Influence of Spatial Resolution on Remote Sensing-Based Irrigation Performance Assessment Using WaPOR Data

This paper analyses the effect of the spatial assessment scale on irrigation performance indicators in small and medium-scale agriculture. Three performance indicators—adequacy (i.e., sufficiency of water use to meet the crop water requirement), equity (i.e., fairness of irrigation distribution), and productivity (i.e., unit of physical crop production/yield per unit water consumption)—are evaluated in five irrigation schemes for three spatial resolutions—250 m, 100 m, and 30 m. Each scheme has varying plot sizes and distributions, with average plot sizes ranging from 0.2 ha to 13 ha. The datasets are derived from the United Nations Food and Agricultural Organization (FAO) water productivity through open access of remotely sensed–derived data (the Water Productivity Open Access Portal—WaPOR) database. Irrigation indicators performed differently in different aspects; for adequacy, all three resolutions show similar spatial trends for relative evapotranspiration (ET) across levels for all years. However, the estimation of relative ET is often higher at higher resolution. In terms of equity, all resolutions show similar inter-annual trends in the coefficient of variation (CV); higher resolutions usually have a higher CV of the annual evapotranspiration and interception (ETIa) while capturing more spatial variability. For productivity, higher resolutions show lower crop water productivity (CWP) due to higher aboveground biomass productivity (AGBP) estimations in lower resolutions; they always have a higher CV of CWP. We find all resolutions of 250 m, 100 m, and 30 m suitable for inter-annual and inter-scheme assessments regardless of plot size. While each resolution shows consistent temporal trends, the magnitude of the trend in both space and time is smoothed by the 100 m and 250 m resolution datasets. This frequently results in substantial differences in the irrigation performance assessment criteria for inter-plot comparisons; therefore, 250 m and 100 m are not recommended for inter-plot comparison for all plot sizes, particularly small plots (<2 ha). Our findings highlight the importance of selecting the spatial resolution appropriate to scheme characteristics when undertaking irrigation performance assessment using remote sensing.

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Global Satellite-Based ET Products for the Local Level Irrigation Management: An Application of Irrigation Performance Assessment in the Sugarbelt of Swaziland

Remote Sensing ◽

10.3390/rs11060705 ◽

2019 ◽

Vol 11 (6) ◽

pp. 705 ◽

Cited By ~ 6

Author(s):

Poolad Karimi ◽

Bhembe Bongani ◽

Megan Blatchford ◽

Charlotte de Fraiture

Keyword(s):

Remote Sensing ◽

Performance Assessment ◽

Management Practices ◽

Local Level ◽

Water Productivity ◽

Irrigation Management ◽

Irrigation Performance ◽

Crop Water ◽

Management Regime ◽

Crop Water Productivity

Remote sensing techniques have been shown, in several studies, to be an extremely effective tool for assessing the performance of irrigated areas at various scales and diverse climatic regions across the world. Open access, ready-made, global ET products were utilized in this first-ever-countrywide irrigation performance assessment study. The study aimed at identifying ‘bright spots’, the highest performing sugarcane growers, and ‘hot spots’, or low performing sugarcane growers. Four remote sensing-derived irrigation performance indicators were applied to over 302 sugarcane growers; equity, adequacy, reliability and crop water productivity. The growers were segmented according to: (i) land holding size or grower scale (ii) management regime, (iii) location of the irrigation schemes and (iv) irrigation method. Five growing seasons, from June 2005 to October 2009, were investigated. The results show while the equity of water distribution is high across all management regimes and locations, adequacy and reliability of water needs improvement in several locations. Given the fact that, in general, water supply was not constrained during the study period, the observed issues with adequacy and reliability of irrigation in some of the schemes were mostly due to poor scheme and farm level water management practices. Sugarcane crop water productivity showed the highest variation among all the indicators, with Estate managed schemes having the highest CWP at 1.57 kg/m3 and the individual growers recording the lowest CWP at 1.14 kg/m3, nearly 30% less. Similarly center pivot systems showed to have the highest CWP at 1.63 kg/m3, which was 30% higher than the CWP in furrow systems. This study showcases the applicability of publicly available global remote sensing products for assessing performance of the irrigated crops at the local level in several aspects.

