The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling approach, named integrated passive and active downscaling (I-PAD), to achieve high spatial and temporal resolution soil moisture datasets over regions without detailed soil data. The Advanced Microwave Scanning Radiometer (AMSR-E) and Phased Array-type L-band SAR (PALSAR) data are combined through a dual-pass land data assimilation system to obtain soil moisture at 1 km resolution. In the first step, fine resolution model parameters are optimized based on fine resolution PALSAR soil moisture and moderate-resolution imaging spectroradiometer (MODIS) leaf area index data, and coarse resolution AMSR-E brightness temperature data. Then, the 25 km AMSR-E observations are assimilated into a land surface model at 1 km resolution with a simple but computationally low-cost algorithm that considers the spatial resolution difference. Precipitation data are used as the only inputs from ground measurements. The evaluations at the two lightly vegetated sites in Mongolia and the Little Washita basin show that the time series of soil moisture are improved at most of the observation by the assimilation scheme. The analyses reveal that I-PAD can capture overall spatial trends of soil moisture within the coarse resolution radiometer footprints, demonstrating the potential of the algorithm to be applied over data-sparse regions. The capability and limitation are discussed based on the simple optimization and assimilation schemes used in the algorithm.

Flood early warning systems (FEWS) are crucial for flood risk management; however, several catchments in the developing world are still far behind in all aspects of FEWS and thus, they encounter devastating damage recurrently due to limitations in data, knowledge, and technologies. This paper presents a catchment-scale integrated flood information system by incorporating present-day multi-platform data and technologies (e.g., ground and satellite rainfall observation, ensemble rainfall forecasts, and flood simulation) and evaluates their performance in a poorly gauged prototype basin (i.e., the Kalu River basin). Satellite rainfall products obtained in real time (GSMaP-NOW) and near-real time (GSMaP-NRT) can detect heavy rainfall events well and bias-corrected products can further improve rainfall estimations and flood simulations. Particularly, GSMaP-NRT, which outperformed GSMaP-NOW in both rainfall and discharge estimations, is suitable for near-real-time flood-related applications. Ensemble rainfall forecasts showed good performance in predicting alarming signals of heavy rainfall and peak flow with uncertainties in the amounts and timings of the events. Information derived from both satellite and ensemble forecasts on heavy rainfall, simulated flood signals, and their possible range of probabilities is promising and can help minimize the data gaps and improve the knowledge and technology of experts and policy-makers in poorly gauged basins.

Having an additional tool for swiftly determining the extent of flood damage to crops with confidence is beneficial. This study focuses on estimating rice crop damage caused by flooding in Candaba, Pampanga, using open-source satellite data. By analyzing the correlation between Normalized Difference Vegetation Index (NDVI) measurements from unmanned aerial vehicles (UAVs) and Sentinel-2 (S2) satellite data, a cost-effective and time-efficient alternative for agricultural monitoring is explored. This study comprises two stages: establishing a correlation between clear sky observations and NDVI measurements, and employing a combination of S2 NDVI and Synthetic Aperture Radar (SAR) NDVI to estimate crop damage. The integration of SAR and optical satellite data overcomes cloud cover challenges during typhoon events. The accuracy of standing crop estimation reached up to 99.2%, while crop damage estimation reached up to 99.7%. UAVs equipped with multispectral cameras prove effective for small-scale monitoring, while satellite imagery offers a valuable alternative for larger areas. The strong correlation between UAV and satellite-derived NDVI measurements highlights the significance of open-source satellite data in accurately estimating rice crop damage, providing a swift and reliable tool for assessing flood damage in agricultural monitoring.

Satellite-based passive microwave (PMW) remote sensing is an essential technique to clarify long-term and large-scale distribution patterns of cloud water content (CWC). However, most CWC estimation methods are not implemented over land because of high heterogeneity of land radiation, and the detailed characteristics of microwave (MW) radiative transfer between land and atmosphere including clouds have not been elucidated. This study aims to elucidate these characteristics and reveal the accuracy of land emissivity representation necessary for adequate CWC estimation over land using satellite-based PMW under various CWC conditions. First, important parameters related to MW radiative transfer between land and atmosphere at CWC-relevant frequencies in the presence of clouds are determined theoretically. Next, the relationship between errors in these parameters and the brightness temperatures used for CWC estimation is clarified through considerations based on radiative transfer equations. Then, ground-based PMW observations and numerical simulation are used to reveal the actual values of these important parameters and the size of errors. Finally, the results show that for any cloud liquid water path (LWP) value greater than 1.6 kg/m² at 89 GHz and 5 kg/m² at 36 GHz, we can reasonably neglect the heterogeneity of emissivity and radiation from the land surface for CWC estimation. However, when LWP values are below the threshold, the error in the representation of land emissivity should be kept below 0.015 for both 89- and 36-GHz data, and volumetric soil moisture content should have an error lower than 5%-6% for both frequencies.

本研究は2020年11月にフィリピン共和国ルソン島を通過した台風Ulyssesによる洪水災害を事例にし，衛星画像で把握できた浸水域に基づいて推定した潜在被災者数が，どの程度実際の避難者数と整合するかを分析することで衛星データ活用のメリットと課題について検討した．この事例では，台風通過翌日の午前中にはSentinel-2によって対象流域の大部分をカバーする光学衛星画像が取得され，対象流域の8割以上の自治体で浸水状況を把握できた．そして，その浸水域に人口分布データを重ねることで求めた潜在被災者数と実際の避難者数との比較では，大きな乖離があることが示されたものの，自治体ベースでの実際の避難者発生有無とは非常に良く整合しており，避難者発生を判断する目安として活用できる可能性が示唆された．

A coil probe (CP) for time-domain reflectometry (TDR) with a sensor length of 40 mm (CP₄₀) was developed for long-term field soil moisture measurements in a thin surface soil layer (0–3 cm depth). In laboratory, soil moisture measurements of CP₄₀ were nearly identical to those of a traditional two-rod type TDR (2RTDR) probe (rod length: 15 cm, diameter: 0.3 cm, spacing: 3 cm). The CP₄₀ measurement accuracy was between 0.01 m³/m³ and 0.03 m³/m³. For long-term field soil moisture measurements, five CP₄₀ units were installed in the highly wetted soil at the Sanzai site (SS), which is in the permafrost area of the Taiga, and dry soil at the Mandalgobi site (MGS) in the semi-arid area of Mongolia. Four units accurately measured soil moisture at both sites over six years (2002–2007and 2008–2009). Three units succeeded in conducting precisely continuous soil moisture measurements between 2008 and 2022 at the MGS. Two units successfully measured soil moisture over 21 years at both the sites. The representativeness of the CP₄₀ soil moisture measurements in highly wetted soils was low because of heavy rainfall and soil heterogeneity. The accuracy of CP₄₀ soil moisture measurements in highly wetted soils was slightly lower than that of the traditional 2RTDR probe. However, the accuracy of the CP₄₀ soil-moisture measurements in the dry soil was comparable to that of the traditional 2RTDR probe. CP₄₀ units are durable and their field soil moisture measurements demonstrate stable and precise performance (bias, RMSE), even under severe environmental conditions.