Remote Sensing of Wetland Dynamics as Indicators of Water Availability in Semi-Arid Africa

Abstract. In semi-arid areas, agricultural production is restricted by water availability; hence efficient agricultural water management is a major issue. The design of tools providing regional estimates of evapotranspiration (ET), one of the most relevant water balance fluxes, may help the sustainable management of water resources. Remote sensing provides periodic data about actual vegetation temporal dynamics (through the Normalized Difference Vegetation Index NDVI) and water availability under water stress (through the land surface temperature LST) which are crucial factors controlling ET. In this study, spatially distributed estimates of ET (or its energy equivalent, the latent heat fluxes LE) in the Kairouan plain (Central Tunisia) were computed by applying the Soil Plant Atmosphere and Remote Sensing Evapotraspiration (SPARSE) model fed by low resolution remote sensing data (Terra and Aqua MODIS). The work goal was to assess the operational use of the SPARSE model and the accuracy of the modelled i) sensible heat flux (H) and ii) daily ET over a heterogeneous semi-arid landscape with a complex land cover (i.e. trees, winter cereals, summer vegetables). The SPARSE's layer approach was run to compute instantaneous estimates of H and LE fluxes at the satellite overpass time. The good correspondence (R2 = 0.60 and 0.63 and RMSE = 57.89 W/m-2 and 53.85 W/m-2; for Terra and Aqua, respectively) between instantaneous H estimates and large aperture scintillometer (XLAS)'s H measurements along a pathlength of 4 km over the study area showed that the SPARSE model presents satisfactory accuracy. Results showed that, despite the fairly large scatter, the instantaneous LE can be suitably estimated at large scale (RMSE = 47.20 W/m-2 and 43.20 W/m-2; for Terra and Aqua, respectively and R2 = 0.55 for both satellites). Additionally, water stress was investigated by comparing modelled (SPARSE derived) to observed (XLAS derived) water stress values; we found that most points were located within a 0.2 conﬁdence interval, thus the general tendencies are well reproduced. Even though extrapolation of instantaneous latent heat flux values to daily totals was less obvious, daily ET estimates are deemed acceptable.

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Identification of droughts from monitored and modelled vegetation condition for improved water management in semi-arid areas

10.5194/egusphere-egu2020-18580 ◽

2020 ◽

Author(s):

Anita Bayer ◽

Christine Mihalyfi-Dean ◽

Robert Behling ◽

Christof Lorenz ◽

Saskia Foerster ◽

...

Keyword(s):

Water Management ◽

Remote Sensing ◽

Water Availability ◽

Standardized Precipitation Index ◽

Integrated Analysis ◽

Arid Areas ◽

Large Variability ◽

Vegetation Condition ◽

Drought Indicators ◽

Semi Arid

<p>Semi-arid areas suffer from small amounts and a large variability in rainfall combined with an increasing risk of droughts under climate change. These long and short-term changes in water availability directly affecting regional livelihoods are depicted in the condition of the rather sparse vegetation. In this study, seasonal and long-term trends in indicators of the vegetation condition related to water availability and droughts (NDVI vs. fAPAR, NPP, soil water content, excess water) are identified from remote sensing data (MODIS) and a process-based dynamic vegetation model (LPJ-GUESS) for at least two semi-arid river basins. Identified trends of both methods are compared and evaluated based on the underlying processes and related to knowledge of past drought events. Finally, we answer the question, which methods and indicators are suitable to identify changes in the vegetation condition preceding a drought and during drought phases considering the methods and indicators as above plus simple precipitation-based drought indicators (e.g. standardized precipitation index, SPI) and enhanced drought indicators applying multiple indicators theirselves (e.g. combined drought indicator, CDI). The study is imbedded in the SaWaM project (Seasonal Water Management for semi-arid areas) and contributes to improved water management in the project regions by the integrated analysis of remote sensing and ecosystem modelling results that are made available to regional stakeholders tasked with water management in an online tool .</p>

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Comparison of blue and green water fluxes for different land use classes in a semi-arid cultivated catchment using remote sensing

Journal of Hydrology Regional Studies ◽

10.1016/j.ejrh.2021.100860 ◽

2021 ◽

Vol 36 ◽

pp. 100860

Author(s):

Anna Msigwa ◽

Hans C. Komakech ◽

Elga Salvadore ◽

Solomon Seyoum ◽

Marloes L. Mul ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Green Water ◽

Water Fluxes ◽

Semi Arid

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Multispectral Remote Sensing of Wetlands in Semi-Arid and Arid Areas: A Review on Applications, Challenges and Possible Future Research Directions

Remote Sensing ◽

10.3390/rs12244190 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4190

Author(s):

Siyamthanda Gxokwe ◽

Timothy Dube ◽

Dominic Mazvimavi

Keyword(s):

Remote Sensing ◽

Arid Regions ◽

Remote Sensing Data ◽

Sub Saharan Africa ◽

Future Research ◽

Multispectral Imagery ◽

Aquatic Life ◽

Sensing Data ◽

Spatial Coverage ◽

Semi Arid

Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions. A literature search shows a significant increase in the number of papers published during the 2000–2020 period, with most of these studies being in semi-arid regions in Australia and China, and few in the sub-Saharan Africa. This paper reviews progress made in the use of remote sensing in detecting and monitoring of the semi-arid and arid wetlands, and focuses particularly on new insights in detection and monitoring of wetlands using freely available multispectral sensors. The paper firstly describes important characteristics of wetlands in semi-arid and arid regions that require monitoring in order to improve their management. Secondly, the use of freely available multispectral imagery for compiling wetland inventories is reviewed. Thirdly, the challenges of using freely available multispectral imagery in mapping and monitoring wetlands dynamics like inundation, vegetation cover and extent, are examined. Lastly, algorithms for image classification as well as challenges associated with their uses and possible future research are summarised. However, there are concerns regarding whether the spatial and temporal resolutions of some of the remote-sensing data enable accurate monitoring of wetlands of varying sizes. Furthermore, it was noted that there were challenges associated with the both spatial and spectral resolutions of data used when mapping and monitoring wetlands. However, advancements in remote-sensing and data analytics provides new opportunities for further research on wetland monitoring and assessment across various scales.

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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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