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A Framework for Irrigation Performance Assessment Using WaPOR data: The case of a Sugarcane Estate in Mozambique

10.5194/hess-2021-409 ◽

2021 ◽

Author(s):

Abebe Demissie Chukalla ◽

Marloes L. Mul ◽

Pieter van der Zaag ◽

Gerardo van Halsema ◽

Evaristo Mubaya ◽

...

Keyword(s):

Performance Assessment ◽

Performance Indicators ◽

Water Productivity ◽

Effective Means ◽

Cost Effective ◽

Absolute Error ◽

Irrigation Performance ◽

Irrigation Method ◽

Spatially Distributed ◽

Response To Water

Abstract. The growing competition for the finite land and water resources and the need to feed an ever-growing population requires new techniques to monitor the performance of irrigation schemes and improve land and water productivity. Datasets from FAO’s portal to monitor Water Productivity through Open access Remotely sensed derived data (WaPOR) is increasingly applied as a cost-effective means to support irrigation performance assessment and identifying possible pathways for improvement. This study presents a framework that applies WaPOR data to assess irrigation performance indicators including uniformity, equity, adequacy and land and water productivity differentiated by irrigation method (furrow, sprinkler and centre pivot) at the Xinavane sugarcane estate, Mozambique. The WaPOR data on water, land and climate is near-real-time and spatially distributed, with the finest spatial resolution in the area of 100 m. The WaPOR data were first validated agronomically by examining the biomass response to water, then the data was used to systematically analyse seasonal indicators for the period 2015 to 2018 on ~8,000 ha. The WaPOR based yield estimates were found to be comparable to the estate-measured yields with ±20 % difference, root mean square error of 19 ± 2.5 ton/ha and mean absolute error of 15 ± 1.6 ton/ha. A climate normalization factor that enables the spatial and temporal comparison of performance indicators are applied. The assessment highlights that in Xinavane no single irrigation method performs the best across all performance indicators. Centre pivot compared to sprinkler and furrow irrigation shows higher adequacy, equity, and land productivity, but lower water productivity. The three irrigation methods have excellent uniformity (~94 %) in the four seasons and acceptable adequacy for most periods of the season except in 2016, when a drought was observed. While this study is done for sugarcane in one irrigation scheme, the approach can be broadened to compare other crops across fields or irrigation schemes across Africa with diverse management units in the different agro-climatic zone within FaO WaPOR coverage. We conclude that the framework is useful for assessing irrigation performance using the WaPOR dataset.

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CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture

Remote Sensing ◽

10.3390/rs10121867 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1867 ◽

Cited By ~ 20

Author(s):

Bruno Aragon ◽

Rasmus Houborg ◽

Kevin Tu ◽

Joshua B. Fisher ◽

Matthew McCabe

Keyword(s):

Remote Sensing ◽

Eddy Covariance ◽

Water Use ◽

Spatial Resolution ◽

Precision Agriculture ◽

High Spatial Resolution ◽

Assessment Tools ◽

Optical Data ◽

Crop Water ◽

Crop Water Use

Remote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data regularly, or high spatial resolution occasionally. As a consequence, this spatiotemporal trade-off has tended to limit the impact of remote sensing for precision agricultural applications. With the recent emergence of constellations of small CubeSat-based satellite systems, these constraints are rapidly being removed, such that daily 3 m resolution optical data are now a reality for earth observation. Such advances provide an opportunity to develop new earth system monitoring and assessment tools. In this manuscript we evaluate the capacity of CubeSats to advance the estimation of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model. To take advantage of the high-spatiotemporal resolution afforded by these systems, we have integrated a CubeSat derived leaf area index as a forcing variable into PT-JPL, as well as modified key biophysical model parameters. We evaluate model performance over an irrigated farmland in Saudi Arabia using observations from an eddy covariance tower. Crop water use retrievals were also compared against measured irrigation from an in-line flow meter installed within a center-pivot system. To leverage the high spatial resolution of the CubeSat imagery, PT-JPL retrievals were integrated over the source area of the eddy covariance footprint, to allow an equivalent intercomparison. Apart from offering new precision agricultural insights into farm operations and management, the 3 m resolution ET retrievals were shown to explain 86% of the observed variability and provide a relative RMSE of 32.9% for irrigated maize, comparable to previously reported satellite-based retrievals. An observed underestimation was diagnosed as a possible misrepresentation of the local surface moisture status, highlighting the challenge of high-resolution modeling applications for precision agriculture and informing future research directions. .

