Crop planting area and spatial distribution information have important practical significance for food security, global change, and sustainable agricultural development. How to efficiently and accurately identify crops in a timely manner by remote sensing in order to determine the crop planting area and its temporal–spatial dynamic change information is a core issue of monitoring crop growth and estimating regional crop yields. Based on hundreds of relevant documents from the past 25 years, in this paper, we summarize research progress in relation to farmland vegetation identification and classification by remote sensing. The classification and identification of farmland vegetation includes classification based on vegetation index, spectral bands, multi-source data fusion, artificial intelligence learning, and drone remote sensing. Representative studies of remote sensing methods are collated, the main content of each technology is summarized, and the advantages and disadvantages of each method are analyzed. Current problems related to crop remote sensing identification are then identified and future development directions are proposed.

Winter extreme low temperature events have been occurring frequently both before and after the winter season. The freezing resistance temperature of wheat is far lower than the intensity of low temperatures during the mid-winter period. Therefore, it is necessary to further quantify and evaluate the impact of low-temperature periods and durations during the early winter and the green-up period on the freezing resistance of wheat, based on different evaluation indicators. Through conducting experiments in an artificial low-temperature control chamber, this study investigates the critical temperature thresholds for the impact of different low-temperature periods and durations on the tiller and yield of winter wheat, as well as the critical temperature thresholds for soil effective negative accumulated temperature. The results demonstrate that (1) the tiller mortality rate (R_T) and yield reduction rate (R_Y) of winter wheat during the winter increase with the severity and duration of low temperatures, showing an S-shaped curve. The winter wheat mortality rate during the early winter is related to the soil effective negative accumulated temperature in an exponential function, while the mid-winter and green-up stages have a linear relationship. (2) The freezing threshold temperatures for the R_T, R_Y and soil negative accumulated temperature (SENAT) in different low-temperature periods (early winter, mid-winter, and green-up periods) range from − 11.7 to -17.9 °C, -9.4 to -15.6 °C, and 15.9 to 131.7 °C·h (2.2 to 16.8 °C·d), respectively. (3) The freezing threshold temperatures for the R_T and R_Y in different low-temperature durations (1 day, 2 days, and 3 days) range from − 2.8 to -17.9 °C and − 9.4 to -15.6 °C, respectively. The findings of this study provide technical support and scientific guidance for the global cultivation structure and variety layout of winter wheat under the background of climate warming, as well as for the prevention and reduction of freezing damage and yield losses.

A forest is vulnerable to drought and plays important roles in the regulation of carbon and water cycling in a terrestrial ecosystem. Atmospheric vapor pressure deficit (VPD) has been identified as an increasingly major factor in plant functioning and has been established as a main contributor to recent drought-induced plant mortality, independent of other drivers associated with climate change. However, most previous studies have focused on the effects of climate warming and CO₂ enrichment on vegetation growth, without considering the effects of an increased VPD on vegetation growth and evapotranspiration (ET) in forest ecosystems. This could lead to a large uncertainty in estimating the variability in forest carbon sinks. Based on the long-term satellite data, we investigated the response of the leaf area index (LAI) and ET to the VPD via a partial correlation analysis in this study. We also examined the temporal variability in the partial coefficients within a ten-year moving window. The results showed that over 50% of the region displayed a negative partial correlation between the LAI, ET, and VPD, and those pixels were mainly concentrated in North America and the plains of Eastern Europe. Regions with a negative trend of partial correlation in both the LAI and ET are mostly located in the plains of Eastern Europe and the Siberian Plain of western Russia, while the positive trend is mainly in South America. The plains of Eastern Europe are becoming drier, which was proved by the interannual trend of the Standardized Precipitation Evapotranspiration Index (SPEI) and soil water content (SWC). Additionally, the LAI and ET in those areas exhibited a significant positive correlation with the SWC based on the moving window average. This study suggests that the role of the VPD on vegetation will become increasingly prominent in the context of future climate change for the forest ecosystem.

