Effects of snow cover and soil moisture on CO2and N2O fluxes from typical semi-arid grassland soil subjected to freeze-thaw cycles

Rainfall is one of the primary geomorphic drivers on Earth&#8217;s surface. How a surface responds to rainfall directly impacts erosional, geomorphic, and natural hazard processes. In the absence of vegetation, whether a land surface retains rainfall as soil moisture or whether rainfall is quickly infiltrated or run off is largely a function of geomorphologic and geologic conditions. In this study, we combine a time series of synthetic aperture radar (SAR) backscatter with daily precipitation to analyze the response of arid and semi-arid land surfaces to rainfall from the event to seasonal scale. The study focuses on northwestern Argentina, where we have extensive field knowledge of local geomorphic features, and is implemented using the cloud computing capacities of Google Earth Engine (GEE).Th Sentinel-1 satellites provide high spatial (10 m) and temporal resolution images of Earth&#8217;s surface, irrespective of cloud cover. We created a 3 year time series from 2018 through 2020 of Sentinel-1 sigma-naught (&#963;0) backscatter from Ground Range Detected (GRD) products available on GEE. Combining the ascending and descending orbits of the Sentinel-1A and -1B satellites into a single time series provides 3 to 6 day temporal resolution in our area of interest. The Global Precipitation Measurement Mission (GPM) was aggregated to daily and monthly precipitation measurements to identify single rainfall events and the seasonal rainfall signal.The response and recovery of SAR backscatter to individual rainfall events across different land surfaces was calculated over 4 to 6 week periods centered on and following a specific rainfall date, respectively. The temporal trend of the backscatter data in these time windows is calculated for every pixel in the backscatter stack to create a map how the surface responds to a large rainfall event. The location of standing water, increased soil moisture, and high infiltration surfaces are detectable in the response maps. The recovery maps provide a proxy for the rate of drying following the rainfall event.In the monsoon-dominated region of northwestern Argentina, both precipitation and SAR backscatter show a clear, periodic seasonal signal over our three-year time series. By aggregating all data to monthly resolution, we can calculate pixel-wise linear regressions and correlation coefficients between precipitation and SAR backscatter. Regressions and correlation analysis are done at the resolution of the Sentinel-1 data and are used to identify whether a surface retains soil moisture, has high infiltration, or experiences seasonal standing water or snow cover. Areas dominated by highly weathered granites and sandstones that can retain soil moisture, for example, have strong positive correlation between rainfall and backscatter due to the increased dielectric constant of wet sediment. In contrast, gravel terraces where rainfall can easily infiltrate the surface show little correlation between backscatter and precipitation. The result is a high resolution map characterizing the propensity for soil moisture retention, high infiltration, and standing water and snow cover. Future work will focus on using these relationships to classify geomorphic surfaces across the arid and semi-arid central Andes.

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Climate Differently Impacts the Growth of Coexisting Trees and Shrubs under Semi-Arid Mediterranean Conditions

Forests ◽

10.3390/f12030381 ◽

2021 ◽

Vol 12 (3) ◽

pp. 381

Author(s):

J. Julio Camarero ◽

Cristina Valeriano ◽

Antonio Gazol ◽

Michele Colangelo ◽

Raúl Sánchez-Salguero

Keyword(s):

Soil Moisture ◽

Radial Growth ◽

Pinus Halepensis ◽

Growth Dynamics ◽

Early Summer ◽

Growth Responses ◽

Shrub Species ◽

High Soil Moisture ◽

Semi Arid ◽

Mediterranean Conditions

Background and Objectives—Coexisting tree and shrub species will have to withstand more arid conditions as temperatures keep rising in the Mediterranean Basin. However, we still lack reliable assessments on how climate and drought affect the radial growth of tree and shrub species at intra- and interannual time scales under semi-arid Mediterranean conditions. Materials and Methods—We investigated the growth responses to climate of four co-occurring gymnosperms inhabiting semi-arid Mediterranean sites in northeastern Spain: two tree species (Aleppo pine, Pinus halepensis Mill.; Spanish juniper, Juniperus thurifera L.) and two shrubs (Phoenicean juniper, Juniperus phoenicea L.; Ephedra nebrodensis Tineo ex Guss.). First, we quantified the intra-annual radial-growth rates of the four species by periodically sampling wood samples during one growing season. Second, we quantified the climate–growth relationships at an interannual scale at two sites with different soil water availability by using dendrochronology. Third, we simulated growth responses to temperature and soil moisture using the forward, process-based Vaganov‒Shashkin (VS-Lite) growth model to disentangle the main climatic drivers of growth. Results—The growth of all species peaked in spring to early summer (May–June). The pine and junipers grew after the dry summer, i.e., they showed a bimodal growth pattern. Prior wet winter conditions leading to high soil moisture before cambium reactivation in spring enhanced the growth of P. halepensis at dry sites, whereas the growth of both junipers and Ephedra depended more on high spring–summer soil moisture. The VS-Lite model identified these different influences of soil moisture on growth in tree and shrub species. Conclusions—Our approach (i) revealed contrasting growth dynamics of co-existing tree and shrub species under semi-arid Mediterranean conditions and (ii) provided novel insights on different responses as a function of growth habits in similar drought-prone regions.

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Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

Remote Sensing ◽

10.3390/rs12162587 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2587

Author(s):

Yan Nie ◽

Ying Tan ◽

Yuqin Deng ◽

Jing Yu

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Arid Regions ◽

Drought Index ◽

Dynamic Monitoring ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Rapid Acquisition ◽

Semi Arid ◽

Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

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