The influence of vegetation water dynamics on the ASCAT backscatter–incidence angle relationship in the Amazon

Abstract

Abstract. Microwave observations are sensitive to plant water content and could therefore provide essential information on biomass and plant water status in ecological and agricultural applications. The combined data record of the C-band scatterometers on the European Remote-Sensing Satellites (ERS)-1/2, the Metop (Meteorological Operational satellite) series, and the planned Metop Second Generation satellites will span over 40 years, which would provide a long-term perspective on the role of vegetation in the climate system. Recent research has indicated that the unique viewing geometry of the Advanced SCATterometer (ASCAT) could be exploited to observe vegetation water dynamics. The incidence angle dependence of backscatter can be described with a second order polynomial, the slope and curvature of which are related to vegetation. In a study limited to grasslands, seasonal cycles, spatial patterns, and interannual variability in the slope and curvature were found to vary among grassland types and were attributed to differences in moisture availability, growing season length and phenological changes. To exploit ASCAT slope and curvature for global vegetation monitoring, their dynamics over a wider range of vegetation types needs to be quantified and explained in terms of vegetation water dynamics. Here, we compare ASCAT data with meteorological data and GRACE equivalent water thickness (EWT) to explain the dynamics of ASCAT backscatter, slope, and curvature in terms of moisture availability and demand. We consider differences in the seasonal cycle, diurnal differences, and the response to the 2010 and 2015 droughts across ecoregions in the Amazon basin and surroundings. Results show that spatial and temporal patterns in backscatter reflect moisture availability indicated by GRACE EWT. Slope and curvature dynamics vary considerably among the ecoregions. The evergreen forests, often used as a calibration target, exhibit very stable behavior, even under drought conditions. The limited seasonal variation follows changes in the radiation cycle and may indicate phenological changes such as litterfall. In contrast, the diversity of land cover types within the Cerrado region results in considerable heterogeneity in terms of the seasonal cycle and the influence of drought on both slope and curvature. Seasonal flooding in forest and savanna areas also produced a distinctive signature in terms of the backscatter as a function of incidence angle. This improved understanding of the incidence angle behavior of backscatter increases our ability to interpret and make optimal use of the ASCAT data record and vegetation optical depth products for vegetation monitoring.

Sentinel-1 Backscatter Time Series for Characterization of Evapotranspiration Dynamics over Temperate Coniferous Forests

“…For coniferous forest, a high correlation (R 2 ) was achieved between the Sentinel-1 SAR backscatter and the ET products of up to 0.86 (full signal) and up to 0.99 (filtered signal with only the seasonality component). Additionally, an influence of overpass direction on the degree of the correlation with an increase from an R 2 of around 0.5 (descending, morning) to around 0.9 (ascending, evening) was shown, suggesting that the daily change of vegetation water storage due to canopy transpiration is visible, even though not fully decrypted at the moment [70]. Moreover, differences of backscatter variability were shown between dry and wet years, further endorsing this outcome.…”

Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges

“…When backscatter is obtained at multiple incidence angles, one can quantify the relationship between incidence angle and backscatter, which is strongly driven by changes in vegetation, with an increase in vegetation leading to a less steep slope (Wagner et al, 1999b;Naeimi et al, 2009). The slope has been used to monitor phenology and vegetation water dynamics and to retrieve VOD (Vreugdenhil et al, 2017;Steele-Dunne et al, 2019;Petchiappan et al, 2022). In addition, when cross-polarized backscatter is measured, this can be used to quantify vegetation dynamics (Toan et al, 1992;Paloscia et al, 1998;Saatchi et al, 2013;Khabbazan et al, 2019).…”

“…Coarse resolution data from radiometers and scatterometers has mainly been used to monitor drought impact on forests. Studies have demonstrated the potential of vegetation parameters such as VOD to monitor drought impact on vegetation, particularly on forests (Konings et al, 2021), such as the Amazon in 2005, 2010 and 2015 (Saatchi et al, 2013;Liu et al, 2018;Petchiappan et al, 2022), and on tree mortality (Rao et al, 2019). Over croplands and pastures only very few studies have been performed using coarse resolution data, mostly passive microwave-based VOD (Buitink et al, 2020;Afshar et al, 2021;Likith et al, 2022) or the active microwave-based slope from the backscatter incidence angle relationship (Schroeder et al, 2016;Vreugdenhil et al, 2017;Steele-Dunne et al, 2019;Petchiappan et al, 2022).…”

“…During the 2011 and 2012 drought strong negative anomalies in slope were observed, suggesting an impact of drought on the vegetation. Petchiappan et al (2022) also performed an analysis of drought impact on vegetation over the Cerrado regions. The 2015 drought occurred during the wet season in the Cerrado, and most of the agricultural areas showed higher than average slope values for this period.…”

VODCA v2: Multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring

“…We also use the slope of the backscatter incidence angle relation of Metop ASCAT (Vreugdenhil et al, 2016). This radar observable is sensitive to vegetation water content and fresh biomass (Steele-Dunne et al, 2019;Petchiappan et al, 2022). Additionally, we use two ground-based vegetation datasets: Normalized Microwave Reflection Index (NMRI) measurements obtained from GPS reflectometry and sapflow observations from the SAPFLUXNET network.…”