The soil phase-transition temperature (PTT) is critical for accurately determining the soil freeze–thaw (FT) state, which is essential for developing surface FT state identification algorithms based on remote sensing technology and reliably validating FT products. In this study, we develop an innovative approach for determining soil PTT in the region of the dense soil moisture and soil temperature monitoring network in the Qinghai Lake Basin (QLB-NET). We employ four advanced models—polynomial regression (PR), random forest (RF), feedforward neural network (FNN), and transformer (XFMR) models—to establish relationships between soil PTT and various environmental factors for daily and site-specific soil PTT estimation. Soil PTT values derived from asigmoidcurve fitting (SCF) method that combines physical theory with empirical modeling are used in the models as the target data considering soil properties and topographic features as predictors. Additionally, we systematically evaluate the models’ performance and compare the results against those of the fixed 0°C threshold methods. The results indicate that most soil freezing temperatures range between -1°C and 0°C, and thawing temperatures range between -0.5°C and 0.5°C, with occasional freezing temperatures above 0°C. Model-derived soil PTT significantly outperforms the traditional 0°C threshold in terms of soil FT state classification accuracy, particularly during the frozen and FT transitional seasons. The XFMR model achieves superior performance in an internal comparative analysis, highlighting its particular effectiveness for soil PTT determination. These results provide valuable insights for improving remote sensing-based FT product validation and permafrost monitoring.

Drought assessment is critical for effective water resource allocation and irrigation management, especially in the era of climate change (CC). The satellite-derived Surface Water Availability-Temperature Index (SWATI) has shown its high applicability for estimating agricultural drought conditions. The SWATI is developed by integrating three satellite variables, namely, the Normalized Difference Latent Heat Index (NDLI), the Normalized Difference Vegetation Index (NDVI), and the Land Surface Temperature (LST). This study examined the ability of SWATI to monitor soil moisture (SM) and drought status from national to global scales. To assess the performance of SWATI, five hydrometeorological datasets relevant to drought assessment, such as downscaled Global Land Surface Satellite (GLASS) SM, SM Active Passive (SMAP) SM, Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall, and Temperature-Vegetation-Shortwave infrared reflectance Dryness Index (TVSDI), were utilized. The national analysis revealed that the SWATI can effectively indicate the SM, as evidenced by its high correlation coefficients (r) with the high-resolution GLASS SM. At the continental scale, the SWATI showed stronger correlations with SMAP SM ($- 0.83\le r \le -0.54$;$p~\lt 0.01$) compared to the TVSDI ($- 0.80\le r~\le -0.47$;$p~\lt 0.01$). Moreover, the spatiotemporal correlation evaluation indicated good consistencies between the SWATI and four datasets (SMAP SM, MODIS ET, CHIRPS rainfall, and TVSDI), with over 43% of the global area showing the highest consistency. It is concluded that the SWATI can represent SM and generate high-resolution drought maps. Furthermore, the SWATI was used to assess global agricultural drought conditions from 2001 to 2022. The analysis of drought distributions and trends based on SWATI showed wide variations across different land cover types and climate conditions. Overall, the satellite-based SWATI is an effective drought monitoring method, and its further utilization will be essential for irrigation management in data-scarce regions.

Drought, a destructive natural disaster, poses a significant threat to vulnerable areas worldwide. Its occurrence in Taiwan brings up concerns, particularly for vital sectors such as the high-end semiconductor chip industry. Many satellite-based indexes have been developed to monitor the drought. The Temperature-Vegetation Dryness Index (TVDI), a commonly-used drought index, uses an empirical simplification of Land Surface Temperature (LST) and Fractional Vegetation Cover (FVC). The newly-developed Temperature-Soil Moisture Dryness Index (TMDI) using the LST–Normalized Difference Latent Heat Index (NDLI) space is regarded as an alternative to TVDI due to its improved ability in vegetation-sparse areas. This article presents advancements in the TMDI utilizing the novel Fractional Surface Water Availability (FSWA) derived from the NDLI, with an emphasis on enhanced edge selection in the LST–FSWA trapezoidal space for observing drought states. An effective method has been used to select the dry and wet edges within this trapezoid. The ability of the indexes was evaluated using indicators, including the Surface Energy Balance Algorithm for Land (SEBAL)-based Crop Water Stress Index (CWSI) and evapotranspiration (ET), Gross Primary Productivity (GPP), and in-situ precipitation. The results show high correlations (r) between the TVDI and both CWSI and ET, with r values of 0.85 and -0.83, respectively. The TMDI reveals even stronger relationships with CWSI (r = 0.93) and ET (r = -0.94) and is more sensitive than individual variables (FVC, FSWA, and LST) and TVDI. It also indicates a high correlation between the TVDI and GPP (r = -0.69), while the TMDI displays a higher correlation with GPP (r = -0.75). Based on the spatiotemporal analysis, the TMDI was spatially well-matched with CWSI and GPP across most of the study area. Compared to other indexes, the TMDI exhibits the highest sensitivity to precipitation (r = -0.60). By leveraging the CWSI classification, a new TMDI threshold is proposed to assess drought status in south-western Taiwan during the fourth quarter of the years 2014 to 2021. Overall, the TMDI accurately captures spatiotemporal variations in drought status, providing valuable insights for irrigation managers to effectively manage limited water resources.

