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","1332"],["dc.bibliographiccitation.firstpage","1332"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Nagler, Pamela L."],["dc.contributor.author","Barreto-Muñoz, Armando"],["dc.contributor.author","Chavoshi Borujeni, Sattar"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Jarchow, Christopher J."],["dc.contributor.author","Didan, Kamel"],["dc.date.accessioned","2021-06-15T16:48:33Z"],["dc.date.available","2021-06-15T16:48:33Z"],["dc.date.issued","2021"],["dc.description.abstract","Declines in riparian ecosystem greenness and water use have been observed in the delta of the Lower Colorado River (LCR) since 2000. The purpose of our case study was to measure these metrics on the U.S. side of the border between Hoover and Morelos Dams to see if declining greenness was unique to the portion of the river in Mexico. In this case study, five riparian reaches of the LCR from Hoover to Morelos Dam since 2000 were studied to evaluate trends in riparian ecosystem health. We measure these riparian woodlands using remotely sensed measurements of the two-band Enhanced Vegetation Index (EVI2; a proxy for greenness); daily evapotranspiration (ET; mmd−1) using EVI2 (ET(EVI2)); and an annualized ET based on EVI2, the Phenology Assessment Metric (PAM ET), an annualized ET using Landsat time-series. A key finding is that riparian health and its water use has been in decline since 2000 on the U.S. portion of the LCR, depicting a loss of green vegetation over the last two decades. EVI2 results show a decline of −13.83%, while average daily ET(EVI2) between the first and last decade had a decrease of over 1 mmd−1 (−27.30%) and the respective average PAM ET losses were 170.91 mmyr−1 (−17.95%). The difference between the first and last five-year periods, 2000–2005 and 2016–2020, showed the largest decrease in daily ET(EVI) of 1.24 mmd−1 (−32.61%). These declines come from a loss in healthy, green, riparian plant-cover, not a change in plant water use efficiency nor efficient use of managed water resources. Our results suggest further deterioration of biodiversity, wildlife habitat and other key ecosystem services on the U.S. portion of the LCR."],["dc.description.sponsorship","National Aeronautics and Space Administration"],["dc.description.sponsorship","USGS Ecosystems Mission Area"],["dc.identifier.doi","10.3390/rs13071332"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/87224"],["dc.identifier.url","https://publications.goettingen-research-online.de/handle/2/85322"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.publisher","MDPI"],["dc.relation.eissn","2072-4292"],["dc.relation.issn","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","Riparian Area Changes in Greenness and Water Use on the Lower Colorado River in the USA from 2000 to 2020"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","3183"],["dc.bibliographiccitation.issue","15"],["dc.bibliographiccitation.journal","Hydrological Processes"],["dc.bibliographiccitation.lastpage","3199"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Nagler, Pamela"],["dc.contributor.author","Chavoshi Borujeni, Sattar"],["dc.contributor.author","Barreto Munez, Armando"],["dc.contributor.author","Alaghmand, Sina"],["dc.contributor.author","Noori, Behnaz"],["dc.contributor.author","Galindo, Alejandro"],["dc.contributor.author","Didan, Kamel"],["dc.date.accessioned","2021-04-14T08:26:17Z"],["dc.date.available","2021-04-14T08:26:17Z"],["dc.date.issued","2020"],["dc.description.abstract","Urban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high-, medium- and coarse-spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI-based ET, over a 780-ha urban park in Adelaide, Australia. We validated ET with the ground-based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long-term changes of VIs and ET calculated from the different imagery acquired for this study (2011–2018). We found high correspondence between ET-MODIS and ET-Landsat (R2 > 0.99 for all VIs). Landsat-VIs captured the seasonal changes of greenness better than MODIS-VIs. We used artificial neural network (ANN) to relate the RS-ET and ground data, and ET-MODIS (EVI2) showed the highest correlation (R2 = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS-ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water-limited cities."],["dc.identifier.doi","10.1002/hyp.13790"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81889"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","John Wiley \\u0026 Sons, Inc."],["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.rights","This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited."],["dc.title","Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","1249"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","Water"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Khan, Tariq"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Booij, Martijn J."],["dc.contributor.author","Hoekstra, Arjen Y."],["dc.contributor.author","Khan, Hizbullah"],["dc.contributor.author","Ullah, Ihsan"],["dc.date.accessioned","2021-06-01T09:42:43Z"],["dc.date.available","2021-06-01T09:42:43Z"],["dc.date.issued","2021"],["dc.description.abstract","Pakistan possesses the fourth largest irrigation network in the world, serving 20.2 million hectares of cultivated land. With an increasing irrigated area, Pakistan is short of freshwater resources and faces severe water scarcity and food security challenges. This is the first comprehensive study on the water footprint (WF) of crop production in Peshawar Basin. WF is defined as the volume of freshwater required to produce goods and services. In this study, we assessed the blue and green