Exploring the relationship between phenology and hydrology using Sentinel-2 and terrestrial photography in Mediterranean grasslands

2020 ◽  
Author(s):  
Pedro Jesús Gómez-Giráldez ◽  
María José Pérez-Palazón ◽  
María José Polo ◽  
María Patrocinio González-Dugo
Keyword(s):  
Soil Moisture ◽  
Rural Economy ◽  
Primary Source ◽  
Logistic Function ◽  
Dry Season ◽  
Water Dynamics ◽  
Close Link ◽  
Mediterranean Grasslands ◽  
Phenological Dates ◽  
Sentinel 2

<p>Mediterranean grasslands are an essential component of rural economy as the primary source of fodder for livestock in extensive areas. These annual grasslands present an escape mechanism to cope with the long summer dry season and the recurrent water scarcity events of the Mediterranean climate, completing their life cycle before serious soil and plant water deficits develop. It results in a close link between grass phenology and soil water dynamics. In this work we have explored this relationship using satellite and ground remote sensing (Sentinel-2 (S2) and a terrestrial digital camera) and ground measurements of hydrological variables.</p><p>The terrestrial photography was used as a field validator, grass greenness was assessed using the Green Chromatic Coordinate Index (GCC) and key phenological dates were extracted from the variation of this index during a calibration period (December 2017 to May 2019). The evolution of GCC index was highly correlated with soil moisture (SM) dynamic, which is consistent with the water-limited condition of the ecosystem. Some other variables, including vapor pressure deficit, solar radiation, and minimum, medium and maximum air temperatures were inversely correlated with greenness. Rainfall, although positively correlated, presented the lowest coefficient of all analyzed variables. The capability of SM and S2-NDVI to predict the phenology of the grass canopy was assessed by fitting a double-logistic function to the variables time-series and extracting the phenological parameters start of season (SOS), peak of season (POS) and end of season (EOS) using the 50% amplitude method. The comparison with the terrestrial camera resulted in differences less than 10 days for all phenological dates parameters studied (representing less than 5% error within a grass cycle). The behavior of S2-NDVI and SM relationship during four growing seasons was analyzed. It pointed out the synchronized seasonality shown in this system by the vegetation greenness, measured here by the NDVI, and the soil moisture. The higher agreement was found at the beginning and the end of the dry season, with stage changes estimated first by SM, followed by NDVI with a delay between 3 to 10 days. These results highlight the close relationship between these phenological parameters and the soil moisture dynamic under the study conditions, and the capability of satellite data to track these parameters.</p>

scholarly journals Monitoring Grass Phenology and Hydrological Dynamics of an Oak–Grass Savanna Ecosystem Using Sentinel-2 and Terrestrial Photography

2020 ◽  
Vol 12 (4) ◽  
pp. 600 ◽  
Author(s):  
Pedro J. Gómez-Giráldez ◽  
María J. Pérez-Palazón ◽  
María J. Polo ◽  
María P. González-Dugo
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Primary Source ◽  
Water Dynamics ◽  
Close Link ◽  
Abiotic Variables ◽  
Sentinel 2

Annual grasslands are an essential component of oak savanna ecosystems as the primary source of fodder for livestock and wildlife. Drought resistance adaptation has led them to complete their life cycle before serious soil and plant water deficits develop, resulting in a close link between grass phenology and soil water dynamics. In this work, these links were explored using a combination of terrestrial photography, satellite imagery and hydrological ground measurements. We obtained key phenological parameters of the grass cycle from terrestrial camera data using the Green Chromatic Coordinate (GCCc) index. These parameters were compared with those provided by time-series of vegetation indices (VI) obtained from Sentinel-2 (S2) satellites and time-series of abiotic variables, which defined the hydrology of the system. The results showed that the phenological parameters estimated by the S2 Normalized Difference Vegetation Index (NDVI) (r = 0.83, p < 0.001) and soil moisture (SM) (r = 0.75, p < 0.001) presented the best agreement with ground-derived observations compared to those provided by other vegetation indices and abiotic variables. The study of NDVI and SM dynamics, that was extended over four growing seasons (July 2015–May 2019), showed that the seasonality of both variables was highly synchronized, with the best agreements at the beginning and at the end of the dry seasons. However, stage changes were estimated first by SM, followed by NDVI, with a delay of between 3 and 10 days. These results support the use of a multi-approach method to monitor the phenology and the influence of the soil moisture dynamic under the study conditions.