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Using remote sensing to assess crop water productivity

SPIE Newsroom ◽

10.1117/2.1201002.002576 ◽

2010 ◽

Author(s):

Xueliang Cai

Keyword(s):

Remote Sensing ◽

Water Productivity ◽

Crop Water ◽

Crop Water Productivity

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Performance Evaluation of Sesame under Regulated Deficit Irrigation Application in the Low Land of Western Gondar, Ethiopia

International Journal of Agronomy ◽

10.1155/2020/3760349 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Sisay Ambachew Mekonnen ◽

Assefa Sintayehu

Keyword(s):

Deficit Irrigation ◽

Crop Production ◽

Water Productivity ◽

Irrigation Scheduling ◽

Growth Stages ◽

Crop Water ◽

Crop Water Productivity ◽

Moisture Deficit ◽

North Western ◽

Western Ethiopia

Sesame (Sesamum indicum L.) is the leading oil seed crop produced in Ethiopia. It is the second most important agricultural commodity for export market in the country. It is well suited as an alternative crop production system, and it has low crop water requirement with moderate resistance to soil moisture deficit. The low land of North Western Ethiopia is the major sesame producer in the country, and the entire production is from rainfed. The rainfall distribution in North Western Ethiopia is significantly varied. This significant rainfall variability hampers the productivity of sesame. Irrigation agriculture has the potential to stabilize crop production and mitigate the negative impacts of variable rainfall. This study was proposed to identify critical growth stages during which sesame is most vulnerable to soil moisture deficit and to evaluate the crop water productivity of sesame under deficit irrigation. The performance of sesame to stage-wise and uniform deficit irrigation scheduling technique was tested at Gondar Agricultural Research Center (Metema Station), Northern Western Ethiopia. Eight treatments, four stage-wise deficit, two uniform deficit, one above optimal, and one optimal irrigation applications, were evaluated during the 2017 irrigation season. The experiment was designed as a randomized complete block design with three replications. Plant phenological variables, grain yield and crop water productivity, were used for performance evaluation. The result showed that deficit irrigation can be applied both throughout and at selected growth stages except the midseason stage. Imposing deficit during the midseason gave the lowest yield indicating the severe effect of water deficit during flowering and capsule initiation stages. When deficit irrigation is induced throughout, a 25% uniform deficit irrigation can give the highest crop water productivity with no or little yield reduction as compared with optimal irrigation. Implementing deficit irrigation scheduling technique will be beneficial for sesame production. Imposing 75% deficit at the initial, development, late season growth stages or 25% deficit irrigation throughout whole seasons will improve sesame crop water productivity.

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Mapping Coastal Wetlands of the Bohai Rim at a Spatial Resolution of 10 m Using Multiple Open-Access Satellite Data and Terrain Indices

Remote Sensing ◽

10.3390/rs12244114 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4114

Author(s):

