Under the changing rainfall patterns induced by global climate change, understanding the response of CO₂ fluxes to rain pulses is becoming increasingly important. Although the response likely varies among ecosystems and plant species, we have limited information regarding the CO₂ flux response (i.e., magnitude and speed) immediately after a rain pulse within a community composed mainly of annual herbs in grassland ecosystems. In this experiment, we examined the change in CO₂ fluxes (net ecosystem CO₂ exchange, NEE; gross primary production, GPP; ecosystem respiration, R_e) after a rainfall event (7.6－8.7 mm d^-1) that occurred in a semi-arid grassland in central Mongolia before dawn on 6 August 2022. Vegetation in the measurement plots was composed mainly of annual herbs, including Chenopodium acuminatum and Salsola collina. Both GPP and R_e increased significantly within 12 h after the rainfall ended (in the daytime on 6 August) by 3.2 and 4.0 times, respectively, compared with those fluxes before the rainfall event (in the daytime on 5 August). There was no significant change in NEE. Our findings demonstrate the rapid and strong change of CO₂ fluxes after a rain pulse in a grassland plant community composed mainly of annual herbs.

　Maize is a vital crop in South Africa, but its production in the semi-arid North West province is vulnerable to low and variable rainfall. This study assessed the effectiveness of Sentinel-1 Synthetic Aperture Radar (SAR) data at monitoring maize phenology and its relationship with yield in this region from 2018 to 2021. SAR effectively monitored key phenological stages. Increases in VV (vertical transmit－vertical receive polarization) and VH (vertical transmit－horizontal receive polarization) gamma naught were linked to rainfall amounts during the sowing period. A rise in the VH/VV cross-polarization ratio signaled the emergence period, as validated by a reference NDVI value of 0.15. The peak in VH gamma naught corresponded with the heading stage, and the peak in VH/VV cross-polarization ratio suggested the maturity stage, 84－144 days after emergence. Interestingly, the VH gamma naught peak had a negative correlation with yield in the overall data. This inverse relationship is attributable to the low plant density and drought stress during the 2018－19 cropping season, which reduced growth and thus increased volume scattering within the maize canopy structure. We conclude that SAR is an effective tool with which to detect phenological stages, and forecast yield in the North West province.

　Gridded yield products are essential for understanding the historical trajectories of food production, assessing climate risks in cropping systems, validating tools for generating yield projections, and contributing to developing a knowledgebase for making agri-food systems sustainable. This study aims to develop a method for deriving a monthly 1-km gridded yield product that is theoretically applicable worldwide and without national or subnational yield statistics. The method combines objective sources of information, i.e., satellite remote sensing, process model simulation and field measurements, via a machine learning residual model approach and does not rely on yield statistics. The method was tested for feasibility in Madagascar, Sri Lanka and the Philippines, where the number of annual rice harvests are different. The gridded products derived using the proposed method produce yield estimates that are consistent with the independent data. However, the consistency varies by season, satellite, and data type (crop cuts, household surveys, yield statistics and other gridded yield products). The 1-km yield estimates agreed better with the field observations than with the yield statistics and surveys. The relative root-mean-squared errors (rRMSE) appeared smaller for the wet-season than for the dry-season. The yield estimates based on MODIS and VIIRS are relatively the most reliable, followed distantly by those derived using GCOM-C. The yield products developed here are unique in that they provide information on season-specific water management conditions and yield responses to changes in nitrogen input and planting dates, providing guidance for adaptation to climate change through agronomic adjustments.

　Winter nocturnal temperature drops in cold regions arise from complex interactions between radiative cooling and terrain-induced micrometeorological processes. Using long-term operational surface observations from 1978 to 2025, this study investigated the regional and topographic controls on nocturnal cooling in Hokkaido, Japan. The results revealed a pronounced regional contrast in long-term mean temperature drops, with stronger cooling on the Pacific side and weaker cooling on the Sea of Japan side. These differences reflect variations in nocturnal radiative conditions, inferred indirectly from the sunshine duration during the 24-h noon-to-noon period, and are further modulated by local cold-air pooling characteristics, as represented by clear-night wind speed. With respect to extreme temperature drops, principal component analysis identified southeastern and northern Hokkaido as the regions most susceptible to intense and widespread nocturnal cooling. A key contribution of this study is the introduction of two terrain-based metrics that capture the terrain characteristics responsible for such large temperature drops, extending the analysis beyond the basin- and valley-focused approaches common in previous studies. These metrics are R_av, the area-volume ratio representing the radiative-cooling capacity of the surrounding terrain, and A_site/A_in, the fraction of the inner-catchment area lying below the site elevation, representing terrain openness and the site’s relative elevation. Analysis indicated that cooling in shallow terrain is governed primarily by R_av, whereas cooling in deep terrain depends on the cold-air pooling structure and the site’s relative position within the pool, captured by A_site/A_in. Both metrics are physically interpretable and rely solely on digital elevation data, making them broadly applicable to other cold regions. These findings provide a unified and mechanistically grounded framework for understanding extreme nocturnal cooling across diverse terrain types and offer practical tools for identifying areas vulnerable to extreme low temperatures in cold-region agriculture and forest management.

　Soil moisture is a key agricultural variable that influences both crop growth and field workability. This study develops a convolutional neural network (CNN) model capable of estimating surface soil moisture from RGB images captured under actual field conditions. The experiment was conducted on bare soil in Matsudo, Chiba, Japan, where time-series images of a 3 m×2 m field plot were collected over five consecutive days. In total, 98 surface images were captured using a consumer-grade trail camera and cropped into 1,960 subplot images corresponding to the EC-5 soil moisture sensors installed at a depth of 2 cm. Data augmentation techniques, including rotation, flipping, and brightness adjustment, were applied to enhance the variability of the training dataset, resulting in 19,600 images. The CNN model was trained using a mini-batch size of 8 over 20 epochs. Evaluation with 100 test images demonstrated a strong estimation performance for volumetric water content, achieving an R² of 0.84, a root mean square error (RMSE) of 1.83%, and a normalized RMSE of 0.07. This highlights the potential of using low-cost cameras and accessible deep learning techniques for soil moisture estimation without relying on labor-intensive conventional soil sensors or costly spectral sensors.