The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992–2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS’ FW compares favorably (R² = 91%–94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS’ inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations.

This letter summarizes substantial modifications made to the Surface Water Microwave Product Series (SWAMPS), a coarse-resolution ($~ $25 km) global inundated area fraction data record derived from active and passive microwave remote sensing. SWAMPS is the most temporally dense, long-term record of global surface water dynamics publicly available today. This update improves upon the original release by: 1) incorporating a customized, consistent resampling and assembly of the Special Sensor Microwave Imager and Special Sensor Microwave Imager Sounder brightness temperature record; 2) eliminating signal contamination from ocean waters along coastlines; 3) inclusion of permanent surface waters as a component of the data record; and 4) reducing anomalous inundation retrievals over arid and semiarid regions. This update provides for the enhanced scientific utility of the full 25+ years of data records. Remaining uncertainties in the surface water fraction retrievals are principally in areas with bare, sandy surface cover and in areas with dense vegetation cover that diminishes radiometric sensitivity to surface water. This data record and associated documentation are freely available through the Alaska Satellite Facility, Fairbanks, AK, USA.

The use of global navigation satellite system reflectometry (GNSS-R) measurements for classification of inundated wetlands is presented. With the launch of NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, space-borne GNSS-R measurements have become available over ocean and land. CYGNSS covers latitudes between ±38°, providing measurements over tropical ecosystems and benefiting new studies of wetland inundation dynamics. The GNSS-R signal over inundated wetlands is driven mainly by coherent scattering associated with the presence of surface water, producing strong forward scattering and a distinctive bistatic scattering signature. This paper presents a methodology used to classify inundation in tropical wetlands using observables derived from GNSS-R measurements and ancillary data. The methodology employs a multiple decision tree randomized (MDTR) algorithm for classification and wetland inundation maps derived from the phased-array L-band synthetic aperture radar (PALSAR-2) as reference for training and validation. The development of an innovative GNSS-R wetland classification methodology is aimed to advance mapping of global wetland distribution and dynamics, which is critical for improved estimates of natural methane production. The results obtained in this manuscript demonstrate the ability of GNSS-R signals to detect inundation under dense vegetation over the Pacaya-Samiria Natural Reserve, a tropical wetland complex located in the Peruvian Amazon. Classification results report an accuracy of 69% for regions of inundated vegetation, 87% for open water regions, and 99% for non-inundated areas. Misclassification of inundated vegetation, primarily as non-inundated area, is likely related to the combination of two factors: partial inundation within the GNSS-R scattering area, and signal attenuation from dense overstory vegetation, resulting in a low signal.

Despite the growing number of remote-sensing products from satellite sensors, mapping of the combined spatial distribution and temporal variability of inundation in tropical wetlands remains challenging. An emerging innovative approach is offered by Global Navigation Satellite System reflectometry (GNSS-R), a concept that takes advantage of GNSS-transmitting satellites and independent radar receivers to provide bistatic radar observations of Earth’s surface with large-scale coverage. The objective of this paper is to assess the capability of spaceborne GNSS reflections to characterize surface inundation dynamics in a complex wetlands environment in the Peruvian Amazon with respect to current state-of-the-art methods. This study examines contemporaneous ALOS2 PALSAR-2 L-band imaging radar, CYGNSS GNSS reflections, and ground measurements to assess associated advantages and challenges to mapping inundation dynamics, particularly in regions under dense tropical forest canopies. Three derivatives of CYGNSS Delay-Doppler maps (1) peak signal-to-noise ratio (SNR), (2) leading edge slope, and (3) trailing edge slope, demonstrated statistically significant logarithmic relationships with estimated flooded area percentages determined from SAR, with SNR exhibiting the strongest association. Aggregated Delay-Doppler maps SNR time series data examined for inundated regions undetected by imaging radar suggests GNSS-R exhibits a potentially greater sensitivity to inundation state beneath dense forest canopies relative to SAR. Results demonstrate the capability for mapping extent and dynamic wetlands ecosystems in complex tropical landscapes, alone or in combination with other remote-sensing techniques such as those based on imaging radar, contributing to enhanced mapping of these regions. However, several aspects of GNSS-R observations such as noise level, spatial resolution, and signal coherence need to be further examined.

Seasonal inundation is an important effect that governs the distribution of ecosystems in the tropics. In the Amazon Basin, the seasonal flood pulse causes a difference in high and low water levels that can exceed 15 m. The associated flood duration and extent play an important role in land-atmosphere carbon exchange and affect the ecosystem’s carbon pool that originates from organic matter transported from upland and flooded forests. Studies of wetlands inundation across the Amazon Basin have utilized dual season mosaics from JERS-1 and wide-swath ScanSAR data from ALOS PALSAR to characterize inundation across the basin. This study builds upon past efforts with JERS-1 and ALOS PALSAR and uses ALOS-2 PALSAR-2 ScanSAR data to generate annual maximum and minimum inundation extent maps over the full Amazon Basin for the period spanning November 2014–October 2017. The study uses decision tree classification to create a maximum and a minimum inundation extent map for each year over this time period. The results show that a generalized algorithm that fits the entire basin has an 86% overall accuracy compared with a classification made for a local region from the same PALSAR-2 datasets. Comparisons with previous full-basin inundation maps by other L-band radars shows similar results for inundated areas during maximum inundation. The maps derived previously from JERS-1 and ALOS PALSAR show 7.3% and 6.9% inundated vegetation, respectively, and this study using PALSAR-2 shows values ranging between 5.5% and 7.0% across the three study years. Comparisons between the stage data across the basin and acquisition dates/periods for JERS-1 and PALSAR-2 show that the sensors capture the nature of the maximum and minimum flooding across the basins but have not successfully captured the exact maximum and minimum flood levels that have been recorded in the stage data. The inundation maps are publicly available under a Creative Commons (CC BY 4.0) licensefrom the Alaska Satellite Facility.

Salicornia rubra is a commonly occurring annual species of the salt playas of the Great Basin Desert of the western United States. In such habitats, plants experience high levels of ultraviolet radiation, which could potentially damage DNA. As a member of the Amaranthaceae (Caryophyllales), S. rubra shoots typically contain high concentrations of the red‐violet pigments called betacyanins, which are ultraviolet‐absorbing compounds. Nevertheless, some specimens of S. rubra are green even when growing with full exposure to the sun. We, therefore, tested several hypotheses regarding the causes of variation among S. rubra plants in betacyanin concentration and the role of betacyanins in the absorption of ultraviolet radiation. We measured ultraviolet radiation absorption and the concentrations of betacyanins and phenolic compounds of the cell sap expressed from red and green plants growing in full sun, as well as plants grown under various levels of shade. We found that while betacyanin concentrations were predictable from plant color (red plants contained more betacyanins than green plants), the ability to absorb ultraviolet radiation was determined primarily by the concentration of phenolic compounds, which was determined by the level of exposure to the sun. Therefore, the DNA of green plants growing in full sun appears to be at no greater risk than the DNA of red plants.