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This study attempts to derive the uncertainty of the soil moisture estimation from passive microwave satellite mission at global scale. To do so, the approach is based on the sensitivity of the Soil Moisture and Ocean Salinity (SMOS) soil moisture retrieval quality to the land surface characteristics within its footprint (presence of forest, topography, open water bodies, sand, clay, bulk density and soil organic carbon content). First, we performed a global assessment of SMOS using in situmeasurements from the International Soil Moisture Network (ISMN) as reference, with more than 1900 ISMN stations and 10 years of SMOS data. This assessment shows that the ubRMSD scores vary greatly between locations (with a mean of 0.074 m3m−3and an interquartile range of 0.030 m3m−3). Second, the scores are analyzed for different surface conditions within the satellite footprint. The best agreement between the ground measurement and SMOS time series are obtained for low forest cover, low topographic complexity, and marginal presence of open water bodies within the SMOS footprint. Soil parameters also have an impact, with better scores for sandier soils with a high bulk-density and low soil organic carbon content. Finally, we propose to extrapolate the obtained relationships, using a multiple linear regression, in order to derive a global map of SMOS uncertainties based on surface conditions. This map of predicted uncertainties show a diverse range of ubRMSD values across the globe (with a mean of 0.076 m3m−3and an interquartile range of 0.031 m3m−3) depending on the surface characteristics. At the ISMN site location, the predicted ubRMSD shows similar results than the comparison between SMOS and the in situmeasurements. The map of predicted SMOS ubRMSD represents an upper bound estimate of the SMOS uncertainty, as it includes the uncertainties of the in situsensor measurements and the scale mismatch. Further investigations will focus on the different components of this uncertainty budget to obtain a better assessment of the absolute uncertainties of SMOS soil moisture retrievals across the globe.

Changes in terrestrial tropical carbon stocks have an important role in the global carbon budget. However, current observational tools do not allow accurate and large-scale monitoring of the spatial distribution and dynamics of carbon stocks1. Here, we used low-frequency L-band passive microwave observations to compute a direct and spatially explicit quantification of annual aboveground carbon (AGC) fluxes and show that the tropical net AGC budget was approximately in balance during 2010 to 2017, the net budget being composed of gross losses of −2.86 PgC yr−1offset by gross gains of −2.97 PgC yr−1between continents. Large interannual and spatial fluctuations of tropical AGC were quantified during the wet 2011 La Niña year and throughout the extreme dry and warm 2015–2016 El Niño episode. These interannual fluctuations, controlled predominantly by semiarid biomes, were shown to be closely related to independent global atmospheric CO2growth-rate anomalies (Pearson’s r= 0.86), highlighting the pivotal role of tropical AGC in the global carbon budget. Tropical carbon stocks are essential for proper accounting of global carbon budgets, but difficult to monitor on a large scale. L-band microwave observations used here enable direct and spatially explicit accounting of annual carbon fluxes from different types of land surface.

Precision irrigation management and crop water stress assessment rely on accurate estimation of root zone soil moisture. However, only the top 5 cm soil moisture can be estimated using the two current passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), which operate at L-band (wavelength of ∼21 cm). Since the contributing depth of the soil to brightness temperature increases with observation wavelength, it is expected that a P-band (wavelength of ∼40 cm) radiometer could potentially provide soil moisture information from deeper layers of the soil profile. Moreover, by combining both L- and P- bands, it is hypothesized that the soil moisture profile can be estimated even beyond their individual observation depths. The aim of this study was to demonstrate the potential of combined L-band and P-band radiometer observations to estimate the soil moisture profile under flat bare soil using a stratified coherent forward model. Brightness temperature observations at L-band and P-band from a tower based experimental site across a dry (April 2019) and a wet (March 2020) period, covering different soil moisture profile shapes, were used in this study. Results from an initial synthetic study showed that the performance of a combined L-band and P-band approach was better than the performance of using either band individually, with an average depth over which reliable soil moisture profile information could be estimated (i.e. with a target root mean square error (RMSE) of less than 0.04 m3/m3) being 20 cm for linear and 15 cm for second-order polynomial functions. Other functions were also tested but found to have a poorer performance. Applying the method to the tower-based brightness temperature achieved an average estimation depth of 28 cm (20 cm) and 5 cm (5 cm) during the dry and wet periods respectively when using a second-order polynomial (linear) function. These findings highlight the opportunity of a satellite mission with L-band and P-band observations to accurately estimate the soil moisture profile to as deep as 30 cm globally.

