We presented an algorithm for inferring aerosol layer height (ALH) and optical depth (AOD) over ocean surface from radiances in oxygen A and B bands measured by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) orbiting at Lagrangian‐1 point. The algorithm was applied to EPIC imagery of a 2 day dust outbreak over the North Atlantic Ocean. Retrieved ALHs and AODs were evaluated against counterparts observed by Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and Aerosol Robotic Network. The comparisons showed 71.5% of EPIC‐retrieved ALHs were within ±0.5 km of those determined from CALIOP and 74.4% of EPIC AOD retrievals fell within a ± (0.1 + 10%) envelope of MODIS retrievals. This study demonstrates the potential of EPIC measurements for retrieving global aerosol height multiple times daily, which are essential for evaluating aerosol profile simulated in climate models and for better estimating aerosol radiative effects.

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The Hunga Hunga-Tonga Ha'apai (HT) (20.54°S, 178.3°W) submarine volcano violently erupted on 15 January 2022. The volcanic explosivity index (VEI) was 5, comparable to Krakatau eruption in 1883. Since HT was a submarine volcano, it appears to have lofted a significant amount of water into the stratosphere. Indeed, Microwave Limb Sounder (MLS) measurements show that HT water enhancement was quite high relative to SO 2 (Millán et al., 2022)-hereafter M22. The MLS estimated water injection was up to 146 Tg (M22). The eruption plume was detected up to 57 km on 15 January 2022 (Carr et al., 2022;Proud et al., 2022). The Ozone Mapping and Profile Suite-Limb Profiler (OMPS-LP) detected extinction enhancements above 45 km (Taha et al., 2022).In this paper we will examine at the evolution of the water vapor and aerosol enhancements that followed the HT eruption. M22 noted that the amount of water deposited in the stratosphere by HT was unprecedented in the modern history of volcanic eruption observations. Several MLS water vapor profiles made shortly after the eruption show concentrations exceeding 300 ppmv against a normal stratospheric concentration of ∼4 ppmv. As the eruption evolved, MLS water vapor maps show that above about 2 hPa (∼43 km), the plume quickly spreads and that the water vapor enhancement disperses. A secondary maximum at about 25 hPa (∼26 km) persists (M22). The aerosol field shows similar behavior with rapid dispersal at higher altitudes but persistent high levels of aerosol extinction below ∼25 hPa (∼26 km) (Taha et al., 2022). The aerosol extinction in this layer grows over the 30 days following the eruption presumably due to the conversion of SO 2 to sulfate aerosols (e.g., Zhu et al., 2020).There are several key questions concerning the HT eruption: Why did the unusual water vapor layer form and persist? How is it related to the aerosol layer? Below we show that the water vapor enhancement overlaps the top of the extinction anomaly, but they are distinct, and furthermore the two enhancements vertically separate over

On 15 January 2022, the Hunga Tonga‐Hunga Ha'apai (HT) eruption injected SO2 and water into the middle stratosphere. Shortly after the eruption, the water vapor anomaly moved northward toward and across the equator. This northward movement appears to be due to equatorial Rossby waves forced by the excessive infrared water vapor cooling. Following the early eruption stage, persistent mid‐stratospheric water vapor and aerosol layers were mostly confined to Southern Hemisphere tropics (Eq. to 30°S). However, during the spring of 2022, the westerly phase of the tropical quasi‐biennial oscillation (QBO) descended through the tropics. The HT water vapor and aerosol anomalies were observed to again move across the equator coincident with the shift in the Brewer‐Dobson circulation and the descent of the QBO shear zone.

In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated.