scholarly journals Drought Monitoring over West Africa Based on an Ecohydrological Simulation (2003–2018)

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 155
Author(s):  
Hiroyuki Tsutsui ◽  
Yohei Sawada ◽  
Katsuhiro Onuma ◽  
Hiroyuki Ito ◽  
Toshio Koike
Keyword(s):  
West Africa ◽  
Food Production ◽  
Soil Moisture Content ◽  
Water Cycle ◽  
Area Index ◽  
Vegetation Water Content ◽  
Significant Damage ◽  
Vegetation Water ◽  
Terrestrial Biomass ◽  
Data Assimilation System

In Africa, droughts are causing significant damage to human health and the economy. In West Africa, a severe decline in food production due to agricultural droughts has been reported in recent years. In this study, we simulated ecohydrological variables using the Coupled Land and Vegetation Data Assimilation System, which can effectively evaluate the hydrological water cycle and provide a dynamic evaluation of terrestrial biomass. Using ecohydrological variables (e.g., soil moisture content, leaf area index and vegetation water content) as a drought indicator, we analyzed agricultural droughts in the Sahel-inland region of West Africa during 2003–2018. Results revealed reasonable agreement between the simulated values and the pearl millet yield, and produced a successful quantification of severe droughts in the Sahel-inland region.

scholarly journals Reconstructing Continuous Vegetation Water Content To Understand Sub-daily Backscatter Variations

2021 ◽  
Author(s):  
Paul C. Vermunt ◽  
Susan C. Steele-Dunne ◽  
Saeed Khabbazan ◽  
Jasmeet Judge ◽  
Nick C. van de Giesen
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Water Cycle ◽  
Water Dynamics ◽  
High Temporal Resolution ◽  
Daily Variations ◽  
Vegetation Water Content ◽  
Dew Formation ◽  
Vegetation Water ◽  
Canopy Water

Abstract. Microwave observations are sensitive to vegetation water content (VWC). Consequently, the increasing temporal and spatial resolution of spaceborne microwave observations creates a unique opportunity to study vegetation water dynamics and its role in the diurnal water cycle. However, we currently have a limited understanding of sub-daily variations in VWC and how they affect passive and active microwave observations. This is partly due to the challenges associated with measuring internal VWC for validation, particularly non-destructively and at timescales of less than a day. In this study, we aimed to (1) use field sensors to reconstruct diurnal and continuous records of internal VWC of corn, and (2) use these records to interpret the sub-daily behaviour of a 10-day time series of polarimetric L-band backscatter with high temporal resolution. Sub-daily variations of internal VWC were calculated based on the cumulative difference between estimated transpiration and sap flow rates at the base of the stems. Destructive samples were used to constrain the estimates and for validation. The inclusion of continuous surface canopy water estimates (dew or interception) and surface soil moisture allowed us to attribute hour-to-hour backscatter dynamics to either internal VWC, surface canopy water or soil moisture variations. Our results showed that internal VWC varied with 10–20 % during the day in non-stressed conditions, and the effect on backscatter was significant. Diurnal variations of internal VWC and nocturnal dew formation affected vertically polarized backscatter most. Moreover, on a typical dry day, backscatter variations were 1.5 (HH-pol) to 3 (VV-pol) times more sensitive to VWC than to soil moisture. These results demonstrate that radar observations have the potential to provide unprecedented insight into the role of vegetation water dynamics in land-atmosphere interactions at sub-daily timescales.

scholarly journals Relationship between Vegetation Biophysical Properties and Surface Temperature Using Multisensor Satellite Data

2007 ◽  
Vol 20 (22) ◽  
pp. 5593-5606 ◽  
Author(s):  
Seungbum Hong ◽  
Venkat Lakshmi ◽  
Eric E. Small
Keyword(s):  
Surface Temperature ◽  
Water Content ◽  
Land Surface ◽  
Vegetation Index ◽  
Climatic Variability ◽  
Regional Distribution ◽  
Spatial And Temporal Variability ◽  
Area Index ◽  
Vegetation Water Content ◽  
Vegetation Water

Abstract Vegetation is an important factor in global climatic variability and plays a key role in the complex interactions between the land surface and the atmosphere. This study focuses on the spatial and temporal variability of vegetation and its relationship with land–atmosphere interactions. The authors have analyzed the vegetation water content (VegWC) from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), the leaf area index (LAI), the normalized difference vegetation index (NDVI), the land surface temperature (Ts), and the Moderate Resolution Imaging Spectroradiometer (MODIS). Three regions, which have climatically differing characteristics, have been selected: the North America Monsoon System (NAMS) region, the Southern Great Plains (SGP) region, and the Little River Watershed in Tifton, Georgia. Temporal analyses were performed by comparing satellite observations from 2003 and 2004. The introduction of the normalized vegetation water content (NVegWC) derived as the ratio of VegWC and LAI corresponding to the amount of water in individual leaves has been estimated and this yields significant correlation with NDVI and Ts. The analysis of the NVegWC–NDVI relationship in the above listed three regions displays a negative exponential relation, and the Ts–NDVI relationship (TvX relationship) is inversely proportional. The correlation between these variables is higher in arid areas such as the NAMS region, and becomes less correlated in the more humid and more vegetated regions such as the area of eastern Georgia. A land-cover map is used to examine the influence of vegetation types on the vegetation biophysical and surface temperature relationships. The regional distribution of vegetation reflects the relationship between the vegetation biological characteristics of water and the growing environment.

scholarly journals Seasonal variation in vegetation water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

2015 ◽  
Vol 12 (7) ◽  
pp. 5503-5533
Author(s):  
G. Mendiguren ◽  
M. P. Martín ◽  
H. Nieto ◽  
J. Pacheco-Labrador ◽  
S. Jurdao
Keyword(s):  
Spatial Variation ◽  
Water Content ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Spectral Indices ◽  
Proximal Sensing ◽  
Area Index ◽  
Vegetation Water Content ◽  
Equivalent Water Thickness ◽  
Vegetation Water

Abstract. This study evaluates three different metrics of vegetation water content estimated from proximal sensing and MODIS satellite imagery: Fuel Moisture Content (FMC), Equivalent Water Thickness (EWT) and Canopy Water Content (CWC). Dry matter (Dm) and Leaf area Index (LAI) were also analyzed in order to connect FMC with EWT and EWT with CWC, respectively. This research took place in a Fluxnet site located in Mediterranean wooded grassland (dehesa) ecosystem in Las Majadas del Tietar (Spain). Results indicated that FMC and EWT showed lower spatial variation than CWC. The spatial variation within the MODIS pixel was not as critical as its temporal trend, so to capture better the variability, fewer plots should be sampled but more times. Due to the high seasonal Dm variability, a constant annual value would not work to predict EWT from FMC. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. VARI provided the worst results in all cases. For proximal sensing, GEMI worked best for both FMC (RRMSE = 34.5%) and EWT (RRMSE = 27.43%) while NDII and GVMI performed best for CWC (RRMSE =30.27% and 31.58% respectively). For MODIS data, results were a bit better with EVI as the best predictor for FMC (RRMSE = 33.81%) and CWC (RRMSE = 27.56%) and GEMI for EWT (RRMSE = 24.6%). To explain these differences, proximal sensing measures only grasslands at nadir view angle, but MODIS includes also trees, their shades, and other artifacts at up to 20° view angle. CWC was better predicted than the other two water content variables, probably because CWC depends on LAI, which is highly correlated to the spectral indices. Finally, these empirical methods outperformed FMC and CWC products based on radiative transfer model inversion.

scholarly journals Monitoring Cotton (Gossypium sps.) Crop Condition through Synergy of Optical and Radar Remote Sensing

