Remote Sensing-Based Evapotranspiration Modelling for Agricultural Water Management in the Limpopo Basin

2018 ◽  
pp. 50-85
Author(s):  
Berhanu F. Alemaw ◽  
Thebeyame Ronald Chaoka ◽  
Brigton Munyai
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Actual Evapotranspiration ◽  
Data Sets ◽  
Landsat Data ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Factors Influencing ◽  
Five Factors ◽  
Semi Arid

The study was motivated by the need to determine the spatial variation of ET and to test the applicability of RS based methods in arid to semi-arid climates with limited ground-based measurements. In this paper we present results of an effort of determining spatial actual evapotranspiration in the Limpopo basin, the Notwane subcatchment in the south-eastern part of Botswana, using remote sensing data from MODIS and Landsat Data sets. The Simplified Surface Energy Balance Index (S-SEBI) was applied to determine actual evapotranspiration using the seven bands of Landsat and MODIS surface reflectance and temperature channels. Three different dates were used to estimate ET from both Landsat and MODIS scenes. The estimated ET values from the two sensors show approximately equally comparable results. An assessment was also conducted to determine the factors influencing evapotranspiration. No strong correlation was identified for ET against the five factors investigated: Net radiation, NDVI, Surface Temperature, emissivity and surface albedo.

Remote Sensing-Based Evapotranspiration Modelling for Agricultural Water Management in the Limpopo Basin

2019 ◽  
pp. 249-286
Author(s):  
Berhanu F. Alemaw ◽  
Thebeyame Ronald Chaoka ◽  
Brigton Munyai
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Actual Evapotranspiration ◽  
Data Sets ◽  
Landsat Data ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Factors Influencing ◽  
Five Factors ◽  
Semi Arid

The study was motivated by the need to determine the spatial variation of ET and to test the applicability of RS based methods in arid to semi-arid climates with limited ground-based measurements. In this paper we present results of an effort of determining spatial actual evapotranspiration in the Limpopo basin, the Notwane subcatchment in the south-eastern part of Botswana, using remote sensing data from MODIS and Landsat Data sets. The Simplified Surface Energy Balance Index (S-SEBI) was applied to determine actual evapotranspiration using the seven bands of Landsat and MODIS surface reflectance and temperature channels. Three different dates were used to estimate ET from both Landsat and MODIS scenes. The estimated ET values from the two sensors show approximately equally comparable results. An assessment was also conducted to determine the factors influencing evapotranspiration. No strong correlation was identified for ET against the five factors investigated: Net radiation, NDVI, Surface Temperature, emissivity and surface albedo.

High-resolution soil moisture mapping through the use of Cosmic-Ray Neutron Sensor and Sentinel-1 data for temperate and semi-arid environments

2021 ◽  
Author(s):  
Hami Said ◽  
Modou Mbaye ◽  
Lee Kheng Heng ◽  
Emil Fulajtar ◽  
Georg Weltin ◽  
...  
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Regional Scale ◽  
Cosmic Ray ◽  
Microwave Remote Sensing ◽  
Nuclear Technology ◽  
Agricultural Water ◽  
Agricultural Water Management ◽  
Semi Arid

<p>Global climate change has a major impact on the availability of water in agriculture. Sustainable agricultural productivity to ensure food security requires good agricultural water management.</p><p>Soil moisture is one of the important variables in irrigation management, and there are many different techniques for estimating it at different scales, from point to landscape scales.</p><p>Cosmic-Ray Neutron Sensor (CRNS) technology has the capability to estimate field-scale soil moisture (SM) in large areas of up to 20 to 30 ha and has demonstrated its ability to support agricultural water management and hydrology studies. However, measurement of soil moisture on a global or regional scale can only be achieved from satellite remote sensing.</p><p>Recently, active microwave remote sensing Synthetic Aperture Radar (SAR) imaging from Sentinel-1 shows great potential for high spatial resolution soil moisture monitoring and can be the basis for producing soil moisture maps. However, these maps can be only used after calibration. Such calibration can be done through traditional, point soil moisture sampling or measurement, which is time-consuming and costly. CRNS technology can be used for calibration and validation remote sensing imagery predictions at field and area-wide level.</p><p>In this study a conversion model to retrieve soil moisture from Sentinel-1 (SAR) was developed using the VV (vertical-vertical) polarization, which is highly sensitive to soil moisture, and then calibrated and validated using CRNS data from temperate (Austria) and semi-arid (Kuwait) Environments. This study is a major step in the monitoring of soil moisture at high spatial and temporal resolution by combining remote sensing and the CRNS based nuclear technology. The preliminary results show the great potential of using nuclear technology such as CRNS for remote sensing calibration of Sentinel-1 (SAR).</p>

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

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.

scholarly journals The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

2017 ◽  
Vol 21 (9) ◽  
pp. 4747-4765 ◽  
Author(s):  
Clara Linés ◽  
Micha Werner ◽  
Wim Bastiaanssen
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Drought Management ◽  
Ebro Basin ◽  
Sensing Data ◽  
Drought Impacts ◽  
Management Plans ◽  
Drought Indicators ◽  
Selection Of

Abstract. The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation–anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.