water footprints (WFs) and annual blue and green water consumption of major crops (maize, rice, tobacco, wheat, barley, sugarcane, and sugar beet) in Peshawar Basin, Pakistan. The Global Water Footprint Assessment Standard (GWFAS) and AquaCrop model were used to model the daily WF of each crop from 1986 to 2015. In addition, the blue water scarcity, in the context of available surface water, and economic water productivity (EWP) of these crops were assessed. The 30 year average blue and green WFs of major crops revealed that maize had the highest blue and green WFs (7077 and 2744 m3/ton, respectively) and sugarcane had the lowest blue and green WFs (174 and 45 m3/ton, respectively). The average annual consumption of blue water by major crops in the basin was 1.9 billion m3, where 67% was used for sugarcane and maize, covering 48% of the cropland. The average annual consumption of green water was 1.0 billion m3, where 68% was used for wheat and sugarcane, covering 67% of the cropland. The WFs of all crops exceeded the global average. The results showed that annually the basin is supplied with 30 billion m3 of freshwater. Annually, 3 billion m3 of freshwater leaves the basin unutilized. The average annual blue water consumption by major crops is 31% of the total available surface water (6 billion m3) in the basin. Tobacco and sugar beet had the highest blue and green EWP while wheat and maize had the lowest. The findings of this study can help the water management authorities in formulating a comprehensive policy for efficient utilization of available water resources in Peshawar Basin."],["dc.identifier.doi","10.3390/w13091249"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85331"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.publisher","MDPI"],["dc.relation.eissn","2073-4441"],["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","Water Footprint, Blue Water Scarcity, and Economic Water Productivity of Irrigated Crops in Peshawar Basin, Pakistan"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","103613"],["dc.bibliographiccitation.journal","Landscape and Urban Planning"],["dc.bibliographiccitation.volume","190"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Chavoshi Borujeni, Sattar"],["dc.contributor.author","Hoekstra, Arjen Y."],["dc.date.accessioned","2020-12-10T15:20:14Z"],["dc.date.available","2020-12-10T15:20:14Z"],["dc.date.issued","2019"],["dc.description.abstract","The development of ‘greening’ cities introduces an uneasy tension between more green spaces and the increased use of scarce blue water resources to maintain this greenness, particularly in dry regions. This paper presents the first estimate of the blue water footprint (WF) of urban greenery. We estimated total water consumption of a 10-hectare parkland in Adelaide, South Australia. Evapotranspiration of the urban vegetation was estimated by monitoring soil water inflows, outflows, and storage changes at an experimental site representing different species, microclimates, and plant densities, the most critical parameters affecting water use. The total WF was estimated at 11,140 m3/ha per year, 59% from blue water (irrigation), and 41% from green water (rainwater), with the highest water consumption in summer. The dependency on blue water resources for maintaining the greenery varied from 49% in October to 67% in March. Even in the wet period of the year, there was a significant blue WF. Given the lack of blue water resources to allocate for further greening the city in an arid environment, we suggest an integrated adaptive management strategy to maintain available greenery and expand green spaces with a minimum of extra pressure on blue water resources."],["dc.identifier.doi","10.1016/j.landurbplan.2019.103613"],["dc.identifier.issn","0169-2046"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16814"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72589"],["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-NC-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/4.0"],["dc.title","The blue water footprint of urban green spaces: An example for Adelaide, Australia"],["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","105781"],["dc.bibliographiccitation.journal","Agricultural Water Management"],["dc.bibliographiccitation.volume","226"],["dc.contributor.author","Martínez-Nicolás, J.J."],["dc.contributor.author","Galindo, A."],["dc.contributor.author","Griñán, I."],["dc.contributor.author","Rodríguez, P."],["dc.contributor.author","Cruz, Z.N."],["dc.contributor.author","Martínez-Font, R."],["dc.contributor.author","Carbonell-Barrachina, A.A."],["dc.contributor.author","Nouri, H."],["dc.contributor.author","Melgarejo, P."],["dc.date.accessioned","2020-12-10T14:22:19Z"],["dc.date.available","2020-12-10T14:22:19Z"],["dc.date.issued","2019"],["dc.description.abstract","Adult pomegranate trees [Punica granatum (L.) cultivars Wonderful and Mollar de Elche] were submitted to different irrigation treatments during the 2015 and 2016 seasons. Control (T0) plants were drip irrigated to guarantee non-limiting soil water conditions; T1 plants were subjected to water withholding during flowering-fruit set period for 64 (2015) and 53 (2016) days, resuming irrigation as in T0 plants subsequently. In both cultivars, the results demonstrated that during the flowering-fruit set period the sensitivity to water stress is very small, being possible to suppress or reduce irrigation (at least, while plant water status maintains similar levels to those reported in this study) without affecting marketable yield and fruit size and composition. Thus, this period can be considered as a clearly non-critical period. In this sense, water saving was around 