2018 ◽  
Author(s):  
Dipanwita Haldar ◽  
Rojalin Tripathy ◽  
Viral Dave ◽  
Rucha Dave ◽  
Bimal Bhattacharya ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Water Content ◽  
Vegetation Index ◽  
Broad Band ◽  
Plant Water ◽  
Area Index ◽  
Vegetation Water Content ◽  
Vegetation Water ◽  
Crop Condition

Morphological parameters like cotton height, branches, Leaf Area Index and biomass are mainly affected by the vegetation water content (VWC). Periodical assessment of the VWC and crop parameters is required for timely management of the crop for maximizing yield. The study aimed at using both optical and microwave remotely sensed data to assess cotton crop condition based on the above mentioned traits. Vegetation indices (VI) derived from ground based measurements (5 narrow band and 2 broad band VIs) as well as satellite derived reflectance (2 broad band VIs) were assessed. Regression models were derived for estimating LAI, biomass and plant water content using the ground based indices and applied to the satellite derived spectral index (from LISS-III) map to estimate the respective parameters. HH and HV polarization from RISAT-1 were used to derive Radar Vegetation Index (RVI). The coefficient of determination of the model for estimating LAI, biomass and vegetation water content of cotton with optical vegetation index as input parameter were found to be 0.42, 0.51 and 0.52, respectively. The correlation between RVI and plant height, date of planting in terms of the age of the crop and vegetation water content were found to range between 0.4 to 0.6. The fresh biomass from RVI showed spatial variability from 100 gm-2 to 4000 gm-2 while the dry biomass map derived from NDVI showed spatial variability of 50 to 950 g m-2 for the study area. Plant water content in the district varied from 65 to 85%. The correlation between optical vegetation index and RVI was not significant. Hence a multiple linear regression model using both optical index (NDVI and LSWI) and SAR index (RVI) was developed to assess the LAI, biomass and plant water content. The model showed a R2 of 0.5 for LAI estimation but not significant for biomass and water content. This study show cased the use of combined optical and microwave (C band) remote sensing for cotton condition assessment.

How forests change transit times of transpiration, evaporation and streamflow – a modeling approach

2021 ◽  
Author(s):  
Ingo Heidbüchel ◽  
Jie Yang ◽  
Jan H. Fleckenstein
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Water Cycle ◽  
Rooting Depth ◽  
Water Fluxes ◽  
Area Index ◽  
Transit Times ◽  
Physically Based ◽  
Forested Areas ◽  
The Individual

<p>In a recent paper we investigated how different catchment and climate properties influence transit time distributions. This was done by employing a physically-based spatially explicit 3D model in a virtual catchment running many different scenarios with different combinations of catchment and climate properties. We found that the velocity distribution of water fluxes through a catchment is more sensitive to certain properties while other factors appear less relevant. Now we expanded the approach by adding vegetation to the model and thus introducing new hydrologic processes (transpiration and evaporation) to the simulated water cycle. On the one hand we wanted to know how these new processes would influence transit times of the water fluxes to the stream, on the other hand we were interested in how exactly differences in the vegetation itself (e.g. rooting depth and leaf area index) would alter the various flux velocities (including transit times of transpiration and evaporation). It was very interesting to observe that streamflow in forested areas appeared to become older on average. We also found that transpiration was generally younger if the vegetation had shallower roots and/or a larger leaf area index. The biggest difference in the age of evaporation was detected for different amounts of subsequent precipitation (evaporation was generally younger in a wetter climate). In conclusion, we found that forests influence the age of the different water fluxes within a catchment. According to our results the overall hydrologic cycle is decelerated when adding vegetation to a model that otherwise only simulates evaporation.</p><p>Still, in order to make meaningful predictions on the age of hydrologic fluxes, it is not constructive to single out specific catchment and climate properties. The multitude of influences from different parameters makes it very challenging to find rules and underlying principles in the integrated catchment response. Therefore it is necessary to look at the individual parameters and their potential interactions and interdependencies in a bottom-up approach.</p>

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