Crop water stress and drought status can be indicat-ed through soil moisture (SM). SM-related factors, such as water availability, vegetation state, and temperature, can be inferred by Normalized Difference Latent Heat Index (NDLI), Normalized Difference Vegetation Index (NDVI), and Land Surface Temper-ature (LST), respectively. This study presents a new Surface Wa-ter Availability and Temperature (SWAT) index that utilizes remotely-sensed data to monitor crop water stress and drought status. The Euclidean distance method was used to analyze the three-dimensional (3D) scatterplots of NDLI, NDVI, and LST to define the SWAT. Subsequently, the SWAT and two drought indices, including the Temperature-Vegetation Dryness Index (TVDI) and Temperature-Soil Moisture Dryness Index (TMDI), were used to determine the spatial signatures of droughts in Tai-wan from 2013 to 2020. These drought signatures were evaluated and compared against corresponding assessments from various indicators, including the in-situ SM, TVDI, TMDI, Crop Water Stress Index (CWSI), and Net Primary Productivity (NPP). The results show that the TVDI and TMDI were negatively correlated with the in-situ SM, with the highest correlation coefficient (r) of −0.53 and −0.67, respectively. The SWAT was found to be even more correlated to the SM (–0.79 ≤ r ≤ –0.49, p < 0.01) and exhib-ited more sensitive and stable than other single indices (NDVI, NDLI, and LST) and integrated indices (TVDI and TMDI). Ac-cording to the spatial observation, the SWAT index was closely related to CWSI (r = 0.77, p < 0.01). Moreover, the SWAT was in line with NPP (r = −0.79, p < 0.01) and more sensitive to drought than the TVDI and TMDI. Furthermore, the SWAT was highly correlated with the TVDI, TMDI, CWSI, and NPP in most agri-cultural regions in Taiwan. Overall, the SWAT is proven to be a satellite index effectively describing agricultural drought stress.

Drought does not occur often in Taiwan, but its consequence may be significant since Taiwan is an isolated island located in the Northwestern Pacific Ocean and its climate is marine tropical, with rainy season from May to October and dry season from November to April. Although Taiwan is characterized by high amount of precipitation annually, the country might still suffer from drought due to uneven temporal spatiotemporal distribution of the rainfall and small volume storage capacity of reservoirs. Severe droughts affected Taiwan in the past, such as in years 2020–2021, and had negative impacts on the environment, industry and agriculture. Thus, monitoring and assessing vulnerability to drought is vital for the environmental benefit and agricultural practice for Taiwan. Relationship between transformations of land use/land cover (LULC), drought features, and advised rapid effective indices to derive vulnerable patterns is important to provide useful information about the drought features for the human beings to mitigate its occurance. This paper introduces a framework to assess vulnerability to drought over Taiwan by using 11 indicators organized into three dimensions, including exposure, sensitivity and adaptive capacity. Results reveal that vulnerability to drought in Taiwan greatly varies from low to high levels with 25.7% and 13.3%, respectively. Serious drought vulnerability is mainly seen along the western coast of Taiwan. The presented framework is potentially useful in evaluating the vulnerability to drought for different sectors and proper management of water stored in the reservoirs is critical to mitigate the impacts of drought in the context of the climate change.

Surface water availability and its temperature are fundamental factors in describing the characteristics of land surface properties. In this study, a new temperature-soil moisture dryness index (TMDI) to quantify surface soil moisture (SSM) is proposed. It is defined as a function of land surface temperature variation and its relationship to surface water availability. The spatial pattern of TMDI has been analyzed over two time points of dry and rainy seasons for the plain area of Tainan, Taiwan, using Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) image acquired on January 20 and October 19, 2014. The effectiveness of TMDI in reflecting the SSM status was then evaluated by the results of the simulated evapotranspiration (ET) and verified by the near-surface air temperature ($T_{\text {air}}$) and humidity (RHair) measured by the ground-based weather stations. The results indicated that TMDI exhibited a significantly negative correlation with the simulated ET and positive correlation within situmeasured$T_{\text {air}}$from seven stations ($r = - 0.95$and −0.9 for simulated ET and$r = 0.94$and 0.78 for$T_{\text {air}}$corresponding to January 20 and October 19, respectively). We further compared the performance of the TMDI with the existing remotely sensed dryness assessment methods, including temperature vegetation dryness index (TVDI) and Surface Energy Balance Algorithm for Land (SEBAL) model. The advantages of the TMDI in reflecting SSM, especially in the nonvegetation area, are clearly demonstrated. It is concluded that the TMDI is a reliable indicator for determining the SSM status with a large degree of freedom for further applications since it does not require any other ground-based measurements.

