Abstract:Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system-three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px −1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m 2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R 2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R 2 values with the best model obtained at flowering (R 2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.

No abstract

No abstract

NAS RK is pleased to announce that News of NAS RK. Series of geology and technical sciences scientific journal has been accepted for indexing in the Emerging Sources Citation Index, a new edition of Web of Science. Content in this index is under consideration by Clarivate Analytics to be accepted in the Science Citation Index Expanded, the Social Sciences Citation Index, and the Arts & Humanities Citation Index. The quality and depth of content Web of Science offers to researchers, authors, publishers, and institutions sets it apart from other research databases. The inclusion of News of NAS RK. Series of geology and technical sciences in the Emerging Sources Citation Index demonstrates our dedication to providing the most relevant and influential content of geology and engineering sciences to our community. Қазақстан Республикасы Ұлттық ғылым академиясы "ҚР ҰҒА Хабарлары. Геология жəне техникалық ғылымдар сериясы" ғылыми журналының Web of Science-тің жаңаланған нұсқасы Emerging Sources Citation Index-те индекстелуге қабылданғанын хабарлайды. Бұл индекстелу барысында Clarivate Analytics компаниясы журналды одан əрі the Science Citation Index Expanded, the Social Sciences Citation Index жəне the Arts & Humanities Citation Index-ке қабылдау мəселесін қарастыруда. Webof Science зерттеушілер, авторлар, баспашылар мен мекемелерге контент тереңдігі мен сапасын ұсынады. ҚР ҰҒА Хабарлары. Геология жəне техникалық ғылымдар сериясы Emerging Sources Citation Index-ке енуі біздің қоғамдастық үшін ең өзекті жəне беделді геология жəне техникалық ғылымдар бойынша контентке адалдығымызды білдіреді. НАН РК сообщает, что научный журнал «Известия НАН РК. Серия геологии и технических наук» был принят для индексирования в Emerging Sources Citation Index, обновленной версии Web of Science. Содержание в этом индексировании находится в стадии рассмотрения компанией Clarivate Analytics для дальнейшего принятия журнала в the Science Citation Index Expanded, the Social Sciences Citation Index и the Arts & Humanities Citation Index. Web of Science предлагает качество и глубину контента для исследователей, авторов, издателей и учреждений. Включение Известия НАН РК. Серия геологии и технических наук в Emerging Sources Citation Index демонстрирует нашу приверженность к наиболее актуальному и влиятельному контенту по геологии и техническим наукам для нашего сообщества.

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.