Estimation of water stress in olive orchards through remote sensing data analysis

2021 ◽  
Author(s):  
Luz Karime Atencia ◽  
María Gómez del Campo ◽  
Gema Camacho ◽  
Antonio Hueso ◽  
Ana M. Tarquis
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Water Potential ◽  
Statistical Tests ◽  
Remote Sensing Data ◽  
Olive Tree ◽  
Fruit Trees ◽  
Data Sets ◽  
Stem Water Potential ◽  
Stem Water

<p>Olive is the main fruit tree in Spain representing 50% of the fruit trees surface, around 2,751,255 ha. Due to its adaptation to arid conditions and the scarcity of water, regulated deficit irrigation (RDI) strategy is normally applied in traditional olive orchards and recently to high density orchards. The application of RDI is one of the most important technique used in the olive hedgerow orchard. An investigation of the detection of water stress in nonhomogeneous olive tree canopies such as orchards using remote sensing imagery is presented.</p><p>In 2018 and 2019 seasons, data on stem water potential were collected to characterize tree water state in a hedgerow olive orchard cv. Arbequina located in Chozas de Canales (Toledo). Close to the measurement’s dates, remote sensing images with spectral and thermal sensors were acquired. Several vegetation indexes (VI) using both or one type of sensors were estimated from the areas selected that correspond to the olive crown avoiding the canopy shadows.</p><p>Nonparametric statistical tests between the VIs and the stem water potential were carried out to reveal the most significant correlation. The results will be discussing in the context of robustness and sensitivity between both data sets at different phenological olive state.</p><p><strong>ACKNOWLODGEMENTS</strong></p><p>Financial support provided by the Spanish Research Agency co-financed with European Union FEDER funds (AEI/FEDER, UE, AGL2016-77282-C3-2R project) and Comunidad de Madrid through calls for grants for the completion of Industrial Doctorates, is greatly appreciated.</p>

scholarly journals Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

2015 ◽  
Vol 19 (1) ◽  
pp. 507-532 ◽  
Author(s):  
P. Karimi ◽  
W. G. M. Bastiaanssen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Water Resource Management ◽  
Hydrological Modeling ◽  
Remote Sensing Data ◽  
Actual Evapotranspiration ◽  
Primary Concern ◽  
Suitable Alternative ◽  
Sensing Data ◽  
Water Accounting

Abstract. The scarcity of water encourages scientists to develop new analytical tools to enhance water resource management. Water accounting and distributed hydrological models are examples of such tools. Water accounting needs accurate input data for adequate descriptions of water distribution and water depletion in river basins. Ground-based observatories are decreasing, and not generally accessible. Remote sensing data is a suitable alternative to measure the required input variables. This paper reviews the reliability of remote sensing algorithms to accurately determine the spatial distribution of actual evapotranspiration, rainfall and land use. For our validation we used only those papers that covered study periods of seasonal to annual cycles because the accumulated water balance is the primary concern. Review papers covering shorter periods only (days, weeks) were not included in our review. Our review shows that by using remote sensing, the absolute values of evapotranspiration can be estimated with an overall accuracy of 95% (SD 5%) and rainfall with an overall absolute accuracy of 82% (SD 15%). Land use can be identified with an overall accuracy of 85% (SD 7%). Hence, more scientific work is needed to improve the spatial mapping of rainfall and land use using multiple space-borne sensors. While not always perfect at all spatial and temporal scales, seasonally accumulated actual evapotranspiration maps can be used with confidence in water accounting and hydrological modeling.

Hydric stress detection through actual evapotranspiration by remote sensing in semi-arid catchments

1999 ◽  
Author(s):  
Fabien Lahoche ◽  
Sonia Bouzidi ◽  
Isabelle L. Herlin ◽  
Jean-Paul Berroir
Keyword(s):  
Remote Sensing ◽  
Actual Evapotranspiration ◽  
Stress Detection ◽  
Hydric Stress ◽  
Semi Arid
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