Urban greenspace is an important component of infrastructure providing essential eco-services to the urban community with rapid growth population, in particular, in the context of climate change. Thus, it is necessary to ensure that urban greenspaces are sufficient with proper monitoring and management. In this study, we present urban greenspace mapping in the northern Taiwan derived from the Sentinel-2B imagery. Urban greenspace is classified with support of Google Earth Engine using classification and regression trees for Machine Learning (CART). 360 points were used for training and validation. Land cover is classified into four classes, including 1) dense tree cover, 2) tree cover, 3) water body, and 4) miscellaneous. Later, urban greenspace was further extracted and classified into roadside tree, park, and shrub or grassland. Overall accuracy of classification is 88.6 % with mean kappa coefficient of 0.84. The vulnerability map was generated and ranked into five levels to distinguish the risk patterns and levels of urban greenspace in the northern Taiwan to typhoons. Results further indicate the applicability of Sentinel-2 MSI data as an effective dataset to study urban greenspace at city scale.

Land cover and land use (LULC) are the key determinant factors that influence the regional climate. In this study, we present LULC classification for the Taipei City, Taiwan based on Sentinel-2B image acquired in 2018. A recently-proposed Normalized Difference Laten Heat Index (NDLI) and Normalized Difference Vegetation Index (NDVI) are utilized and compared to derive LULC, in particular, water bodies. Validation is based on reference datasets collected from Google Earth and field survey. Overall accuracies of classification are about 76% for NDLI and 91% for NDVI. However, it is shown that NDLI is highly capable to distinguish the water bodies from the others, such as built-up and bareland with accuracies of 100% and 95%, respectively, while NDVI shows better performance on vegetation classification only. In addition, it is found that shortwave infrared (SWIR)-2 (band 12) is more sensitive to identify the water bodies in comparison to SWIR-1 (band 11) of Sentinel-2B image to compute NDLI for extracting water bodies. This result further demonstrates that NDLI can be used as an effective indicator to detect and map the water surface or built-up or bareland by using Sentinel-2 imagery as initially suggested by Liou et al. [1].

Knowledge and understanding of evapotranspiration (ET) are crucial for a variety of water and energy balance applications. In the past decades, multiple ground observations and models have been used to evaluate the movement of water into atmosphere from different land cover types, while they cannot be conveniently implemented globally due to the former ones' limited spatial coverage and the later ones' complexity. In this study, we further examine the sensitivity of a newly developed remote sensing index, Normalized Difference Latent Heat Index (NDLI), on the potential ET against the other existing indexes in the literature. Results confirm that the newly developed multi-band NDLI exhibits the highest correlation with ET derived from the Surface Energy Balance Algorithm for Land (SEBAL) model as compared to the other indexes. The correlation coefficients are approximately 0.70 and 0.71 for the eastern part of Chiayi City, Taiwan and Thai Binh Province, Viet Nam, respectively.

The increase in the urban population and number of severe weather extremes (including excessive heat) potentially linked to climate change has highlighted the demand of research into assessment and risk mitigation solutions. One of the naturally-based solutions for urban heat stress risk is to enhance the effectiveness of urban greenspaces to mitigate the adverse effects of urbanization and its consequence of urban heat island effect in a sustainable way. The focus of this paper is to investigate the urban greenspaces’ features in urban area of Taipei by using high-resolution bands of Sentinel 2 images, which are freely accessible. We present a composite index aggregating four factors including proximity to urban greenspaces, land surface temperature, population density, and land cover to identity patterns of heat stress risk. Results show that urban greenspaces derived from Sentinel 2 images achieved high accuracy with Kappa coefficient of 0.98 and overall accuracy of 99%. Analysis results of heat stress risk indicate that the level of risk is closely correlated with the accessibility to urban greenspaces. It is advised that Taipei City Government and decision makers should take serious measures to enhance the greenness of the environment and effectiveness of urban greenspaces for the benefits of citizens’ health and well-beings. Also, it is indicated that Sentinel 2 data are well suitable for not only visualizing the urban greenspaces, but also offering the guidance for landscape planning and management at city scale.

