extending the SMAP 9-km SM by developing a non-local filter based spatio-temporal fusion model (STFM). With the auxiliary of the historical 9-kmand 36-km products, the STFM was used to downscale the daily 36-km product to 9-km. Two-year 9-km product was estimated using STFM in the study. It shows that the estimated product has the detailed information retention of 9-km product and the comparable accuracy with 36-km product, which makes it feasible to improve the application potential of the current SMAP SM products.
•A STFM is presented to extend the SMAP 9-km soil moisture product.•A two-year period of the 9-km soil moisture product is estimated by the STFM.•The estimated 9-km soil moisture product has a comparable accuracy to P36.
As a key variable in the climate system, soil moisture (SM) plays a central role in the Earth's terrestrial water, energy, and biogeochemical cycles through its coupling with surface latent heat flux ...(LH). Despite the need to accurately represent SM/LH coupling in Earth system models, we currently lack quantitative, observation‐based, and unbiased estimates of its strength. Here we utilize the triple‐collocation (TC) approach introduced in Crow et al. () to SM and LH products obtained from multiple satellite remote sensing platforms and land surface models (LSMs) to obtain unbiased global maps of SM/LH coupling strength. Results demonstrate that relative to coupling strength estimates acquired directly from remote sensing‐based data sets, the application of TC generally enhances estimates of warm‐season SM/LH coupling, especially in the western United States, the Sahel, central Asia, and Australia. However, relative to triple‐collocation estimates, LSMs (still) overpredict SM/LH coupling strength along transitional climate regimes between wet and dry climates, such as the central Great Plains of North America, India, and coastal Australia. Specific climate zones with biased relations in LSMs are identified to geographically focus the reexamination of LSM parameterizations. TC‐based coupling strength estimates are robust to our choice of LSM contributing SM and LH products to the TC analysis. Given their robustness, TC‐based coupling strength estimates can serve as an objective benchmark for investigating model‐predicted SM/LH coupling.
Plain Language Summary
Physical models describing land‐atmosphere coupling have been developed to help better understand the impact of local‐, regional‐, and global‐scale climate on weather and the water cycle. However, verifying the accuracy of these models is challenging over sparsely instrumented areas. Here the strength of land‐atmosphere coupling between soil moisture and terrestrial evapotranspiration is examined by combining multiple global‐scale remote sensing and modeling products into a unified analysis. This analysis is unique in that it can be conducted globally and is unbiased by the presence of random errors in the remote sensing products. As such it provides the first robust estimate of the degree to which soil moisture and evapotranspiration are linked. Results show strong soil moisture/evapotranspiration coupling over the western United States, the African Sahel, central Asia, and Australia. However, they also demonstrate that most existing models are still overpredicting this coupling along transitional regions between wet and dry climates (like the Central Great Plains of North America, India, and coastal Australia). This work will help improve the representation of land‐atmosphere coupling in models used to obtain future climate projections.
Key Points
Multiple sources of remote sensing‐based soil moisture and latent heat flux products are integrated via triple collocation
Global observation‐based estimates of coupling strength between surface soil moisture and latent heat flux are obtained
Land surface models overestimate the strength of this coupling along transitional climate regimes
Downscaled microwave soil moisture (SM) products with a fine resolution are of great importance for both local and regional studies. However, few studies have explored the merits of multiple ...downscaled microwave SM products. An evaluation of the different products could help to advance knowledge of the downscaled microwave SM products and help researchers to choose the appropriate downscaled SM products for use in further studies. In this research, five microwave SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), AMSR2, and Soil Moisture and Ocean Salinity (SMOS) data were downscaled via the back-propagation neural network (BPNN). The BPNN was chosen because it can effectively simulate the nonlinear relationship between SM and the land surface temperature (LST)/vegetation index (VI). The different downscaled SM products were evaluated with in-situ SM data from the central Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN) during the period from 1 August 2010 to 31 December 2012. Compared with the regression technique, the downscaled correlation coefficient (r) is significantly improved by the BPNN. The downscaled root-mean-square error (RMSE) and bias are comparable for the two techniques. As expected, LST and enhanced VI (EVI) are physically related to SM, and this is the most suitable combination for SM downscaling. Except for the ascending node of SMOS and AMSR2, the downscaled r is closely related to the original RMSE, and a lower original RMSE for the SM product results in a higher downscaled r. The BPNN-downscaled SMOS product in descending node is the closest to the in-situ SM among the different downscaled microwave SM products. The temporal variations and ranges of the microwave SM products are well maintained by the BPNN downscaling. Furthermore, the evaluations against in-situ SM reveal that the overall accuracies of the BPNN-downscaled SM products are very close to the original microwave SM products.
A high spatial and temporal resolution global soil moisture product is essential for understanding hydrologic and meteorological processes and enhancing agricultural applications. Global navigation ...satellite system (GNSS) signals at L-band frequencies that reflect off the land surface can convey high-resolution land surface information, including surface soil moisture (SM). Cyclone global navigation satellite system (CYGNSS) constellation generates Delay-Doppler Maps (DDMs) that contain important Earth surface information from GNSS reflection measurements. DDMs are affected by soil moisture and other factors such as complex topography, soil texture, and overlying vegetation. Including entire DDM information can help reduce the uncertainty of SM estimation under different conditions along with remotely sensed geophysical data. This work extends our previously developed deep learning (DL) framework to a global scale by utilizing processed DDM measurements (analog power, effective scattering area, and bistatic radar cross-section) and ancillary data (elevation, slope, water percentage, soil properties, and vegetation water content). The DL model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at 9-km resolution. This study comprehensively evaluates the DL model against publicly available CYGNSS-based SM products at a quasi-global scale. In addition to the typical comparison against in-situ measurements, a robust triple collocation technique is used to evaluate the DL-based SM product and other CYGNSS-derived SM products.
Particularly in light of California’s recent multiyear drought, there is a critical need for accurate and timely evapotranspiration (ET) and crop stress information to ensure long-term sustainability ...of high-value crops. Providing this information requires the development of tools applicable across the continuum from subfield scales to improve water management within individual fields up to watershed and regional scales to assess water resources at county and state levels. High-value perennial crops (vineyards and orchards) are major water users, and growers will need better tools to improve water-use efficiency to remain economically viable and sustainable during periods of prolonged drought. To develop these tools, government, university, and industry partners are evaluating a multiscale remote sensing–based modeling system for application over vineyards. During the 2013–17 growing seasons, the Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project has collected micrometeorological and biophysical data within adjacent pinot noir vineyards in the Central Valley of California. Additionally, each year ground, airborne, and satellite remote sensing data were collected during intensive observation periods (IOPs) representing different vine phenological stages. An overview of the measurements and some initial results regarding the impact of vine canopy architecture on modeling ET and plant stress are presented here. Refinements to the ET modeling system based on GRAPEX are being implemented initially at the field scale for validation and then will be integrated into the regional modeling toolkit for large area assessment.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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