Earth observations and integrative models in support of food and water security.

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.

Advances in the Remote Sensing of Terrestrial Evaporation.

Characterizing the terrestrial carbon, water and energy cycles depends strongly on a capacity to accurately reproduce the spatial and temporal dynamics of land surface evaporation. For this, and many other reasons, monitoring terrestrial evaporation across multiple space and time scales has been an area of focused research for many decades. Much of this activity has been supported by developments in satellite remote sensing, which have been leveraged to deliver new process insights, model development and methodological improvements. In this Special Issue, published contributions explored a range of research topics directed towards the enhanced estimation of terrestrial evaporation. Here we summarize these cutting-edge efforts and provide an overview of some of the state-of-the-art approaches for retrieving this key variable. Some perspectives on outstanding challenges, issues, and opportunities are also presented.

Assessment of the impact of spatial heterogeneity on microwave satellite soil moisture periodic error.

An accurate temporal and spatial characterization of errors is required for the efficient processing, evaluation, and assimilation of remotely-sensed surface soil moisture retrievals. However, empirical evidence exists that passive microwave soil moisture retrievals are prone to periodic artifacts which may complicate their application in data assimilation systems (which commonly treat observational errors as being temporally white). In this paper, the link between such temporally-periodic errors and spatial land surface heterogeneity is examined. Both the synthetic experiment and site-specified cases reveal that, when combined with strong spatial heterogeneity, temporal periodicity in satellite sampling patterns (associated with exact repeat intervals of the polar-orbiting satellites) can lead to spurious high frequency spectral peaks in soil moisture retrievals. In addition, the global distribution of the most prominent and consistent 8-day spectral peak in the Advanced Microwave Scanning Radiometer - Earth Observing System soil moisture retrievals is revealed via a peak detection method. Three spatial heterogeneity indicators - based on microwave brightness temperature, land cover types, and long-term averaged vegetation index - are proposed to characterize the degree to which the variability of land surface is capable of inducing periodic error into satellite-based soil moisture retrievals. Regions demonstrating 8-day periodic errors are generally consistent with those exhibiting relatively higher heterogeneity indicators. This implies a causal relationship between spatial land surface heterogeneity and temporal periodic error in remotely-sensed surface soil moisture retrievals.

Global Investigation of Soil Moisture and Latent Heat Flux Coupling Strength.

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. (2015) 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 datasets, 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) over-predict 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 re-examination 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.