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Monitoring drought precisely and evaluating drought effects quantitatively can establish a scientific foundation for understanding drought. Although solar-induced chlorophyll fluorescence (SIF) can detect the drought stress in advance, the performance of SIF in monitoring drought and assessing drought-induced gross primary productivity (GPP) losses from lush to senescence remains to be further studied. Taking the 2019 drought in the middle and lower reaches of the Yangtze River (MLRYR) as an example, this study aims to monitor and assess this drought by employing a new global, OCO-2-based SIF (GOSIF) and vegetation indexes (VIs). Results showed that the GPP, GOSIF, and VIs all exhibited significant increasing trends during 2000–2020. GOSIF was most consistent with GPP in spatial distribution and was most correlated with GPP in both annual (linear correlation, R2 = 0.87) and monthly (polynomial correlation, R2 = 0.976) time scales by comparing with VIs. During July–December 2019, the precipitation (PPT), soil moisture, and standardized precipitation evapotranspiration index (SPEI) were generally below the averages during 2011–2020 and reached their lowest point in November, while those of air temperature (Tem), land surface temperature (LST), and photosynthetically active radiation (PAR) were the contrary. For drought monitoring, the spatial distributions of standardized anomalies of GOSIF and VIs were consistent during August–October 2019. In November and December, however, considering vegetation has entered the senescence stage, SIF had an obvious early response in vegetation physiological state monitoring compared with VIs, while VIs can better indicate meteorological drought conditions than SIF. For drought assessment, the spatial distribution characteristics of GOSIF and its standardized anomaly were both most consistent with that of GPP, especially the standardized anomaly in November and December. All the above phenomena verified the good spatial consistency between SIF and GPP and the superior ability of SIF in capturing and quantifying drought-induced GPP losses. Results of this study will improve the understanding of the prevention and reduction in agrometeorological disasters and can provide an accurate and timely method for drought monitoring.

The precipitation enhancement operation data of aircraft from 2014 to 2019 and the global data assimilation system (NCEP GDAS) were used in this study. The transport process of the transmission of artificial precipitation enhancement seeding agents for aircraft was successfully simulated by the HYSPLIT model. The purpose of the study was to explore the applicability of the model in determining the artificial precipitation enhancement influence area and provide a technical method for evaluating the effect of artificial precipitation enhancement. The results show that (1) the HYSPLIT model can be used to track the transmission of aircraft precipitation enhancement seeding agents hourly. Suppose the seeding route satisfies the condition that the route and its interval area are the effective seeding area within 3 h after the end of the seeding agent. In that case, the seeding area’s boundary points can be used as dynamic change markers in the influence area. (2) The HYSPLIT model was used to simulate 24 aircraft precipitation enhancement seeding agent transmission processes. The transmission path for the seeding agent influence altitude layer was mostly southwest or west; the angle ranged from 225° to 268°; the horizontal transport distance of the seeding agent for three hours was 100–200 km; the vertical transmission direction was mostly upward; the range was 0–1200 m; the influence area decreased at the third h of seeding agent transport for 71% of the precipitation enhancement operations. (3) Based on the dynamic variations of 24 aircraft precipitation affected areas determined by the HYSPLIT model, and the contrast area selected by the similarity measurement method, 15 (63%) aircraft precipitation operations contributed to the increase in precipitation.

