This paper proposes a novel method to estimate rainfall intensity by analyzing the sound of raindrops. An innovative device for collecting acoustic data was designed, capable of blocking ambient noise in rainy environments. The device was deployed in real rainfall conditions during both the monsoon season and non-monsoon season to record raindrop sounds. The collected raindrop sounds were divided into 1 s, 10 s, and 1 min intervals, and the performance of rainfall intensity estimation for each segment length was compared. First, the rainfall occurrence was determined based on four extracted frequency domain features (average of dB, frequency-weighted average of dB, standard deviation of dB, and highest frequency), followed by a quantitative estimation of the rainfall intensity for the periods in which rainfall occurred. The results indicated that the best estimation performance was achieved when using 10 s segments, corresponding to the following metrics: accuracy: 0.909, false alarm ratio: 0.099, critical success index: 0.753, precision: 0.901, recall: 0.821, and F1 score: 0.859 for rainfall occurrence classification; and root mean square error: 1.675 mm/h, $R^2$: 0.798, and mean absolute error: 0.493 mm/h for quantitative rainfall intensity estimation. The proposed small and lightweight device is convenient to install and manage and is remarkably cost-effective compared with traditional rainfall observation equipment. Additionally, this compact rainfall acoustic collection device can facilitate the collection of detailed rainfall information over vast areas.

This study proposes a novel method for rainfall intensity estimation from acoustic and vibration data with low-cost sensors. At first, a precipitation measurement device was developed to collect sound and touch signals from raindrops, composed of Raspberry Pi, a condenser microphone, and an accelerometer with 6 degrees of freedom. To figure out whether rainfall occurred or not, a binary classification model with the XGBoost algorithm was considered to analyze long-term time series of vibration data. Then, high-resolution acoustic data was used to investigate the main characteristics of rainfall patterns at a frequency domain for the period when it was determined that rainfall occurred. As a result of the Short Time Fourier Transform (STFT), the highest frequency, mean and standard deviation of amplitudes were selected as representative values for minute data. Finally, different types of regression models were applied to develop the method for rainfall intensity estimation from comparative analysis with other precipitation measurement devices (e.g., PARSIVEL, etc.). It should be noted that the new device with the proposed method functions reliably under extreme environmental conditions when the estimated rainfall intensity was compared with measured data from ground-based precipitation devices. It shows that low-cost sensors with sound and touch signals from raindrops can be effectively used for rainfall intensity estimation with easy installation and maintenance, indicating a strong possibility of being considered in a wide range of areas for precipitation measurement with high resolution and accuracyAcknowledgementThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).

Inland wetlands provide essential hydrological–ecological functions, yet their conservation is increasingly challenged by land-use and climate change, underscoring the need for robust, large-scale approaches to identifying wetland restoration candidates. This study proposes a data-driven framework for classifying inland wetlands and identifying stable restoration candidates by integrating multi-source remote sensing and reanalysis datasets (MODIS, GPM, and ERA5-Land) within an eXtreme Gradient Boosting-based binary classification model. To explicitly account for type-dependent environmental characteristics, inland wetlands across South Korea were classified into four categories: mountain, lake, river, and constructed wetlands. For each wetland type, the 10 largest sites from the national inland wetland inventory were selected for type-specific model training using 13 hydrometeorological, vegetation, and carbon-related variables derived from monthly data (2017–2022). The trained models were applied to seasonally averaged 2023 datasets, and stable restoration candidates were identified by extracting the intersection of pixels classified as wetlands under a binary classification criterion set at a 99% confidence level, thereby ensuring consistency despite seasonal variability. Predictive performance varied with training data availability: river wetlands achieved the highest accuracy (F1 = 0.88), whereas mountain wetlands showed the lowest performance (F1 = 0.82) among wetland types, due to limited training samples and environmental heterogeneity. When the trained models were applied nationwide across South Korea, seasonally consistent restoration candidates accounted for 3.57% of mountain wetlands, 3.24% of lake wetlands, 2.80% of constructed wetlands, and 0.52% of river wetlands. These results provide a nationwide spatial baseline for identifying potential sites for inland wetland restoration.