Shaobo Sun ◽

Yonggen Zhang ◽

Zhaoliang Song ◽

Baozhang Chen ◽

Yangjian Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Open Access ◽

Spatial Resolution ◽

Coastal Wetlands ◽

Coastal Wetland ◽

Regional Scale ◽

Google Earth ◽

Human Welfare ◽

Bohai Rim ◽

Terrain Indices

Coastal wetlands provide essential ecosystem services and are closely related to human welfare. However, they can experience substantial degradation, especially in regions in which there is intense human activity. To control these increasingly severe problems and to develop corresponding management policies in coastal wetlands, it is critical to accurately map coastal wetlands. Although remote sensing is the most efficient way to monitor coastal wetlands at a regional scale, it traditionally involves a large amount of work, high cost, and low spatial resolution when mapping coastal wetlands at a large scale. In this study, we developed a workflow for rapidly mapping coastal wetlands at a 10 m spatial resolution, based on the recently emergent Google Earth Engine platform, using a machine learning algorithm, open-access Synthetic Aperture Radar (SAR) and optical images from the Sentinel satellites, and two terrain indices. We then generated a coastal wetland map of the Bohai Rim (BRCW10) based on the workflow. It has a producer accuracy of 82.7%, according to validation using 150 wetland samples. The BRCW10 data reflected finer information when compared to wetland maps derived from two sets of global high-spatial-resolution land cover data, due to the fusion of multiple data sources. The study highlights the benefits of simultaneously merging SAR and optical remote sensing images when mapping coastal wetlands.

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Evaluating Irrigation Performance and Water Productivity Using EEFlux ET and NDVI

Sustainability ◽

10.3390/su13147967 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7967

Author(s):

Usha Poudel ◽

Haroon Stephen ◽

Sajjad Ahmad

Keyword(s):

Water Management ◽

Sugar Beet ◽

Crop Yield ◽

Water Conservation ◽

Irrigation System ◽

Water Productivity ◽

Google Earth ◽

Geographical Information ◽

Irrigation Performance ◽

Crop Water

Southern California’s Imperial Valley (IV) faces serious water management concerns due to its semi-arid environment, water-intensive crops and limited water supply. Accurate and reliable irrigation system performance and water productivity information is required in order to assess and improve the current water management strategies. This study evaluates the spatially distributed irrigation equity, adequacy and crop water productivity (CWP) for two water-intensive crops, alfalfa and sugar beet, using remotely sensed data and a geographical information system for the 2018/2019 crop growing season. The actual crop evapotranspiration (ETa) was mapped in Google Earth Engine Evapotranspiration Flux, using the linear interpolation method in R version 4.0.2. The approx() function in the base R was used to produce daily ETa maps, and then totaled to compute the ETa for the whole season. The equity and adequacy were determined according to the ETa’s coefficient of variation (CV) and relative evapotranspiration (RET), respectively. The crop classification was performed using a machine learning approach (a random forest algorithm). The CWP was computed as a ratio of the crop yield to the crop water use, employing yield disaggregation to map the crop yield, using county-level production statistics data and normalized difference vegetation index (NDVI) images. The relative errors (RE) of the ETa compared to the reported literature values were 7–27% for alfalfa and 0–3% for sugar beet. The average ETa variation was low; however, the spatial variation within the fields showed that 35% had a variability greater than 10%. The RET was high, indicating adequate irrigation; 31.5% of the alfalfa and 12% of the sugar beet fields clustered in the Valley’s central corner were consuming more water than their potential visibly. The CWP showed wide variation, with CVs of 32.92% for alfalfa and 25.4% for sugar beet, signifying a substantial scope for CWP enhancement. The correlation between the CWP, ETa and yield showed that reducing the ETa to approximately 1500 mm for alfalfa and 1200 mm for sugar beet would help boost the CWP without decreasing the yield, which is nearly equivalent to 44.52M cu. m (36,000 acre-ft) of water. The study’s results could help water managers to identify poorly performing fields where water conservation and management could be focused.

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Remote Sensing for Crop Water Management: from Experiments to User-Driven Services

10.20944/preprints201611.0095.v1 ◽

2016 ◽

Author(s):

Alfonso Calera ◽

Isidro Campos ◽

Anna Osann ◽

Guido D´Urso ◽

Massimo Menenti

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Meteorological Data ◽

Irrigation Scheduling ◽

Web Gis ◽

Optical Remote Sensing ◽

Crop Water ◽

Biophysical Parameters ◽

Water Requirements ◽

Crop Water Requirements

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e. professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

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