Water vapor transport is an important foundation and prerequisite for the occurrence of rainstorms. Consequently, the understanding of water vapor transport as well as the sources of water vapor during rainstorm processes should be considered as essential to study the formation mechanism of rainstorms. In this study, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is adopted for backward tracking of water vapor transport trajectories and sources during the “7·20” extraordinary heavy rain process in Zhengzhou City of China that occurred on 20 July 2021. On this basis, the trajectory clustering method is applied to quantitatively analyze the contributions of water vapor sources, aiming to provide a basis for exploring the maintenance mechanism of this extreme rainstorm event. The spatio-temporal characteristics of this rainstorm event show that there are 4 consecutive days with the precipitation reaching or exceeding the rainstorm level across the whole Zhengzhou City, with the daily rainfall amounts at eight national meteorological stations all breaking their respective historical extreme values. The regional-averaged rainfall amount in Zhengzhou City is 527.4 mm, while the maximum accumulated rainfall amount reaches 985.2 mm at Xinmi station and the maximum hourly rainfall amount at Zhengzhou national meteorological station reaches 201.9 mm h−1. The water vapor sources for this rainfall process, ranked in descending order of contribution, are the Western Pacific, inland areas of Northwest China and South China, and South China Sea. The water vapor at lower levels is mainly transported from the Western Pacific and the South China Sea, while those from the inland areas of Northwest China and South China provide a supply of water vapor at upper levels to a certain extent. The water vapor at 950 hPa is mainly sourced from the Western Pacific and South China Sea, accounting for 56% and 44%, respectively. The water vapor at 850 hPa mainly derives from the Western Pacific and the inland areas of South China, contributing 58% and 34% of the total, respectively. The water vapor at 700 hPa mainly comes from the inland areas of Northwest China and South China Sea. Specifically, the water vapor from inland Northwest China contributes 44% of the total, acting as the primary source. The water vapor at 500 hPa is mainly transported from the inland areas of South China and Northwest China, with that from the inland South China (56%) being more prominent. The water vapor at all levels is mainly transported to the rainstorm region through the eastern and southern regions of China from the source areas. Additionally, there are some differences in the water vapor trajectories at a 6 h interval.

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Reference evapotranspiration (ET0) is essential for agricultural production and crop water management. The recent climate change affecting the spatiotemporal variation of ET0 in eastern China continues to still be less understood. For this purpose, the latest observed data from 77 meteorological stations in Anhui province were utilized to determine the spatiotemporal variations of ET0 by the use of the Penman–Monteith FAO 56 (PMF-56) model. Furthermore, the Theil–Sen estimator and the Mann–Kendall (M–K) test were adopted to analyze the trends of ET0 and meteorological factors. Moreover, the differential method was employed to explore the sensitivity of ET0 to meteorological factors and the contributions of meteorological factors to ET0 trends. Results show that the ET0 decreased significantly before 1990, and then increased slowly. The ET0 is commonly higher in the north and lower in the south. ET0 is most sensitive to relative humidity (RH), except in summer. However, in summer, net radiation (Rn) is the most sensitive factor. During 1961–1990, Rn was the leading factor annually, during the growing season and summer, while wind speed (u2) played a leading role in others. All meteorological factors provide negative contributions to ET0 trends, which ultimately lead to decreasing ET0 trends. During 1991–2019, the leading factor of ET0 trends changed to the mean temperature (Ta) annually, during the growing season, spring and summer, and then to Rn in others. Overall, the negative contributions from u2 and Rn cannot offset the positive contributions from Ta and RH, which ultimately lead to slow upward ET0 trends. The dramatic drop in the amount of u2 that contributes to the changes in ET0 in Region III is also worth noting.

The scale effect problem is one of the most challenging issues in remote sensing studies. However, the research on the methodology and theory of the scale effect is scarcely applied in practice. To this end, in this study, 3 years of field experimental data of continuous water stresses on summer maize were used for this purpose. Furthermore, the Prospect and Sail models were employed to investigate the scale effects of reflectance characteristics and vegetation indexes. The results indicated that the spectral characteristics of canopy and leaf of summer maize were similar under continuous water stresses at various stages. The reflectance at the canopy level was distinct from that at the leaf level, considering the soil background differences. From leaf to canopy scales, with the increase in the leaf area index (LAI), the spectral reflectance of all treatments in the visible band decreased, but increased in the near-infrared band, and the reflectance was saturated when LAI increased to 5. The reflectance difference caused by LAI variation was enlarged as the drought stress intensified in the short-wave infrared band. The spectral reflectance in the near-infrared band was susceptible to leaf inclination angle (LIA) variation and changed significantly, especially in the closed canopy. With the increase in LAI, the difference vegetation index (DVI) and normalized difference vegetation index (NDVI) values under each treatment showed a gradually increasing trend. With the increase in LIA, the DVI value decreased gradually, and the DVI value under the saturated canopy was significantly higher than that under the unclosed canopy. However, the NDVI values of all treatments did not change with LIA, mostly under the closed canopy. Overall, the results demonstrated that LAI had a more significant influence on canopy reflectance than LIA. In addition, NDVI was not able to capture the LAI and LIA information when the canopy was closed, but DVI performed better.