scholarly journals Assessment of Irrigation Water Use Patterns Using Remote Sensing Data in Mexico’s Northeast

10.29007/qz1w ◽  
2018 ◽  
Author(s):  
Saul Arciniega ◽  
Jose A. Breña-Naranjo ◽  
Adrián Pedrozo-Acuña ◽  
Antonio Hernández-Espriú
Keyword(s):  
Remote Sensing ◽  
Water Use ◽  
Irrigation Water ◽  
Land Surface ◽  
Irrigation System ◽  
Functional Model ◽  
Remote Sensing Data ◽  
Irrigation Scheduling ◽  
Sensing Data ◽  
Irrigation Water Use

Irrigation water use (IWU) or withdrawal is a key component for the water management of a region since it tends to exceed the crops consumptive water use, especially in water-stressed regions where groundwater is the main source of water. Nevertheless, temporal IWU information is missing in many irrigation areas. Remote sensing (RS) data is commonly used for crop water requirements estimations in areas with lack of data, however, IWU is more complex to approach since it also depends on water use efficiency, irrigation system type, irrigation scheduling, and water availability, among others. This work explores the use of remote sensing data (TRMM, MODIS) and land surface hydrological products (GLDAS 2 and MERRA 2) to obtain insights about the space-time annual IWU patterns across croplands located within Mexico’s northeast region. Reported IWU in three irrigation districts (Don Martín, Región Lagunera and Bajo Río Bravo) was used to obtain a functional model using satellite data derived. Results suggest strong relationship between reported IWU with soil moisture content from GLDAS and the maximum annual EVI from MODIS, where a potential regression shown statistical correlations of 0.83 and 0.77, respectively.

Combining the FAO56 agrohydrological model and remote sensing data to assess water demand in a Sicilian irrigation district

2020 ◽  
Author(s):  
Matteo Ippolito ◽  
Mario Minacapilli ◽  
Giuseppe Provenzano
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Water Use ◽  
Irrigation Water ◽  
Remote Sensing Data ◽  
Irrigation Scheduling ◽  
Irrigation District ◽  
Sensing Data ◽  
Spatiotemporal Distributions ◽  
Crop Parameters

<p>Agricultural water use in irrigated areas plays a key role in the Mediterranean regions characterized by semi-arid climate and water shortage. In the face of optimizing irrigation water use, farmers must revise their irrigation practices to increase the drought resilience of agricultural systems and to avoid severe damages in agro-ecosystems. In this direction, during the last decades, the research has been focused on mathematical models to simulate the process of driving mass transport and energy exchanges in the Soil-Plant-Atmosphere system.</p><p>The objective of the paper was to test the suitability of the combination of FAO56 agro-hydrological model with remote sensing data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform, to assess the spatiotemporal distributions of crop water requirement and to schedule irrigation in an irrigation district of the south-west of Sicily, Italy.</p><p>The proposed approach allowed obtaining the spatiotemporal distributions of soil and crop parameters used in the FAO56 model implemented in a GIS environment to simulate the water balance, as well as to assess the actual irrigation strategy. The GIS database was organized to include soil and crop parameters, as well as the irrigation volumes actually delivered to each farmer; the latter data can be used not only as input for water balance to evaluate the efficiency of the actual irrigation strategies but also to identify different irrigation scheduling scenario obtained by the FAO56 procedure.</p><p>The first application was carried out for the period 2014-2017, to identify a combination of irrigation scheduling parameters to be implemented in the model aimed at reproducing the ordinary strategy adopted by the farmers, based on the spatiotemporal variability of soil and climate forcings. When the model outputs were aggregated for single crop types, a fairly good agreement was found between simulated and actual seasonal irrigation volumes delivered either at the level of district and secondary units. Alternative scenarios of irrigation water distribution were then identified and analyzed, to provide irrigation technicians and policymakers a decision support tool to improve the efficiency of irrigation systems and to optimize the distribution based on the availability of water resources.</p>

Using a Remote Sensing data based toolkit to monitor vine water use and water status for real time irrigation scheduling in California vineyards

2020 ◽  
Author(s):  
Maria Mar Alsina ◽  
Kyle Knipper ◽  
Martha Anderson ◽  
WIlliam Kustas ◽  
Nicolas Bambach ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Water Use ◽  
Water Status ◽  
Irrigation Management ◽  
Remote Sensing Data ◽  
Irrigation Scheduling ◽  
Area Index ◽  
Pilot Experiment ◽  
Sensing Data