Soil moisture is an essential climate variable influencing land–atmosphere interactions, an essential hydrologic variable impacting rainfall–runoff processes, an essential ecological variable regulating net ecosystem exchange, and an essential agricultural variable constraining food security. Large‐scale soil moisture monitoring has advanced in recent years, creating opportunities to transform scientific understanding of soil moisture and related processes. These advances are being driven by researchers from a broad range of disciplines, but this complicates collaboration and communication; and, for some applications, the science required to utilize large‐scale soil moisture data is poorly developed. In this review, we describe the state of the art in large‐scale soil moisture monitoring and identify some critical needs for research to optimize the use of increasingly available soil moisture data. We review representative examples of (i) emerging in situ and proximal sensing techniques, (ii) dedicated soil moisture remote sensing missions, (iii) soil moisture monitoring networks, and (iv) applications of large‐scale soil moisture measurements. Significant near‐term progress seems possible in the use of large‐scale soil moisture data for drought monitoring. Assimilation of soil moisture data for meteorological or hydrologic forecasting also shows promise, but significant challenges related to spatial variability and model structures remain. Little progress has been made in the use of large‐scale soil moisture observations within the context of ecological or agricultural modeling. Opportunities abound to advance the science and practice of large‐scale soil moisture monitoring for the sake of improved Earth system monitoring, modeling, and forecasting.

Two methods for retrieving sensible heat flux over a bare soil/wheat composite surface are compared. The first one is based on field measurements using large aperture scintillometers. The comparison with reference fluxes obtained from eddy correlation technique shows that scintillometry-derived fluxes are overestimated by 10%. A numerical experiment demonstrates this is induced by the non-uniform sensitivity of the scintillometer to C$_{\rm N}^2$ along the path length which follows a `bell-shaped' curve. The second method is based on the use of a simple surface energy balance model, SEBAL, supplied with high spatial resolution remote sensing data from two airborne sensors in visible, near infrared and TIR bands. A comparison with scintillometry-derived fluxes shows important discrepancies. These result from large errors in the estimation of the roughness length z$_0$ in the model. This demonstrates that the use of an empirical relationship based on NDVI only is inadequate for inferring this key parameter in SEBAL.

The atmospheric surface layer couples processes in the atmosphere with those at the land surface, but is not fully understood for heterogeneous land surfaces. A small remotely piloted aircraft was flown during the ReSeDA experiment to measure profiles of heat and humidity in the surface layer near boundaries between different land cover types to study atmospheric structure over heterogeneous terrain. The aircraft payload included sensors for temperature, humidity and position. Extensive data processing has been performed to check data quality and coordinate the datasets created (to common time and position references). Initial results show the expected general features of the surface layer profiles of temperature and humidity and also give details of structure near field boundaries, especially where there is strong contrast in surface properties and radiative forcing. The experiments also demonstrate that unmanned aircraft are practical tools for this form of atmospheric research.

Aerodynamic parameters (displacement height $d$ and roughness length for momentum $z_0$), as well as friction velocity $u_*$ and temperature scaling parameter $T_*$, were estimated from the Alpilles energy balance measurement data. Air temperature and wind speed measurements at two heights and sensible heat flux data were needed in the estimation. Application of this method to two data sets measured in different ways for the temperature gradients found that the estimation of $d$ and $z_0$ was applicable only when the temperature gradients were measured differentially. Sensitivity tests showed that the estimated $d$ and $z_0$ were sensitive to the temperature gradients. However, both of the data sets produced good estimation of $u_*$ and $T_*$. The main advantage of this method is that it is applicable not only under near-neutral conditions but also under strong unstable conditions.

A physical algorithm is developed to solve the radiative transfer problem in the solar reflective spectral domain. This new code, Advanced Modeling of the Atmospheric Radiative Transfer for Inhomogeneous Surfaces (AMARTIS), takes into account the relief, the spatial heterogeneity, and the bidirectional reflectances of ground surfaces. The resolution method consists of first identifying the irradiance and radiance components at ground and sensor levels and then modeling these components separately, the rationale being to find the optimal trade off between accuracy and computation times. The validity of the various assumptions introduced in the AMARTIS model are checked through comparisons with a reference Monte Carlo radiative transfer code for various ground scenes: flat ground with two surface types, a linear sand dune landscape, and an extreme mountainous configuration. The results show a divergence of less than 2% between the AMARTIS code and the Monte Carlo reference code for the total signals received at satellite level. In particular, it is demonstrated that the environmental and topographic effects are properly assessed by the AMARTIS model even for situations in which the effects become dominant.

An algorithm based on the Monte Carlo method is developed to solve the radiative transfer equation in the reflective domain (0.4–4 µm) of the solar spectrum over rugged terrain. This algorithm takes into account relief, spatial heterogeneity, and ground bidirectional reflectance. The method permits the computation of irradiance components at ground level and radiance terms reaching an airborne or satelliteborne sensor. The Monte Carlo method consists of statistically simulating the paths of photons inside the Earth–atmosphere system to reproduce physical phenomena while introducing neither analytical modeling nor assumption. The potentialities of the code are then depicted over different types of landscape, including a seashore, a desert region, and a steep mountainous valley.

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) Global Vegetation Indices (GVI) were used during the 1986 rainy season (June-September) over Senegal to monitor rainfall. The satellite data were used in conjunction with ground-based measurements so as to derive empirical relationships between rainfall and GVI. The regression obtained was then used to map the total rainfall corresponding to the growing season, yielding good results. Normalized Difference Vegetation Indices (NDVI) derived from High Resolution Picture Transmission (HRJT) data were also compared with actual evapotranspiration (ET) data and proved to be closely correlated with it with a time lapse of 20 days.