Monitoring Mediterranean grass phenology from digital terrestrial camera and Sentinel-2 vegetation indices in an oak-grass savanna ecosystem

2020 ◽  
Author(s):  
Maria P. González-Dugo ◽  
Pedro J. Gómez-Giraldez ◽  
María J. Pérez-Palazón ◽  
María J. Polo
Keyword(s):  
Vegetation Indices ◽  
Broad Band ◽  
Primary Source ◽  
Hydrological Regime ◽  
Agroforestry System ◽  
Red Edge ◽  
Annual Grasslands ◽  
The Difference ◽  
Phenological Dates ◽  
Sentinel 2

&lt;p&gt;Annual grasslands are an essential component of Mediterranean oak savannas, the most extensive agroforestry system in Europe, as the primary source of fodder for livestock and wildlife. Monitoring its phenology is key to adequately assess the impacts of global warming on different time scales and identify pre-critical states in the framework of early warning decision making systems. The natural variability of the climatic-hydrological regime in these areas and the usually complex spatial patterns of the vegetation, with sparse distribution and multiple layers, encourage the exploitation of available data from remote sensing sources. This work presents an assessment of vegetation indexes (VI) from Sentinel-2 validated against field data from terrestrial photography in an oak-grass system in southern Spain as a multi-approach method to monitor phenology in grass pastures. The analysis also has provided an insight into the links of the phenology dynamics with hydrological variables under these conditions.&lt;/p&gt;&lt;p&gt;From December 2017 to May 2019 a quantitative value of grassland greenness was computed using the Green Chromatic Coordinate (GCC) index. The phenological parameters of the start of the season (SOS), the peak of the season (POS) and end of the season (EOS) were extracted using the 50% amplitude method and confirmed using field photography. These values were compared with those provided by eight VI's derived from Sentinel-2 (NDVI, GNDVI, SAVI, EVI, EVI2, MTCI, IRECI and S2REP) and the difference in days between the key phenological dates were estimated. The results showed that for annual grasslands NDVI was the index providing estimations closest to those of ground GCC, with differences below 10 days for all phenological dates and the best correlation with GCC values (r = 0.83, p &lt;0.001). None of the VIs using bands in the red-edge region have improved the NDVI results. Two of them, MTCI and S2REP, followed a different trend that the rest of explored indices, presenting a high temporal variability. The high diversity of species, typical of Mediterranean grasslands, might explain the high variability observed in these values. However, the third index using red-edge bands, IRECI, presented a high correlation with GCC. In this case, the index was designed to focus on the chlorophyll content of the canopy instead of the leaf scale addressed by S2REP. The influence of the vegetation ground coverage and foliage density is then higher and more similar to the broad-band indices. GNDVI also provided good general results. Soil moisture (SM) time-series were also used to estimate phenology and have presented a good agreement with GCC in SOS and EOS estimations, with SM reaching threshold values a few days before greenness ones, as measured by GCC. However, SM was not a good indicator of the POS, presenting significant biases with respect to GCC estimations.&lt;/p&gt;

scholarly journals Investigating the Spatio-Temporal Variation of Soil Moisture and Agricultural Drought towards Supporting Water Resources Management in the Red River Basin of Vietnam

2021 ◽  
Vol 13 (9) ◽  
pp. 4926
Author(s):  
Nguyen Duc Luong ◽  
Nguyen Hoang Hiep ◽  
Thi Hieu Bui
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Temporal Dynamics ◽  
Regional Scale ◽  
Dry Season ◽  
Red River ◽  
Agricultural Drought ◽  
Severe Drought ◽  
Red River Basin ◽  
Spatio Temporal

The increasing serious droughts recently might have significant impacts on socioeconomic development in the Red River basin (RRB). This study applied the variable infiltration capacity (VIC) model to investigate spatio-temporal dynamics of soil moisture in the northeast, northwest, and Red River Delta (RRD) regions of the RRB part belongs to territory of Vietnam. The soil moisture dataset simulated for 10 years (2005–2014) was utilized to establish the soil moisture anomaly percentage index (SMAPI) for assessing intensity of agricultural drought. Soil moisture appeared to co-vary with precipitation, air temperature, evapotranspiration, and various features of land cover, topography, and soil type in three regions of the RRB. SMAPI analysis revealed that more areas in the northeast experienced severe droughts compared to those in other regions, especially in the dry season and transitional months. Meanwhile, the northwest mainly suffered from mild drought and a slightly wet condition during the dry season. Different from that, the RRD mainly had moderately to very wet conditions throughout the year. The areas of both agricultural and forested lands associated with severe drought in the dry season were larger than those in the wet season. Generally, VIC-based soil moisture approach offered a feasible solution for improving soil moisture and agricultural drought monitoring capabilities at the regional scale.

