Remotely-sensed data are a source of rich information and are valuable for precision agricultural tasks such as soil quality, plant disease analysis, crop stress assessment, and allowing for better management. It is necessary to validate the accuracy of land surface temperature (LST) that is acquired from an unmanned aerial vehicle (UAV) and satellite-based remote sensing and verify these data by a comparison with in situ LST. Comprehensive studies at the field scale are still needed to understand the suitability of UAV imagery and resolution, for which ground measurement is used as a reference. In this study, we examined the accuracy of surface temperature data that were obtained from a thermal infrared (TIR) sensor placed on a UAV. Accordingly, we evaluated the LST from the Landsat 8 satellite for the same specific periods. We used contact thermometers to measure LSTs in situ for comparison and evaluation. Between 18 August and 2 September 2020, UAV imagery and in situ measurements were carried out. The effectiveness of high-resolution UAVs imagery and of Landsat 8 imagery was evaluated by considering a regression and correlation coefficient analysis. The data from the satellite photography was compared to the UAV imagery using statistical metrics after it had been pre-processed. Ground control points (GCPs) were collected to create a rigorous geo-referenced dataset of UAV imagery that could be compared to the geo-referenced satellite and aerial imagery. The UAV TIR LST showed higher accuracy (R² 0.89, 0.90, root-mean-square error (RMSE) 1.07, 0.70 °C) than the Landsat LST accuracy (R² 0.70, 0.73, (RMSE) 0.78 °C). The relationship between LST and the available soil water content (SWC) was also observed. The results suggested that the UAV-SMC correlation was negative (−0.85) for the image of DOY 230, while this value remains approximately constant (−0.86) for the DOY 245. Our results showed that satellite imagery that was coherent and correlated with UAV images could be useful to assess the general conditions of the field while the UAV favors localized circumscribed areas that the lowest resolution of satellites missed. Accordingly, our results could help with urban area and environmental planning decisions that take into account the thermal environment.

The challenge in the field of agricultural remote sensing monitoring has always been how to use crop growth models to quantitatively analyze the dynamic changes of regional summer maize biomass（referring to aboveground biomass weight，AGBW）. In this study, we constructed a summer maize biomass process simulation model (SM_SMBP) to analyze AGBW and its change characteristics at different growth stages of field summer maize in Jianghuai regional along the east coast of China. First, the initial biomass simulation model was used to estimate AGBW of summer maize from the seedling emergence to the jointing stage, from the jointing to the tasseling stage , and from the tasseling to the grain filling stage. Then, the biomass simulation model parameters were adjusted based on measured leaf area index and AGBW data at the jointing stage of summer maize. Finally, the adjusted model was used to estimate AGBW from jointing to tasseling and from tasseling to filling. The results showed that the relative errors of the predicted and measured values of summer maize AGBW from seedling emergence to before jointing, during the early stage of jointing, before tasseling, and during the grain filling stage were 3.35%, 11.56%, 23.26%, and 15.83%, respectively. For the adjusted model , the root mean square error (RMSE) between the predicted and the measured values before tasseling was 219.43 kg/hm² and the relative error was 4.18%, and the relative error between the predicted and the measured values during the grain filling stage was 3.44%. Adjusting the model parameters using data from the maize jointing stage improved the prediction from jointing to tasseling and from tasseling to grain filling. This study provides a reference for the prediction of AGBW and its dynamic changes in different growth stages of summer maize, and could assist agricultural management department to adjust production measures. Keywords: Biomass; Estimation; Planting areas along the east coast of China; Process simulation model; Summer maize

The Meiren yak is an important genetic resource in Gansu Province, China. In this study, we aimed to explore the evolutionary history and population structure of the genetic resource of Meiren yak and to mine the characteristic genes of Meiren yak. We analysed a total of 93 yaks of eight yak breeds based on whole genome resequencing combined with population genomics and used θπ ratio and Fst method to screen the selected sites in the genome region. The results proved that Meiren yak can be used as a potential genetic resource in Gansu Province. The genes in Meiren yak with positive selection in selection signal analysis were subjected to the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, which indicated that the genes were related to the adaptability to high altitude and hypoxic environment. By analysing the genetic variation of Meiren yak at the genome-wide level, this study provided a theoretical basis for genetic improvement of Meiren yak and for the development of high-quality yak resources.