This research proposes a comprehensive and sustainable framework for wetland monitoring by evaluating the wetland environmental superiority index (WESI) of inland wetlands using a long short-term memory (LSTM) model. The WESI estimation method aims to establish a long-term and periodic monitoring system for extensive regions based on remote sensing and reanalysis data. To achieve this, exemplary wetland sites representing high-quality and vulnerable wetlands were selected, with a label of 1 assigned to high-quality wetlands and 0 to vulnerable wetlands. These exemplary wetland sites provide the target variables for training the LSTM-based WESI estimation model, while remote sensing and reanalysis datasets closely associated with the environmental characteristics of inland wetlands are utilized as input variables. In this study, a comprehensive database comprising 13 types of hydrometeorological, vegetation, topographic, and carbon-related remote sensing and reanalysis datasets was established. Additionally, 30 exemplary high-quality and 30 vulnerable inland wetlands—identified based on the field survey conducted by the National Institute of Ecology (NIE) in South Korea—were used to evaluate the applicability of the proposed framework. The WESI estimation method is expected to contribute to the establishment of a long-term, continuous monitoring system for inland wetlands by leveraging the stable data production capabilities of remote sensing and reanalysis data. AcknowledgementThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2024-00334564) and in part by Korea Environmental Industry&Technology Institute (KEITI) through Wetland Ecosystem Value Evaluation and Carbon Absorption Value Promotion Technology Development Project, funded by Korea Ministry of Environment (MOE). (2022003640001)

In this study, we propose a novel approach for precipitation measurement based on rainfall acoustics, utilizing an effective rainfall acoustic collection device with low-cost IoT sensors housed in waterproof enclosure. Here, rainfall acoustics refer to the sound generated when raindrops fall and collide with surfaces such as the ground or canopy. Even at the same rainfall intensity, depending on the medium with which raindrops collide, acoustics with different frequency characteristics may occur. In this research, an acoustic collection device, combining a Raspberry Pi and a condenser microphone, was inserted into a waterproof enclosure and deployed in a rainfall environment to collect rainfall acoustics. This approach not only controls the medium of rainfall acoustics but also effectively blocks ambient noise and water, ensuring consistent characteristics of rainfall acoustics regardless of the installation environment. The collected rainfall acoustics were segmented into 10-second intervals, and spectrograms in the frequency domain were extracted by applying Short-Time Fourier Transform for each segment. Finally, using the extracted spectrogram as input data, a rainfall intensity estimation model based on a convolutional neural network was developed and other precision rainfall observation instruments (e.g., PARSIVEL, Pluvio², etc.) were considered collectively for the validation of the developed rainfall intensity estimation model. Acoustic-based rainfall observation enables the establishment of a dense observation network using low-cost devices. Leveraging the high temporal resolution of acoustic data, extremely short observation periods for rainfall can be achieved. This methodology presents an opportunity for cost-effective and high-spatiotemporal-resolution rainfall observation, overcoming the limitations of traditional methods.Keywords: Acoustic Sensing, Rainfall Acoustics, Precipitation, Convolutional Neural NetworkAcknowledgementThis work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS-2023-00250239) and in part by the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00243008.This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.NRF-2022R1A4A3032838).

Gross Primary Productivity (GPP) plays a central role in regulating terrestrial carbon uptake, yet commonly used satellite-based GPP products are provided at multi-day temporal resolutions, limiting their ability to capture rapid ecosystem responses to short-term environmental variability. This temporal constraint is particularly critical under increasing occurrences of extreme weather events, where sub-daily vegetation dynamics remain poorly understood. In this study, we propose a machine-learning-based framework to generate hourly GPP estimates at moderate spatial resolution across the Korean Peninsula. The approach integrates satellite-derived vegetation indices with reanalysis-based hydrometeorological variables and explicitly accounts for land-cover heterogeneity by constructing independent models for major land-cover classes. To enhance model interpretability and efficiency, a feature selection strategy was applied to identify key environmental drivers of GPP variability for each land-cover type. Model performance was evaluated using temporally independent datasets, demonstrating that hourly GPP estimates aggregated to multi-day scales are consistent with existing satellite GPP products, while additionally capturing realistic diurnal cycles and seasonal patterns. The results indicate that a reduced set of influential variables can preserve predictive skill while improving computational efficiency. The proposed framework provides a practical pathway for temporally downscaling widely available satellite GPP products to sub-daily resolution in regions with limited ground observations. This capability offers new opportunities to investigate vegetation productivity responses to short-term climatic extremes such as heatwaves and droughts, contributing to improved understanding of ecosystem carbon dynamics under a changing climate. AcknowledgementThis work was supported by the Korea Environmental Industry & Technology Institute (KEITI) through Wetland Ecosystem Value Evaluation and Carbon Absorption Value Promotion Technology Development Project, funded by Korea Ministry of Climate, Energy and Environment (MCEE) (RS-2022-KE002066), and supported by the National Research Foundation of Korea(NRF) funded by the Ministry of Education (RS-2024-00465925) and by the Korea government (MSIT) (RS-2024-00334564 & RS-2021-NR060085).