A laboratory study of the effect of soil-water dynamics on the migration of gases from subsurface sources

2021 ◽  
Author(s):  
Ana M. C. Ilie ◽  
Tissa H. Illangasekare ◽  
Kenichi Soga ◽  
William R. Whalley
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Sandy Soil ◽  
Land Surface ◽  
Soil Gas ◽  
Water Dynamics ◽  
Gas Migration ◽  
Soil Water Dynamics ◽  
Scale Test ◽  
Properties Of Soil

&lt;p&gt;Understanding the soil-gas migration in unsaturated soil is important in a number of problems that include carbon loading to the atmosphere from the bio-geochemical activity and leakage of gases from subsurface sources from carbon storage unconventional energy development. The soil water dynamics in the vadose zone control the soil-gas pathway development and, hence, the gas flux's spatial and temporal distribution at the soil surface. The spatial distribution of soil-water content depends on soil water characteristics. The dynamics are controlled by the water flux at the land surface and water table fluctuations. Physical properties of soil give a better understanding of the soil gas dynamics and migration from greater soil depths. The fundamental process of soil gas migration under dynamic water content was investigated in the laboratory using an intermediate-scale test system under controlled conditions that is not possible in the field. The experiments focus on observing the methane gas migration in relation to the physical properties of soil and the soil moisture patterns. A 2D soil tank with dimensions of 60 cm &amp;#215; 90 cm &amp;#215; 5.6 cm (height &amp;#215; length &amp;#215; width) was used.&amp;#160; The tank was heterogeneously packed with sandy soil along with a distributed network of soil moisture, temperature, and electrical conductivity sensors. The heterogeneous soil configuration was designed using nine uniform silica sands with the effective sieve numbers #16, #70, #8, #40/50, #110, #30/40, #50, and #20/30 (Accusands, Unimin Corp., Ottawa, MN), and a porosity ranging in values from 0.31 to 0.42. Four methane infrared gas sensors and a Flame Ionization detector (HFR400 Fast FID) were used for the soil gas sampling at different depths within the soil profiles and at the land surface.&amp;#160; A complex transient soil moisture distribution and soil gas migration patterns were observed in the 2D tank. These processes were successfully captured by the sensors. These preliminary experiments helped us to understand the mechanism of soil moisture sensor response and methane gas migration into a heterogeneous sandy soil with a view to developing a large-scale test in a 3D tank (4.87 m &amp;#215; 2.44 m &amp;#215; 0.40 m) and finally transition to field deployment.&lt;/p&gt;

Soil moisture assessment based on Sentinel 1/2 and in-situ data: The vineyard case study

2021 ◽  
Author(s):  
Maria Paula Mendes ◽  
Ana Paula Falcão ◽  
Magda Matias ◽  
Rui Gomes
Keyword(s):  
Soil Moisture ◽  
Climate Models ◽  
Root Zone ◽  
Irrigation Management ◽  
Management Strategies ◽  
Winter Period ◽  
Classification Algorithms ◽  
Precipitation Regime ◽  
Terrain Attributes ◽  
Sentinel 2

&lt;p&gt;Vineyards are crops whose production has a major economic impact in the Portuguese economy (~750 million euros) being exported worldwide. As the climate models project a larger variability in precipitation regime, the water requirements of vineyards can change and drip irrigation can be responsible for salt accumulation in the root zone, especially when late autumn and winter precipitation is not enough to leach salts from the soil upper horizons, turning the soil unsuitable for grape production.&lt;/p&gt;&lt;p&gt;The aim of this work is to present a methodology to map surface soil moisture content (SMC) in a vineyard, (40 hectares) based on the application of two classification algorithms to satellite imagery (Sentinel 1 and Sentinel 2). Two vineyard plots were considered and three field campaigns (December 2017, January 2018 and May 2018) were conducted to measure soil moisture contents (SMC). A geostatistical method was used to estimate the SM class probabilities according to a threshold value, enlarging the training set (i.e., SMC data of the two plots) for the classification algorithms. Sentinel-1 and Sentinel-2 images and terrain attributes fed the classification algorithms. Both methods, Random Forest and Logistic Regression, classified the highest SMC areas, with probabilities above 14%, located close to a stream at the lower altitudes.&lt;/p&gt;&lt;p&gt;RF performed very well in classifying the topsoil zones with lower SMC during the autumn-winter period (F-measure=0.82).&lt;/p&gt;&lt;p&gt;This delineation allows the prevention of the occurrence of areas affected by salinization, indicating which areas will need irrigation management strategies to control the salinity, especially under climate change, and the expected increase in droughts.&lt;/p&gt;

