Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France

A global urban microwave backscatter time series data set for 1993-2020 using ERS, QuikSCAT, and ASCAT data

Urban settlements are rapidly growing outward and upward, with consequences for resource use, greenhouse gas emissions, and ecosystem and public health, but rates of change are uneven around the world. Understanding trajectories and predicting consequences of global urban expansion requires quantifying rates of change with consistent, well-calibrated data. Microwave backscatter data provides important information on upward urban growth - essentially the vertical built-up area. We developed a multi-sensor, multi-decadal, gridded (0.05° lat/lon) data set of global urban microwave backscatter, 1993-2020. Comparison of backscatter from two C-band sensors (ERS and ASCAT) and one Ku-band sensor (QuikSCAT) are made at four invariant non-urban sites (~3500 km²) to evaluate instrument stability and multi-decadal pattern. For urban areas, there was a strong linear correlation (overall R² = 0.69) between 2015 ASCAT urban backscatter and a continental-scale gridded product of building volume, across 8450 urban grid cells (0.05° × 0.05°) in Europe, China, and the USA. This urban backscatter data set provides a time series characterizing global urban change over the past three decades.

On the suitability of using vegetation indices to monitor the response of Africa's terrestrial ecoregions to drought

Drought remains one of the world's most devastating phenomena, exhibiting impacts in both magnitude and frequency. African vegetation remains highly vulnerable to drought impacts and this is heightened by a changing climate. In this study, we evaluated the suitability of vegetation indices to monitor the response of Africa's terrestrial ecoregions to drought. Here, we used the SPEI, a global drought index to investigate the spatiotemporal characteristics of drought on vegetation. In addition, TVDI, TCI, VCI, NVSWI, VSWI and DSI, which are remotely sensed derived drought indices were also used to characterize drought. For the vegetation indices, we used the optical satellite calculated NDVI; VOD, a passive microwave remote sensing product; and derived Nvod as proxies for vegetation. The climatology of climate and vegetation data was calculated, and the trend of the variables was examined. Additionally, comparisons were performed between the SPEI and the other drought indices. Subsequently, we computed the correlations between the SPEI and vegetation indices spatially, temporally and seasonally. Our results show that VOD and the NDVI have similar spatial distribution, with higher values of the indices recorded over the Democratic Republic of Congo (DRC) and Central African Republic (C.A.R) compared to the rest of the region. Furthermore, we also found that the indices have similar seasonal patterns as precipitation and an inverse relationship with temperature. The study also reveals that there is a declining long-term trend of precipitation over evergreen needleleaf forest, evergreen broadleaf forest, and woody savanna; and an increasing trend of VOD and NDVI over Africa's ecoregions. Furthermore, the results show a high SPEI - VOD correlations (r² = 0.8) in southern Africa and the Horn of Africa, and a weak response in the Sahelian region. While the response of NDVI is similar to a spatial distribution as VOD, the magnitudes of response are generally weaker in the NDVI, and the magnitudes and distribution of response by Nvod are similar to VOD. Also, the response of Nvod is the weakest across all the timescales although its magnitudes vary significantly from year - year, with the timescale of occurrence mostly shorter for JJA but largely longer for MAM. However, the magnitudes of the response of vegetation indices are different for remotely sensed derived drought indices. In addition, the mean and trend of the response of VOD are consistently stronger in evergreen needleleaf forest and open shrublands but weaker over the evergreen broadleaf forest. Our study has presented insights on methods by which the impacts of droughts on plant activities and functions may be monitored.

Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review

Vegetation is a key element in the energy, water and carbon balances over the land surfaces and is strongly impacted by climate change and anthropogenic effects. Remotely sensed observations are commonly used for the monitoring of vegetation dynamics and its temporal changes from regional to global scales. Among the different indices derived from Earth observation satellites to study the vegetation, the vegetation optical depth (VOD), which is related to the intensity of extinction effects within the vegetation canopy layer in the microwave domain and which can be derived from both passive and active microwave observations, is increasingly used for monitoring a wide range of ecological vegetation variables. Based on different frequency bands used to derive VOD, from L- to Ka-bands, these variables include, among others, the vegetation water content/status and the above ground biomass. In this review, the theoretical bases of VOD estimates for both the passive and active microwave domains are presented and the global long-term VOD products computed from various groups in the world are described. Then, major findings obtained using VOD are reviewed and the perspectives offered by methodological improvements and by new sensors onboard satellite missions recently launched or to be launched in a close future are presented.

From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable.

Editorial for the Special Issue “Soil Moisture Retrieval using Radar Remote Sensing Sensors”

Soil moisture is a key parameter when it comes to understanding the processes related to the water cycle on continental surfaces (infiltration, evapotranspiration, runoff, etc [...]