Nazari, Meisam

[["dc.bibliographiccitation.artnumber","806146"],["dc.bibliographiccitation.journal","Frontiers in Agronomy"],["dc.bibliographiccitation.volume","3"],["dc.contributor.affiliation","Nazari, Meisam; 1Division of Biogeochemistry of Agroecosystems, Georg-August University of Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Mirgol, Behnam; 2Center for Agroecology, Water and Resilience, Coventry University, Coventry, United Kingdom"],["dc.contributor.affiliation","Salehi, Hamid; 3Department of Water Engineering, Tarbiat Modares University, Tehran, Iran"],["dc.contributor.author","Nazari, Meisam"],["dc.contributor.author","Mirgol, Behnam"],["dc.contributor.author","Salehi, Hamid"],["dc.date.accessioned","2022-07-13T07:38:01Z"],["dc.date.available","2022-07-13T07:38:01Z"],["dc.date.issued","2021"],["dc.date.updated","2022-09-04T22:36:08Z"],["dc.description.abstract","This is the first large-scale study to assess the climate change impact on the grain yield of rainfed wheat for three provinces of contrasting climatic conditions (temperate, cold semi-arid, and hot arid) in Iran. Five integrative climate change scenarios including +0.5°C temperature plus−5% precipitation, +1°C plus−10%, +1.5°C plus−15%, +2°C plus−20%, and +2.5°C plus−25% were used and evaluated. Nitrogen fertilizer and shifting planting dates were tested for their suitability as adaptive strategies for rainfed wheat against the changing climate. The climate change scenarios reduced the grain yield by −6.9 to −44.8% in the temperate province Mazandaran and by −7.3 to −54.4% in the hot arid province Khuzestan but increased it by +16.7% in the cold semi-arid province Eastern Azarbaijan. The additional application of +15, +30, +45, and +60 kg ha−1 nitrogen fertilizer as urea at sowing could not, in most cases, compensate for the grain yield reductions under the climate change scenarios. Instead, late planting dates in November, December, and January enhanced the grain yield by +6 to +70.6% in Mazandaran under all climate change scenarios and by +94 to +271% in Khuzestan under all climate change scenarios except under the scenario +2.5°C temperature plus−25% precipitation which led to a grain yield reduction of −85.5%. It is concluded that rainfed wheat production in regions with cold climates can benefit from the climate change, but it can be impaired in temperate regions and especially in vulnerable hot regions like Khuzestan. Shifting planting date can be regarded as an efficient yield-compensating and environmentally friendly adaptive strategy of rainfed wheat against the climate change in temperate and hot arid regions."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3389/fagro.2021.806146"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112481"],["dc.language.iso","en"],["dc.relation.eissn","2673-3218"],["dc.relation.issn","2673-3218"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Climate Change Impact Assessment and Adaptation Strategies for Rainfed Wheat in Contrasting Climatic Regions of Iran"],["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","795287"],["dc.bibliographiccitation.journal","Frontiers in Environmental Science"],["dc.bibliographiccitation.volume","9"],["dc.contributor.affiliation","Salehi, Hamid; \r\n\r\n1\r\nDepartment of Water Engineering, Tarbiat Modares University, Tehran, Iran"],["dc.contributor.affiliation","Shamsoddini, Ali; \r\n\r\n2\r\nDepartment of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran"],["dc.contributor.affiliation","Mirlatifi, Seyed Majid; \r\n\r\n1\r\nDepartment of Water Engineering, Tarbiat Modares University, Tehran, Iran"],["dc.contributor.affiliation","Mirgol, Behnam; \r\n\r\n3\r\nCenter for Agroecology, Water and Resilience, Coventry University, Coventry, United Kingdom"],["dc.contributor.affiliation","Nazari, Meisam; \r\n\r\n4\r\nDivision of Biogeochemistry of Agroecosystems, Georg-August University of Göttingen, Göttingen, Germany"],["dc.contributor.author","Salehi, Hamid"],["dc.contributor.author","Shamsoddini, Ali"],["dc.contributor.author","Mirlatifi, Seyed Majid"],["dc.contributor.author","Mirgol, Behnam"],["dc.contributor.author","Nazari, Meisam"],["dc.date.accessioned","2022-02-01T10:31:38Z"],["dc.date.available","2022-02-01T10:31:38Z"],["dc.date.issued","2021"],["dc.date.updated","2022-02-09T13:20:15Z"],["dc.description.abstract","Producing daily actual evapotranspiration (ET a ) maps with high spatial resolution has always been a challenge for remote sensing research. This study assessed the feasibility of producing daily ET a maps with a high spatial resolution (30 m) for the sugarcane farmlands of Amir Kabir Sugarcane Agro-industry (Khuzestan, Iran) using three different scenarios. In the first scenario, the reflectance bands of Landsat 8 were predicted from the moderate resolution imaging spectroradiometer (MODIS) imagery using the spatial and temporal adaptive reflectance fusion model (STARFM) algorithm. Also, the thermal bands of Landsat 8 were predicted by the spatiotemporal adaptive data fusion algorithm for temperature mapping (SADFAT). Then, ET a amounts were calculated employing such bands and the surface energy balance algorithm for land (SEBAL). In the second scenario, the input data needed by SEBAL were downscaled using the MODIS images and different methods. Then, using the downscaled data and SEBAL, daily ET a amounts with a spatial resolution of 30 m were calculated. In the third scenario, ET a data acquired by MODIS were downscaled to the scale of Landsat 8. In the second and third scenarios, downscaling of the data was carried out by the ratio, regression, and neural networks methods with two different approaches. In the first approach, the Landsat image on day 1 and the relationship between the two MODIS images on day 1 and the other days were used. In the second approach, the simulated image on the previous day and the relationship between the two consecutive images of MODIS were used. Comparing the simulated ET a amounts with the ET a amounts derived from Landsat 8, the first scenario had the best result with an RMSE (root mean square error) of 0.68 mm day −1 . The neural networks method used in the third scenario with the second approach had the worst result with an RMSE of 2.25 mm day −1 , which was however a better result than the ET a amounts derived from MODIS with an RMSE of 3.19 mm day −1 . The method developed in this study offers an efficient and inexpensive way to produce daily ET a maps with a high spatial resolution. Furthermore, we suggest that STARFM and SADFAT algorithms have acceptable accuracies in the simulation of reflectance and thermal bands of Landsat 8 images for homogeneous areas."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3389/fenvs.2021.795287"],["dc.identifier.eissn","2296-665X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/98911"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-517"],["dc.relation.eissn","2296-665X"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Spatial and Temporal Resolution Improvement of Actual Evapotranspiration Maps Using Landsat and MODIS Data Fusion"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]