scholarly journals Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

2018 ◽  
Vol 22 (6) ◽  
pp. 3229-3243 ◽  
Author(s):  
Maoya Bassiouni ◽  
Chad W. Higgins ◽  
Christopher J. Still ◽  
Stephen P. Good
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Dry Season ◽  
Soil Water Balance ◽  
Rainfall Pattern ◽  
Site Specific ◽  
Soil Moisture Dynamics ◽  
Soil Saturation ◽  
Moisture Dynamics

Abstract. Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash–Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < 1 to 15 %. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations.

scholarly journals Satellite evidence of substantial rain-induced soil emissions of ammonia across the Sahel

2018 ◽  
Vol 18 (22) ◽  
pp. 16713-16727 ◽  
Author(s):  
Jonathan E. Hickman ◽  
Enrico Dammers ◽  
Corinne Galy-Lacaux ◽  
Guido R. van der Werf
Keyword(s):  
Soil Moisture ◽  
Rainy Season ◽  
Agricultural Soils ◽  
Fine Particulate Matter ◽  
Primary Source ◽  
N Deposition ◽  
Box Model ◽  
Ozone Monitoring Instrument ◽  
Animal Excreta ◽  
Birch Effect

Abstract. Atmospheric ammonia (NH3) is a precursor to fine particulate matter formation and contributes to nitrogen (N) deposition, with potential implications for the health of humans and ecosystems. Agricultural soils and animal excreta are the primary source of atmospheric NH3, but natural soils can also be an important emitter. In regions with distinct dry and wet seasons such as the Sahel, the start of the rainy season triggers a pulse of biogeochemical activity in surface soils known as the Birch effect, which is often accompanied by emissions of microbially produced gases such as carbon dioxide and nitric oxide. Field and lab studies have sometimes, but not always, observed pulses of NH3 after the wetting of dry soils; however, the potential regional importance of these emissions remains poorly constrained. Here we use satellite retrievals of atmospheric NH3 using the Infrared Atmospheric Sounding Interferometer (IASI) regridded at 0.25∘ resolution, in combination with satellite-based observations of precipitation, surface soil moisture, and nitrogen dioxide concentrations, to reveal substantial precipitation-induced pulses of NH3 across the Sahel at the onset of the rainy season in 2008. The highest concentrations of NH3 occur in pulses during March and April when NH3 biomass burning emissions estimated for the region are low. For the region of the Sahel spanning 10 to 16∘ N and 0 to 30∘ E, changes in NH3 concentrations are weakly but significantly correlated with changes in soil moisture during the period from mid-March through April when the peak NH3 concentrations occur (r=0.28, p=0.02). The correlation is also present when evaluated on an individual pixel basis during April (r=0.16, p<0.001). Average emissions for the entire Sahel from a simple box model are estimated to be between 2 and 6 mg NH3 m−2 d−1 during peaks of the observed pulses, depending on the assumed effective NH3 lifetime. These early season pulses are consistent with surface observations of monthly concentrations, which show an uptick in NH3 concentration at the start of the rainy season for sites in the Sahel. The NH3 concentrations in April are also correlated with increasing tropospheric NO2 concentrations observed by the Ozone Monitoring Instrument (r=0.78, p<0.0001), which have previously been attributed to the Birch effect. Box model results suggest that pulses occurring over a 35-day period in March and April are responsible for roughly one-fifth of annual emissions of NH3-N from the Sahel. We conclude that precipitation early in the rainy season is responsible for substantial NH3 emissions in the Sahel, likely representing the largest instantaneous fluxes of gas-phase N from the region during the year.

scholarly journals Implications of CO<sub>2</sub> pooling on δ<sup>13</sup>C of ecosystem respiration and leaves in Amazonian forest

