Soil moisture (SM) is crucial in land-atmosphere (LA) interactions, regulating evapotranspiration and influencing moisture and energy budgets, which impact weather, precipitation, and the broader climate system. Despite efforts to quantify LA coupling using observational data, reanalysis, or combined observation-model products, challenges remain due to inconsistencies and discrepancies among datasets. This highlights the need for observational data to assess model performance, diagnose errors in model structures, and potentially improve their physical process. Flux tower sites, while valuable for in-situ observations, have limited global distribution and short durations. Conversely, satellite data offer high-quality, long-term, and globally distributed observations but are prone to random errors, which can degrade estimates of LA coupling.     As a result, there is an incomplete picture of the reality of global LA coupling for model validation and calibration. This study introduces a method for deriving the Pearson correlation coefficient between SM time series from the Soil Moisture Active Passive (SMAP) satellite and observation-based latent heat flux (LE) products from the Global Land Evaporation Amsterdam Model (GLEAM), FluxCom, and Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration (CAMELE). While SMAP data correlate closely with in-situ SM measurements, they exhibit stochastic random errors, which can reduce the accuracy of SM-LE correlation. On the other hand, SM variability typically resembles a first-order Markov process, enabling the estimation of the ratio between variance of random error and temporal variance in SM measurements. Given that the sequence of random error variances is unknown, it is not feasible to derive an error-free time series by eliminating random errors. As a solution, we propose a mathematical methodology to estimate the corrected correlation, considering random errors in satellite-based SM data. The accuracy of corrected correlations will be further evaluated using in-situ measurements (AmeriFlux network). We aim to construct a global depiction of SM-LE correlation from observationally gridded data with a quantification of uncertainties and potentially establish a new benchmark for model validation.

Soil moisture (SM) analyses and assessments hold significance for numerous applications in the fields of hydrometeorology and agriculture. Throughout history, flux tower sites have been a primary source of data for observationally-based SM examinations and evaluations of landatmosphere interaction. However, these monitoring stations are not evenly distributed worldwide. One of the ways in which the comprehensive understanding of how land and atmosphere interact can be improved is by incorporating remotely sensed SM observations. The Soil Moisture Active Passive (SMAP) satellite is one of the satellite resources which closely aligns with in-site observations. However, the remote sensing nature of SMAP data means that it is prone to unpredictable random distortions. Since variations in SM tend to follow a fundamental Markov process, they typically display a specific "red noise" pattern of variability. On the other hand, satellite data that incorporates random fluctuations exhibits a more uniform "white noise" pattern at higher frequencies, which contrasts with the anticipated red noise pattern. Furthermore, gaps in SMAP data are not randomly distributed; due to its orbital characteristics, the satellite experiences regular instances of missing data during its 8-day orbital cycle, differing depending on the orbital pass. This introduces additional anomalies in the power spectrum, performed through examining correlations in the time series data, leading to recurring spikes at intervals of 8, 4 (half of 8), 2 and 2/3 (one-third of 8), and 2 days (one-fourth of 8). These spectral spikes become broader due to small variations in the satellite's orbit. To make the satellite data most effective for assessing land-atmosphere interactions, which tend to rely on estimates of covariability of SM with other environmental variables, it is crucial to minimize the impact of random distortions and systematic missing data. A technique for adjusting the power spectrum, and thus the time series, of SM has been developed to minimize the influence of orbital harmonic spikes in the gridded Level 3 (L3) SMAP dataset. This is achieved by fitting a catenary function to the power spectrum between the harmonic spikes and then removing their influence. The adjusted spectrum is then aligned with soil moisture data from the surface layer, collected from sites within the AmeriFlux network (in-situ flux tower data). These sites demonstrate relatively minimal distortion and exhibit SM power spectra that closely resemble those generated by offline land surface models (LSMs), which are free of random noise by nature. Using validated spectral data from gridded LSM-based datasets, an improved global L3 SMAP dataset is being generated that accounts for noise and harmonic effects. This presentation will showcase the outcomes of this technique in enhancing SMAP data and its temporal correspondence with observational data.

Land-atmosphere feedbacks act through process chains that link variables in the land-atmosphere system. For the global energy and water cycles, the first link in the chain is soil moisture. Flux tower sites provide in-situ observations, including land surface states, surface fluxes, and nearsurface atmospheric states, to validate these links; however, they are unevenly distributed over the globe. Therefore, to obtain a global view of observationally based land-atmosphere coupling metrics, satellite data are useful. Among satellite products, the Soil Moisture Active Passive (SMAP) satellite provides the closest match to in-situ observations. However, SMAP exhibits stochastic random noise that can deflate coupling estimates. Since soil moisture variability closely follows a first-order Markov process, it typically has a distinct red noise spectrum. Satellite data with random noise has a whiter spectrum at high frequencies that can be compared to the expected red spectrum. Also, missing data in SMAP are not entirely random; its 8-day repeating polar orbit creates a cadence of missing data for both ascending and descending overpasses, depending on the location. This creates additional artifacts in the power spectrum, calculated through lagged autocovariance in the time series, with harmonic spikes at 8, 4 (8/2), 2 2/3 (8/3), and 2 (8/4) days that broaden due to the satellite's orbital variations. To be optimally useful for quantifying land-atmosphere feedbacks, the effects of random noise and periodic missing data must be minimized. A power spectrum adjustment technique has been designed to remove the orbital harmonic spikes from Level 3 (L3) SMAP data. This is achieved by fitting and removing a catenary function to the power spectrum between harmonic spikes. This adjusted spectrum is then scaled to match surface layer soil moisture observations at sites of the AmeriFlux network (in-situ data), which exhibit relatively low noise and have spectra that are very similar to those produced by offline land surface models (LSMs). Utilizing validated spectral data from gridded LSM-based datasets, a global L3 SMAP product with removed noise and harmonic effects is being produced. We will present results quantifying the extent to which this technique improves SMAP data and its temporal correlation with observations.