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Abstract: Optimal deconvolution (ODC) utilizes the footprint overlap in microwave observations to estimate the earth''s brightness temperatures (T B). This paper examines the accuracy of ODC-estimated T B compared with a standard averaging technique. Because brightness temperatures cannot be independently verified, we constructed synthetic True T B for accuracy assessment. We assigned T B at a high spatial resolution (1 km) grid and computed the True T B by spatial averaging of the assigned T B to a lower resolution earth grid (25 km), selected to match the resolution of products generated from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). We used the sensor antenna response function along with the 1-km assigned T B to generate synthetic observations at AMSR-E footprint locations. These synthetic observations were subsequently deconvolved in the ODC technique to estimate T B at the lower resolution earth grid. The ODC-estimated T B and the simple grid cell averages of the synthetic observations were compared with the True T B allowing us to quantify the efficacy of each technique. In areas of high T B contrast (such as boundaries of water bodies), ODC performed significantly better than averaging. In other areas, ODC and averaging techniques produced similar results. A technique similar to ODC can be effective in delineating water bodies with significant clarity. That will allow microwave observations to be utilized near the shorelines, a trouble spot for the currently used averaging techniques. [Copyright &y& Elsevier]

An optimal deconvolution (ODC) technique has been developed to estimate microwave brightness temperatures of agricultural fields using microwave radiometer observations. The technique is applied to airborne measurements taken by the Passive and Active L and S band (PALS) sensor in Iowa during Soil Moisture Experiments in 2002 (SMEX02). Agricultural fields in the study area were predominantly soybeans and corn. The brightness temperatures of corn and soybeans were observed to be significantly different because of large differences in vegetation biomass. PALS observations have significant over-sampling; observations were made about 100 m apart and the sensor footprint extends to about 400 m. Conventionally, observations of this type are averaged to produce smooth spatial data fields of brightness temperatures. However, the conventional approach is in contrast to reality in which the brightness temperatures are in fact strongly dependent on land cover, which is characterized by sharp boundaries. In this study, we mathematically deconvolve the observations into brightness temperature at the field scale (500–800 m) using the sensor antenna response function. The result is more accurate spatial representation of field-scale brightness temperatures, which may in turn lead to more accurate soil moisture retrieval. [Copyright &y& Elsevier]

Understanding the spatial and temporal distribution of hydrologic variables, such as streamflow, is important for sustainable development, especially with global population growth and climate variations. Typical monitoring of streamflow is conducted using in situ gauging stations; however, stations are costly to setup and maintain, leading to data gaps in regions that cannot afford gauges. Satellite data, including altimetry data, are used to supplement in situ observations and in some cases supply information where they are lacking. This study introduces an open-source web application to access and explore altimetry datasets for use in water level monitoring, named the Altimetry Explorer (AltEx). This web application, along with its relevant REST API, facilitates access to altimetry data for analysis, visualization, and impact. The data provided through AltEx is validated using thirteen gauges in the Amazon Basin from 2008 to 2018 with an average Nash-Sutcliffe Coefficient and root mean square error of 0.78 and 1.2 m, respectively. Access to global water level data should be particularly helpful for water resource practitioners and researchers seeking to understand the long-term trends and dynamics of global water level and availability. This work provides an initial framework for a more robust and comprehensive platform to access future altimetry datasets and support research related to global water resources. [ABSTRACT FROM AUTHOR]

Sustainable Development Goal (SDG) no. 15 addresses the protection of terrestrial ecosystems and sustainable forest management, and Target 15.2 encourages countries to sustainably manage forests, and halt deforestation by 2020. SDG indicator 15.1.1 proposes tracking forest area as an indicator for achieving that SDG. Though mangrove forests represent only about 5% of Belize's overall forest cover, the critical ecosystem services they provide are recognized in the country's Forests Act, which regulates the modification of mangrove ecosystems. Preceding the SDGs, from 2008 to 2009, the Government of Belize piloted a complete moratorium on mangrove removal, building on the Forests Act. As Earth Observation (EO) systems provide a means to track effectiveness of Belize's management of its mangrove forests, this paper examines historic and recent changes in mangrove cover across all of Belize, applying statistical adjustments to rates of change derived from Landsat satellite data. Particular attention was paid to the country's only World Heritage Site, the Belize Barrier Reef Reserve System (BBRRS), where mangrove clearing was prohibited since the site's designation in December 1996. The data indicate that within the BBRRS, approximately 89 ha of mangroves were lost from 1996 to 2017, compared to the estimated loss of 2703 ha outside the BBRRS during the same period, and nationwide loss of almost 4100 ha from 1980 to 2017. Thus, compared to the mangroves outside of the BBRRS, the annual rate of mangrove loss within the BBRRS over the period 1996–2017 was merely 4.24 ha per year, versus 129.11 ha per year outside the BBRRS. Furthermore, almost 75% of the 1996–2017 mangrove loss outside the BBRRS were concentrated in three particular geographic zones associated with tourism infrastructure. It was also estimated that Belize's overall mangrove cover declined 5.4% over 36 years, from 76,250 ha in 1980 to 72,169 ha in 2017. In terms of its implications, in addition to contributing to SDG 15, this work also addresses SDG Target 14.2 regarding sustainable management of marine and coastal ecosystems. This study serves as a use case of how EO data can contribute to monitoring changes in baseline data and thus tracking of progress toward SDG Targets. • Mangrove loss rates across Belize were examined for 1980–2017. • Remote sensing data were statistically adjusted. • The Belize Barrier Reef World Heritage Site lost 89 ha. of mangroves over 21 years. • Outside the World Heritage Site, 2703 ha of mangrove were lost. • Belize's overall mangrove area declined by 5.4% from 1980 to 2017. [ABSTRACT FROM AUTHOR]