2008 ◽  
Vol 5 (3) ◽  
pp. 779-795 ◽  
Author(s):  
A. C. de Araújo ◽  
J. P. H. B. Ometto ◽  
A. J. Dolman ◽  
B. Kruijt ◽  
M. J. Waterloo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Carbon Isotope ◽  
Isotope Ratio ◽  
Ecosystem Respiration ◽  
Nitrogen Concentration ◽  
Carbon Isotope Ratio ◽  
Dry Season ◽  
Small Scale ◽  
Supporting Evidence ◽  
Co2 Efflux

Abstract. The carbon isotope of a leaf (δ13Cleaf) is generally more negative in riparian zones than in areas with low soil moisture content or rainfall input. In Central Amazonia, the small-scale topography is composed of plateaus and valleys, with plateaus generally having a lower soil moisture status than the valley edges in the dry season. Yet in the dry season, the nocturnal accumulation of CO2 is higher in the valleys than on the plateaus. Samples of sunlit leaves and atmospheric air were collected along a topographical gradient in the dry season to test whether the δ13Cleaf of sunlit leaves and the carbon isotope ratio of ecosystem respired CO2 (δ13CReco) may be more negative in the valley than those on the plateau. The δ13Cleaf was significantly more negative in the valley than on the plateau. Factors considered to be driving the observed variability in δ13Cleaf were: leaf nitrogen concentration, leaf mass per unit area (LMA), soil moisture availability, more negative carbon isotope ratio of atmospheric CO2 (δ13Ca) in the valleys during daytime hours, and leaf discrimination (Δleaf). The observed pattern of δ13Cleaf might suggest that water-use efficiency (WUE) is higher on the plateaus than in the valleys. However, there was no full supporting evidence for this because it remains unclear how much of the difference in δ13Cleaf was driven by physiology or &amp;delta13Ca. The δ13CReco was more negative in the valleys than on the plateaus on some nights, whereas in others it was not. It is likely that lateral drainage of CO2 enriched in 13C from upslope areas might have happened when the nights were less stable. Biotic factors such as soil CO2 efflux (Rsoil) and the responses of plants to environmental variables such as vapor pressure deficit (D) may also play a role. The preferential pooling of CO2 in the low-lying areas of this landscape may confound the interpretation of δ13Cleaf and δ13CReco.

scholarly journals Implications of CO<sub>2</sub> pooling on δ<sup>13</sup>C of ecosystem respiration and leaves in Amazonian forest

2007 ◽  
Vol 4 (6) ◽  
pp. 4459-4506
Author(s):  
A. C. de Araújo ◽  
J. P. H. B. Ometto ◽  
A. J. Dolman ◽  
B. Kruijt ◽  
M. J. Waterloo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Carbon Isotope ◽  
Isotope Ratio ◽  
Ecosystem Respiration ◽  
Nitrogen Concentration ◽  
Carbon Isotope Ratio ◽  
Dry Season ◽  
Atmospheric Co2 ◽  
Small Scale ◽  
Co2 Efflux

Abstract. The carbon isotope of a leaf (δ13Cleaf) is generally more negative in riparian zones than in areas with low soil moisture content or rainfall input. In Central Amazonia, the small-scale topography is composed of plateaus and valleys, with plateaus generally being drier than the valley edges in the dry season. The nocturnal accumulation of CO2 is higher in the valleys than on the plateaus in the dry season. The CO2 stored in the valleys takes longer to be released than that on the plateaus, and sometimes the atmospheric CO2 concentration (ca) does not drop to the same level as on the plateaus at any time during the day. Samples of sunlit leaves and atmospheric air were collected along a topographical gradient to test whether the δ13Cleaf of sunlit leaves and the carbon isotope ratio of ecosystem respired CO2 (δ13CR) may be more negative in the valley than those on the plateau. The δ13Cleaf was significantly more negative in the valley than on the plateau. Factors considered to be driving the observed variability in δ13Cleaf were: leaf nitrogen concentration, leaf mass per unit area (LMA), soil moisture availability, more negative carbon isotope ratio of atmospheric CO2 (δ13Ca) in the valleys during daytime hours, and leaf discrimination (Δleaf). The observed pattern of δ13Cleaf suggests that water-use efficiency (WUE) may be higher on the plateaus than in the valleys. The ;13CR was more negative in the valleys than on the plateaus on some nights, whereas in others it was not. It is likely that lateral drainage of CO2 enriched in 13C from upslope areas might have happened when the nights were less stable. Biotic factors such as soil CO2 efflux (Rsoil) and the responses of plants to environmental variables such as vapor pressure deficit (D) may also play a role.

Sign in / Sign up

Export Citation Format

Share Document