Nouri, Hamideh

[["dc.bibliographiccitation.artnumber","414"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","ISPRS International Journal of Geo-Information"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Karimi, Poolad"],["dc.contributor.author","Bastiaanssen, W. G. M."],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Blatchford, Megan Leigh"],["dc.date.accessioned","2019-07-09T11:50:21Z"],["dc.date.available","2019-07-09T11:50:21Z"],["dc.date.issued","2018"],["dc.description.abstract","Crop water productivity (CWP) has become a recognised indicator in assessing the state of Sustainable Development Goals (SDG) 6.4—to substantially increase water use efficiency. This indicator, while useful at a global scale, is not comprehensive at a local scale. To fill this gap, this research proposes a CWP framework, that takes advantage of the spatio-temporal availability of remote sensing, that identifies CWP goals and sub-indicators specific to the needs of the targeted domain. Three sub-indicators are considered; (i) a global water productivity score (GWPS), (ii) a local water productivity score (LWPS) and (iii) a land and water use productivity score (YWPS). The GWPS places local CWP in the global context and focuses on maximised CWP. The LWPS differentiates yield zones, normalising for potential product, and focuses on minimising water consumption. The YWPS focuses simultaneously on improving land and water productivity equally. The CWP framework was applied to potato in the West Bank, Palestine. Three management practices were compared under each sub-indicator. The case study showed that fields with high and low performance were different under each sub-indicator. The performance associated with different management practices was also different under each sub-indicator. For example, a winter rotation had a higher performance under the YWPS, the fall rotation had a higher performance under the LWPS and under the GWPS there was little difference. The results showed, that depending on the basin goal, not only do the sub-indicators required change, but also the management practices or approach required to reach those basin goals. This highlights the importance of providing a CWP framework with multiple sub-indicators, suitable to basin needs, to ensure that meeting the SDG 6.4 goal does not jeopardise local objectives."],["dc.identifier.doi","10.3390/ijgi7110414"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15919"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59755"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/606838/EU//EWPFP COFUND"],["dc.relation.eissn","2220-9964"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","630"],["dc.title","From Global Goals to Local Gains—A Framework for Crop Water Productivity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","2949"],["dc.bibliographiccitation.issue","18"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Blatchford, Megan"],["dc.contributor.author","M. Mannaerts, Chris"],["dc.contributor.author","Zeng, Yijian"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Karimi, Poolad"],["dc.date.accessioned","2021-04-14T08:32:28Z"],["dc.date.available","2021-04-14T08:32:28Z"],["dc.date.issued","2020"],["dc.description.abstract","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."],["dc.identifier.doi","10.3390/rs12182949"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83932"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","MDPI"],["dc.relation.eissn","2072-4292"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Influence of Spatial Resolution on Remote Sensing-Based Irrigation Performance Assessment Using WaPOR Data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","111413"],["dc.bibliographiccitation.journal","Remote Sensing of Environment"],["dc.bibliographiccitation.volume","234"],["dc.contributor.author","Blatchford, Megan L."],["dc.contributor.author","Mannaerts, Chris M."],["dc.contributor.author","Zeng, Yijian"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Karimi, Poolad"],["dc.date.accessioned","2020-12-10T15:21:08Z"],["dc.date.available","2020-12-10T15:21:08Z"],["dc.date.issued","2019"],["dc.description.abstract","The scarcity of water and the growing global food demand has fevered the debate on how to increase agricultural production without further depleting water resources. Crop water productivity (CWP) is a performance indicator to monitor and evaluate water use efficiency in agriculture. Often in remote sensing datasets of CWP and its components, i.e. crop yield or above ground biomass production (AGBP) and evapotranspiration (ETa), the end-users and developers are different actors. The accuracy of the datasets should therefore be clear to both users and developers. We assess the accuracy of remotely sensed CWP against the accuracy of estimated in-situ CWP. First, the accuracy of CWP based on in-situ methods, which are assumed to be the user's benchmark for CWP accuracy, is reviewed. Then, the accuracy of current remote sensing products is described to determine if the accuracy benchmark, as set by in-situ methods, can be met with current algorithms. The percentage error of CWP from in-situ methods ranges from 7% to 67%, depending on method and scale. The error of CWP from remote sensing ranges from 7% to 22%, based on the highest reported performing remote sensing products. However, when considering the entire breadth of reported crop yield and ETa accuracy, the achievable errors propagate to CWP ranges of 74% to 108%. Although the remote sensing CWP appears comparable to the accuracy of in-situ methods in many cases, users should determine whether it is suitable for their specific application of CWP."],["dc.identifier.doi","10.1016/j.rse.2019.111413"],["dc.identifier.issn","0034-4257"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17065"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72927"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Status of accuracy in remotely sensed and in-situ agricultural water productivity estimates: A review"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","3200"],["dc.bibliographiccitation.issue","15"],["dc.bibliographiccitation.journal","Hydrological Processes"],["dc.bibliographiccitation.lastpage","3221"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Blatchford, Megan L."],["dc.contributor.author","Mannaerts, Chris M."],["dc.contributor.author","Njuki, Sammy M."],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Zeng, Yijian"],["dc.contributor.author","Pelgrum, Henk"],["dc.contributor.author","Wonink, Steven"],["dc.contributor.author","Karimi, Poolad"],["dc.date.accessioned","2021-04-14T08:26:17Z"],["dc.date.available","2021-04-14T08:26:17Z"],["dc.date.issued","2020"],["dc.description.abstract","The Food and Agricultural Organization of the United Nations (FAO) portal to monitor water productivity through open-access of remotely sensed derived data (WaPOR) offers continuous actual evapotranspiration and interception (ETIa-WPR) data at a 10-day basis across Africa and the Middle East from 2009 onwards at three spatial resolutions. The continental level (250 m) covers Africa and the Middle East (L1). The national level (100 m) covers 21 countries and 4 river basins (L2). The third level (30 m) covers eight irrigation areas (L3). To quantify the uncertainty of WaPOR version 2 (V2.0) ETIa-WPR in Africa, we used a number of validation methods. We checked the physical consistency against water availability and the long-term water balance and then verify the continental spatial and temporal trends for the major climates in Africa. We directly validated ETIa-WPR against in situ data of 14 eddy covariance stations (EC). Finally, we checked the level consistency between the different spatial resolutions. Our findings indicate that ETIa-WPR is performing well, but with some noticeable overestimation. The ETIa-WPR is showing expected spatial and temporal consistency with respect to climate classes. ETIa-WPR shows mixed results at point scale as compared to EC flux towers with an overall correlation of 0.71, and a root mean square error of 1.2 mm/day. The level consistency is very high between L1 and L2. However, the consistency between L1 and L3 varies significantly between irrigation areas. In rainfed areas, the ETIa-WPR is overestimating at low ETIa-WPR and underestimating when ETIa is high. In irrigated areas, ETIa-WPR values appear to be consistently overestimating ETa. The relative soil moisture content (SMC), the input of quality layers and local advection effects were some of the identified causes. The quality assessment of ETIa-WPR product is enhanced by combining multiple evaluation methods. Based on the results, the ETIa-WPR dataset is of enough quality to contribute to the understanding and monitoring of local and continental water processes and water management."],["dc.identifier.doi","10.1002/hyp.13791"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81888"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1099-1085"],["dc.relation.issn","0885-6087"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.title","Evaluation of WaPOR V2 evapotranspiration products across Africa"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]