19–30% and water productivity (WP) increased around 4–14% in Wonderful and 10–16% in Mollar de Elche. Water stress increased flowering, but not the number of viable hermaphrodite flowers, and decreased shoot growth (TSG), which could favour a compensatory young fruit growth when irrigation was resumed due to a shift in the carbon allocation pattern. This WP increase, the reduction in pruning cost (TSG decrease), and the redder arils in T1 plants were key aspects to increase consumers’ acceptance and farmers’ crop revenues."],["dc.identifier.doi","10.1016/j.agwat.2019.105781"],["dc.identifier.issn","0378-3774"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17311"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/71579"],["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-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nd/4.0"],["dc.title","Irrigation water saving during pomegranate flowering and fruit set period do not affect Wonderful and Mollar de Elche cultivars yield and fruit composition"],["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","5167"],["dc.bibliographiccitation.issue","24"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","13"],["dc.contributor.affiliation","Abbasi, Neda; 1Department of Crop Sciences, University of Göttingen, Von-Siebold-Straße 8, 37075 Göttingen, Germany; hamideh.nouri@uni-goettingen.de (H.N.); stefan.siebert@uni-goettingen.de (S.S.)"],["dc.contributor.affiliation","Nouri, Hamideh; 1Department of Crop Sciences, University of Göttingen, Von-Siebold-Straße 8, 37075 Göttingen, Germany; hamideh.nouri@uni-goettingen.de (H.N.); stefan.siebert@uni-goettingen.de (S.S.)"],["dc.contributor.affiliation","Didan, Kamel; 3Biosystems Engineering, The University of Arizona, 1177 E. 4th St., Tucson, AZ 85719, USA; didan@arizona.edu (K.D.); abarreto@arizona.edu (A.B.-M.)"],["dc.contributor.affiliation","Barreto-Muñoz, Armando; 3Biosystems Engineering, The University of Arizona, 1177 E. 4th St., Tucson, AZ 85719, USA; didan@arizona.edu (K.D.); abarreto@arizona.edu (A.B.-M.)"],["dc.contributor.affiliation","Chavoshi Borujeni, Sattar; 4Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan 19395-1113, Iran; Sattar.Chavoshiborujeni@student.uts.edu.au"],["dc.contributor.affiliation","Salemi, Hamidreza; 6Agricultural Engineering Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan 19395-1113, Iran; hrsalemiwk@gmail.com"],["dc.contributor.affiliation","Opp, Christian; 2Department of Geography, Philipps-Universität Marburg, Deutschhausstraße 10, 35032 Marburg, Germany; opp@geo.uni-marburg.de"],["dc.contributor.affiliation","Siebert, Stefan; 1Department of Crop Sciences, University of Göttingen, Von-Siebold-Straße 8, 37075 Göttingen, Germany; hamideh.nouri@uni-goettingen.de (H.N.); stefan.siebert@uni-goettingen.de (S.S.)"],["dc.contributor.affiliation","Nagler, Pamela; 7U.S. Geological Survey, Southwest Biological Science Center, 520 N. Park Avenue, Tucson, AZ 85719, USA; pnagler@usgs.gov"],["dc.contributor.author","Abbasi, Neda"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Didan, Kamel"],["dc.contributor.author","Barreto-Muñoz, Armando"],["dc.contributor.author","Chavoshi Borujeni, Sattar"],["dc.contributor.author","Salemi, Hamidreza"],["dc.contributor.author","Opp, Christian"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Nagler, Pamela"],["dc.contributor.editor","Doughty, Russell"],["dc.contributor.editor","Bajgain, Rajen"],["dc.contributor.editor","Wang, Jie"],["dc.date.accessioned","2022-01-26T16:33:57Z"],["dc.date.available","2022-01-26T16:33:57Z"],["dc.date.issued","2021-12-20"],["dc.date.updated","2022-02-09T13:20:04Z"],["dc.description.abstract","Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns\\’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVIMccL and EVI2MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption."],["dc.identifier.doi","10.3390/rs13245167"],["dc.identifier.eissn","2072-4292"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/98675"],["dc.language.iso","en"],["dc.publisher","MDPI"],["dc.relation.doi","10.3390/rs13245167"],["dc.relation.issn","2072-4292"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.rights","Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)."],["dc.subject.gro","actual evapotranspiration"],["dc.subject.gro","google earth engine"],["dc.subject.gro","harvested area"],["dc.subject.gro","enhanced vegetation index"],["dc.subject.gro","cross-sensor transformation"],["dc.title","Estimating actual evapotranspiration over croplands using vegetation index methods and dynamic harvested area"],["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","4716"],["dc.bibliographiccitation.issue","22"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Zhu, Wanxue"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Yang, Ting"],["dc.contributor.author","Li, Binbin"],["dc.contributor.author","Gong, Huarui"],["dc.contributor.author","Lyu, Yun"],["dc.contributor.author","Peng, Jinbang"],["dc.contributor.author","Sun, Zhigang"],["dc.contributor.editor","Zhu, Wenquan"],["dc.date.accessioned","2022-01-11T14:07:54Z"],["dc.date.available","2022-01-11T14:07:54Z"],["dc.date.issued","2021"],["dc.description.abstract","Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes."],["dc.description.abstract","Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes."],["dc.identifier.doi","10.3390/rs13224716"],["dc.identifier.pii","rs13224716"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97887"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-507"],["dc.publisher","MDPI"],["dc.relation.eissn","2072-4292"],["dc.rights","Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)."],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]