Wheat scab (WS, Fusarium head blight), one of the most severe diseases of winter wheat in Yangtze-Huaihe river region, whose monitoring and timely forecasting at large scale would help to optimize pesticide spraying and achieve the purpose of reducing yield loss. In the present study, remote sensing monitoring on WS was conducted in 4 counties in Yangtze-Huaihe river region. Sensitive factors of WS were selected to establish the remote sensing estimation model of winter wheat scab index (WSI) based on interactions between spectral information and meteorological factors. The results showed that: 1) Correlations between the daily average temperature (DAT) and daily average relative humidity (DAH) at different time scales and WSI were significant. 2) There were positive linear correlations between winter wheat biomass, leaf area index (LAI), leaf chlorophyll content (LCC) and WSI. 3) NDVI (normalized difference vegetation index), RVI (ratio vegetation index) and DVI (difference vegetation index) which had a good correlation with LAI, biomass and LCC, respectively, and could be used to replace them in modeling. 4) The estimated values of the model were consistent with the measured values (RMSE = 5.3%, estimation accuracy = 90.46%). Estimation results showed that the model could efficiently estimate WS in Yangtze-Huaihe river region. Graphical

For landslide surface monitoring, the Global Navigation Satellite System (GNSS) has been widely used in landslides due to its real-time, all-weather, high-precision, simple operation and a high degree of automation. However, these data are not intuitive and visual data will be more interesting for users without professional knowledge. At the same time, the conventional data representation method is in the form of curves or tables for three-dimensional data of landslide surface deformation collected by GNSS. To make the data more intuitive, clear and valuable, it is easier for people to understand the process of landslide deformation and finally realize the visualization of decision. Here we show that a polar coordinate system rather than a Cartesian coordinate system is adopted to visualize the horizontal data, which not only shows the horizontal deformation of the landslide, but also easily knows the direction of the landslide deformation. The vertical data is in the form of slices rather than curves, which not only shows the deformation of the landslide surface, but also shows the process of the vertical change of the landslide in terms of the time series. Single GNSS monitoring station is composed of a GNSS receiver, GNSS antenna with random, a solar power unit, and a network transmission module. The system can be powered by the solar energy system, which can realize 24-hour unmanned operation, 7 days a week. The system can receive satellite signals in real-time process and analyze deformation data, then it automatically broadcast early warning information. Our results demonstrate that it is a better choice that the thematic map of Geographic Information System (GIS) is a technical system for collecting, storing, managing, calculating, analyzing and displaying geographic data supported by computer hardware and software systems. Here we show that the multi-dimensional properties of deformation monitoring and multiple expressions of the attribute values are displayed synchronously in order to obtain more useful information from the visual graphics.

Earthquake, flood, human activity, and rainfall are some of the trigger factors leading to landslides. Landslide monitoring data analysis indicates the deformation characteristics of landslides and helps to reduce the threat of landslide disasters. There are monitoring methods that enable efficient acquisition of real-time data to facilitate comprehensive research on landslides. However, it is challenging to analyze large amounts of monitoring data with problems like missing data and outlier data during data collection and transfer. These problems also hinder practical analysis and determination concerning the uncertain monitoring data. This work analyzes and processes the deformation characteristics of a rainfall-induced rotational landslide based on exploratory data analysis techniques. First, we found that the moving average denoising method is better than the polynomial fitting method for the repair and fitting of monitoring data. Besides, the exploratory data analysis of the Global Navigation Satellite System (GNSS) monitoring data reveals that the distribution of GNSS monitoring points has a positive correlation with the deformational characteristics of a rotational landslide. Our findings in the subsequent case study indicate that rainfalls are the primary trigger of the Zhutoushan landslide, Jiangsu Province, China. Therefore, this method provides support for the analysis of rotational landslides and more useful landslide monitoring information.