Atmospheric vapor pressure deficit (VPD, indicative of atmospheric water conditions) has been identified as a major driver of global vegetation dynamics. Drylands, including deserts, temperate grasslands, savannas, and dry forests, are more sensitive to water conditions and affect carbon, nitrogen, and water cycles. However, our knowledge is limited on the way increasing VPD affects vegetation growth and evapotranspiration (ET) in global drylands. In this study, we used long-term satellite datasets combined with multiple statistical analyses to examine the relationship between the satellite-derived normalized difference vegetation index (NDVI), a proxy for vegetation growth, and ET to VPD across global drylands. We found that significant decreases in NDVI and ET predominantly influenced the NDVI (RVPD − NDVI) and ET (RVPD − ET) responses to VPD in both the savannas and dry forests of South American, African, and Australian savannas and dry forests, as well as in temperate grasslands (e.g., Eurasian steppes and American prairies). Notably, more than 60% of global drylands exhibited significantly negative RVPD − NDVI and RVPD − ET values. In contrast, the percentage of significantly negative RVPD − NDVI and RVPD − ET decreased to <10% in cold drylands (>60° N). In predominantly warm drylands (60° N~60° S), negative VPD effects were significantly and positively regulated by soil water availability, as determined by multiple linear regression models. However, these significant regulatory effects were not observed in cold drylands. Moving-window analyses further revealed that temporal changes in RVPD − NDVI and RVPD − ET were positively correlated with changes in the Standardized Precipitation Evapotranspiration Index (SPEI). In warm drylands, areas with increasing RVPD − NDVI and RVPD − ET over time showed an increasing trend in the SPEI, whereas areas with a decreasing SPEI showed a negative trend in RVPD − NDVI and RVPD − ET values over time. Given the increasing atmospheric dryness due to climate change, this study highlighted the importance of re-evaluating the representation of the role of water availability in driving the response of the carbon-water cycle to increased VPD across global drylands.

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.

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.

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