Climate change has had a significant impact on agricultural production. It is important to evaluate the vulnerability of agricultural production to climate change. The previous methods for evaluating vulnerability are inconsiderate and unrealistic. This paper proposes an improved vulnerability assessment method, introduces the Agricultural Production System Simulator (APSIM)-wheat model to evaluate vulnerability, and uses spring wheat, in Inner Mongolia, China, as an example for evaluating the vulnerability of spring wheat under climate change. The results show that, from 1996 to 2015, the adaptability to climate change of spring wheat production, in Inner Mongolia, increased, and its sensitivity to climate change decreased. That is to say, that climatic conditions have a negative impact on spring wheat, and adaptation measures have a positive impact on spring wheat. From 1996 to 2009, the vulnerability of spring wheat production in Inner Mongolia showed a very significant increasing trend, while showing a significant downward trend during 2009–2015, which is consistent with the actual situation. The improved vulnerability assessment method can reflect the actual impact of climatic conditions on agricultural production. We expect that the new vulnerability assessment method can provide a theoretical basis for studying the impact of climate change on agricultural production.

In order to investigate the stage plant architecture and productivity characteristics of different cucumber varieties and pruning methods and to construct a comprehensive productivity evaluation system based on plant architecture parameters, this study used JY35 and JS206 as experiment materials and conducted a dual factor control experiment with variety and pruning methods (single-stem pruning (SP) and natural growth (NG)) to systematically analyze the key phenotypic characteristics and productivity indicators of cucumbers at different developmental stages based on variance analysis and principal component evaluation. The results indicated the following: (1) Varieties and pruning methods have a significant impact on the plant architecture characteristics and productivity indicators. (2) The dominant plant architecture characteristics and productivity indicators of JY35 include dry and fresh weights of the tendril, main stem, total stem, leaves, petioles, flowers, overground parts, and overall plant, as well as dry and fresh weight distribution index of the tendril, total stem, leaves, petioles, flowers, overground parts, and overall plant, main stem fresh weight distribution index, water content of roots, tendrils, main stem, leaves, petioles, and flowers, volume of total stem, main stem, and petioles, plant height, total leaf area per plant, leaf area index, and specific leaf area. The remaining plant architecture characteristics and productivity indicators are dominated by the plant architecture of JS206. (3) The dominant plant architecture characteristics and productivity indicators of the SP method include dry and fresh weight distribution index of roots, fruit carpopodiums, main stems, and total stems, water content of petioles, stems, and leaves, and root-to-shoot ratio. The remaining plant architecture characteristics and productivity indicators are dominated by the NG method. This study quantified the dynamic correlation between cucumber plant architecture and productivity characteristics, and the research results can provide a morphological basis for facility cucumber variety breeding and theoretical support for optimizing pruning cultivation mode and achieving efficient utilization of light and heat resources.

Cucumber (Cucumis sativus L.) and celery (Apium graveolens L.) are among the most widely cultivated vegetable crops, belonging to the melon and leafy vegetable categories, respectively. This study aims to provide predictions for the quality formation of greenhouse cucumber and celery, as well as technical support for intelligent vegetable production management. Based on the light and temperature response characteristics of cucumber and celery growth and development and using the experimental varieties ‘Jinsheng 206’ for cucumber and ‘Juventus’ for celery, the observational data from a five-cropping season trial over 2 years were utilized. By analyzing the relationship between the growth and development of vegetables and key meteorological factors (radiation and temperature), simulation models for quality formation were constructed using the day–night temperature difference method (ATD) and the product of thermal effectiveness and the photosynthetically active method (TEP) as independent variables. The model parameters were determined and the model was validated using independent trial data. The results showed the following: (1) There exist quantifiable relationships between different quality indicators and ATD or TEP. These relationships are mainly presented through linear functions, exponential functions, logarithmic function, and logical functions. (2) The normalized root mean square error (NRMSE) of the cucumber quality model ranges from 1.13% to 29.53%, and the NRMSE of the celery quality model ranges from 1.63% to 31.47%. (3) Based on two kinds of normalization methods, the average NRMSE of the VQSM model is 13.72%, demonstrating a relatively high level of accuracy in simulation. These results demonstrate that the proposed model can dynamically and accurately simulate the quality formation process of vegetables, providing a theoretical basis and data support for the research on productivity and the economic benefits of greenhouse crops.