<p>Grapevines are one of the major drivers of agriculture in California, representing a production equivalent to $6.25 billion in 2018. Water is scarce, and increasingly intense and prolonged drought periods, like one that recently occurred in the 2012-2016 period, may happen with greater frequency. Consequently, there is a need to develop irrigation management decision tools to help growers maximize water use while maintaining productivity. Furthermore, grapevines are deficit irrigated, and a correct management of the vine water status during the season is key to achieve the target yield and quality. Traditionally, viticulturists use visual clues and/or leaf level indicators of vine water status to regulate the water deficit along the season. However, these methods are time-consuming and only provide discrete data that do not represent the often-high spatial variability of vineyards.  Remote sensing techniques may represent a fast real-time decision-making tool for irrigation management, able to extensively cover multiple vineyards with low human or economic investments. <br>While growers currently calculate the vine water demands using the reference evapotranspiration from a weather station located in the region and a crop coefficient, usually from literature, they don't have any means to measure or estimate the actual water used by the vines. Knowing the actual evapotranspiration (ET) in real-time and at a sub-field scale would provide essential information to monitor vine water status and adjust the irrigation amounts to the real water needs. The aim of the GRAPEX (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) project, has been to provide growers with an irrigation toolkit that integrates the spatial distribution of vine water use and water status. The project focuses on grapevines, but it will be easily extrapolated to orchards and other crop types.<br>We present the results of a pilot experiment where we applied the scientific developments of the GRAPEX project into a practical tool that growers can use for irrigation management. We run this pilot experiment over 6 commercial grapevine blocks, located in Cloverdale, Sonoma, CA. During the 2019 growing season, we provided the viticulturists with weekly maps of actual ET calculated using the DisALEXI model, Sentinel-2 Normalized Difference Vegetation and Normalized Vegetation Water Indices as well as local weather data, forecasted ET and soil moisture. The data were delivered weekly in a dashboard, including spatial and tabular views, as well as an irrigation recommendation derived from the past week's vine water use and water status data. Along with the remote sensing data, we took periodic measurements of leaf area index, leaf water potential, and gas exchange to evaluate the irrigation practices. We compared the irrigation prescription based on the provided data with the grower's practices. The total season irrigation ranged between 70 and 120 mm depending on the block, and our irrigation recommendations deviated between 10 and 30 mm from the growers' practices, also depending on the block. This analyzes the performance of the ET toolkit in assisting irrigation scheduling for improving water use efficiency of the vineyard blocks.</p>

Irrigation practices on north-east Victorian dairy farms: a survey

2005 ◽  
Vol 45 (12) ◽  
pp. 1539 ◽  
Author(s):  
D. J. Watson ◽  
G. Drysdale
Keyword(s):  
Water Use ◽  
Irrigation Water ◽  
Irrigation System ◽  
Sprinkler Irrigation ◽  
Dairy Farms ◽  
Irrigation Scheduling ◽  
Dairy Farmers ◽  
North East ◽  
Irrigation Water Use ◽  
Irrigation Practices

The north-east region of Victoria is an important water-harvesting catchment for gravity-fed irrigators downstream of Lake Mulwala. Dairy farmers are significant users of irrigation water in north-east Victoria but little was known about their irrigation practices and attitudes. A survey undertaken in 2000 collected data on irrigation practices and attitudes from 92% of the irrigating dairy farmers in the region. It found diversity in many aspects of irrigation amongst the region’s irrigated dairy farms, ranging from the proportion of the farm irrigated to the irrigation system used, and identified areas where improvements to irrigation practices could be made. More than 8 different irrigation systems were used in the region, and flood irrigation was the most commonly used. However, a large proportion (37%) of flood irrigators were contemplating changing to spray irrigation, mostly to long lateral hand move sprinkler irrigation, in an effort to improve water use efficiency. More than 50% of respondents did not meter irrigation water use, and 83% pumped water directly from rivers or creeks, with dams and dragline holes the next most common sources. Irrigation scheduling (when to start irrigating and the frequency of irrigation thereafter) and the amount of water to apply were generally based on knowledge and experience rather than on soil moisture monitoring equipment or use of evaporation rates. Most survey respondents recognised that their irrigation practices could improve and said that they would be interested in information to help them make more informed decisions about irrigation practices.

scholarly journals Evapotranspiration mapping for agricultural water management: An overview

2013 ◽  
Vol 5 (2) ◽  
pp. 522-534 ◽  
Author(s):  
Rakesh Kumar ◽  
Shweta Shambhavi ◽  
Rajesh Kumar ◽  
Yanendra Kumar Singh ◽  
Kisan Singh Rawat
Keyword(s):  
Remote Sensing ◽  
Water Use ◽  
Land Surface ◽  
Irrigation Management ◽  
Remote Sensing Data ◽  
Crop Coefficient ◽  
Estimation Accuracy ◽  
Water Evaporation ◽  
Sensing Data ◽  
Spatially Distributed

Evapotranspiration (ET) is an essential component of the water balance. Any attempt to improve water use efficiency must be based on reliable estimates of ET, which includes water evaporation from land and water surfaces and transpiration by vegetation. ET varies regionally and seasonally according to weather and wind conditions. Remote sensing based agro-meteorological models are presently most suited for estimating crop water use at both field and regional scales. Numerous ET algorithms have been developed to make use of remote sensing data acquired by sensors on airborne and satellite platforms. The use of remote sensing to estimate ET is presently being developed along two approaches: (a) land surface energy balance (EB) method and (b) Reflectance based crop coefficient and reference ET approach. The reported estimation accuracy varied from 67 to 97% for daily ET and above 94% for seasonal ET indicating that they have the potential to estimate regional ET accurately. Automated contours are not confined to specific pre-determined geographic areas (as in MLRA), require less time and cost. The spatial and temporal remote sensing data from the existing set of earth observing satellite platforms are not sufficient enough to be used in the estimation of spatially distributed ET for on-farm irrigation management purposes, especially at a field scale level (~10 to 200 ha). However, research opportunities exist to improve the spatial and temporal resolution of ET by developing algorithms to increase the spatial resolution of reflectance and surface temperature data derived from K1VHRR/Landsat/ASTER/MODIS images using same/other-sensor high resolution multi-spectral images.

scholarly journals Optimization of irrigation water in South Africa for sustainable and beneficial use

2017 ◽  
Author(s):  
◽  
Akinola Mayowa Ikudayisi
Keyword(s):  
South Africa ◽  
Water Use ◽  
Crop Yield ◽  
Optimization Algorithm ◽  
Irrigation Water ◽  
Irrigation Scheduling ◽  
Crop Water ◽  
Land Area ◽  
Water Requirements ◽  
Irrigation Water Use

Water is an essential natural resource for human existence and survival on the earth. South Africa, a water stressed country, allocates a high percentage of its available consumptive water use to irrigation. Therefore, it is necessary that we optimize water use in order to enhance food security. This study presents the development of mathematical models for irrigation scheduling of crops, optimal irrigation water release and crop yields in Vaal Harts irrigation scheme (VIS) of South Africa. For efficient irrigation water management, an accurate estimation of reference evapotranspiration (ETₒ) should be carried out. However, due to non-availability of enough historical data for the study area, mathematical models were developed to estimate ETₒ. A 20-year monthly meteorological data was collected and analysed using two data–driven modeling techniques namely principal component analysis (PCA) and adaptive neuro-fuzzy inference systems (ANFIS). Furthermore, an artificial neural network (ANN) model was developed for real time prediction of future ETₒ for the study area. The real time irrigation scheduling of potatoes was developed using a crop growth simulation model called CROPWAT. It was used to determine the crop water productivity (CWP), which is a determinant of the relationship between water applied and crop yield. Finally, a new and novel evolutionary multi-objective optimization algorithm called combined Pareto multi-objective differential evolution (CPMDE) was applied to optimize irrigation water use and crop yield on the VIS farmland. The net irrigation benefit, land area and irrigation water use of maize, potatoes and groundnut were optimized. Results obtained show that ETₒ increases with temperature and windspeed. Other variables such as rainfall and relative humidity have less significance on the value of ETₒ. Also, ANN models with one hidden layer showed better predictive performance compared with other considered configurations. A 5-day time step irrigation schedule data and graphs showing the crop water requirements and irrigation water requirements was generated. This would enable farmers know when, where, and how much water to apply to a given farmland. Finally, the employed CPMDE optimization algorithm produced a set of non-dominated Pareto optimal solutions. The best solution suggests that maize, groundnut and potatoes should be planted on 403543.44 m2, 181542.00 m2 and 352876.05 m2areas of land respectively. This solution generates a total net benefit of ZAR 767,961.49, total planting area of 937961.49 m2 and irrigation water volume of 391,061.52 m3. Among the three crops optimized, maize has the greatest land area, followed by potatoes and groundnut. This shows that maize is more profitable than potatoes and groundnut with respect to crop yield and water use in the study area.

Study on the Application and Comparative Analysis of Land Surface Temperature Retrieval Method Based on Multi-Sensor Remote Sensing Data

2014 ◽  
Vol 1010-1012 ◽  
pp. 1276-1279 ◽  
Author(s):  
Yin Tai Na
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Urban Heat ◽  
Split Window Algorithm ◽  
Temperature Retrieval

The three commonly used remote sensing land surface temperature retrieval methods are described, namely single-window algorithm, split window algorithm and multi-channel algorithm, which have their advantages and disadvantages. The land surface temperature (LST) of study area was retrieved with multi-source remote sensing data. LST of study area was retrieved with the split window algorithm on January 10, 2003 and November 19, 2003 which is comparatively analyzed with the LST result of ETM+data with the single-window algorithm and the LST result of ASTER data with multi channel algorithm in the same period. The results show that land surface temperature of different land features are significantly different, where the surface temperature of urban land is the highest, and that of rivers and lakes is the lowest, followed by woodland. It is concluded that the expansion of urban green space and protection of urban water can prevent or diminish the urban heat island.

scholarly journals Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture

2020 ◽  
Vol 12 (16) ◽  
pp. 2660
Author(s):  
Philip Marzahn ◽  
Swen Meyer
Keyword(s):  
Remote Sensing ◽  
Soil Texture ◽  
Land Surface ◽  
Temporal Frequency ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Test Site ◽  
Soil Parameters ◽  
Sensing Data

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches.

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