Latent heat is the energy released or absorbed by a substance through phase change without changing its temperature. Its flux is a crucial element of the hydrological cycle at the land-air interface. Many water-related indexes have been proposed as indicators for latent heat flux extraction from satellite imagery, while the extraction accuracy still remains a space to improve nowadays. In this paper, a new multiband index, called normalized difference latent heat index (NDLI), is proposed for remote sensing of land surface heat flux. It utilizes the reflectance observations of three bands from Landsat 8 operational land imager, including red, green, and shortwave infrared channels. Its performance in terms of deriving latent heat flux through their incorporation into the commonly used surface energy balance algorithm for land (SEBAL) is then compared with the other three existing water-related indexes with two of them using normalized difference water index and normalized difference vegetation index. Results show that NDLI exhibits the strongest correlation with the SEBAL-derived latent heat flux among the used water-related indexes with correlation coefficient r = 0.75. They also indicate that the NDLI is the most sensitive and reliable index outperforming the previously developed indexes to determine the characteristics of water content in different land cover types. It is concluded that NDLI can be used as a good indicator to represent the potential latent heat flux at the earth's surface.

We utilize information derived from Landsat satellite images to produce eco-environmental vulnerability maps by considering four main factors: hydrometeorology, topography, land resources, and socioeconomics. Twin Taipei Cities are chosen for our study. Their eco-environmental vulnerability assessment (EVA) is first performed. Subsequently, classified regions in needs of environmental protection are proposed. We found that high vulnerable levels are observed in the Taipei Metropolis, consisting of Taipei City and its transboundary regions with New Taipei City as a result of high-density buildings and urbanization with low vegetation. Lowlands in the metropolitan area with concentration of impervious surface might lead to flood if heavy rainfall events occur. In contrast, the regions outside Taipei Metropolis, especially in southwest and southeast parts, have higher quality eco-environment due to better-protected forest areas with lower intervention of human activities. Information of suggested ecological zoning regions is useful for Twin Cities' governments to understand their eco-environmental vulnerability conditions.

Complex interactions of ecological and climate indicators playa vital role in defining spatiotemporal patterns of vector-borne disease habitats and transmission. Vietnam is in the sub-tropical climate zone with high humidity and abundant precipitation, while unevenly distributed precipitation leads to frequently annual occurrence of drought and flooding. The increase in frequency and magnitude of severe weather extremes likely linked to climate change and anthropogenic processes highlights the demands of research in GeoHealth risk and adaptive capacity assessment. In this study, mapping vector-borne disease is made for revealing physical features of ecological indicators and its association with vector-borne diseases and adaptive capacity to illustrate how remotely sensed data products have been utilized in GeoHealth applications, surveillance, and vector-borne disease risk mapping in Vietnam. Additionally, it is to promise the possibilities of establishing disease early-warning systems with citizen participation and provide knowledge to support ecological and human health diseases research.

This paper extends a former study on supertyphoon's formation in winter to its seasonal dependence on distribution and profiles. It aims to enhance understanding and predictability of the super-typhoons to reduce their impacts on human beings and properties in the regions where they pass by or influence. A framework by integrating remote-sensing imagery and image-processing techniques is implemented to analyze the seven super-typhoons of concern in both summer and winter seasons in the northwestern Pacific Ocean. Conventionally, typhoons occur in summer in the Northern Hemisphere, while owing to increased climate variability in recent decades, the occurrence of typhoons in winter is increased and a large number of super-typhoons are observed. Surprisingly, three summer super-typhoons-Neoguri (July), Rammasun (July), and Halong (August)-and four winter super-typhoons-Phanfone (October), Vongfong (October), Nuri (November), and Hagupit (December)-happened in 2014. Their physical responses of tracks, distributions, profiles, and intensifications are examined. The 3-D profiles of typhoons derived from surface cloud images are used to investigate the in-depth distributions of the cloud tops. Results show that southwest airflows, and cold fronts and southwest airflows enhance the summer and winter typhoons, respectively, to become super-typhoons eventually within the zones of lat 15°-20° and 10°-20°N and long 110°-135° and 133°-138°E in summer and winter, respectively. The lat 20°N is likely the boundary for the super-typhoons' occurrence and a turning point of the typhoons' two major tracks, with the first track moving in the westerly direction and the second track inclining in the northerly direction. Three crucial findings are obtained. First of all, it is observed that Track 1 inclined toward the latitude with an inclination angle θ1and Track 2 inclined toward Track 1 with an inclination angle θ2. Using both inclination angles, the patterns of the super-typhoons were approximately described. Second, the inclination angle θ1 of the winter super-typhoons is represented with smaller values and a narrower range (10°-26°) than those (13°-40°) of the summer super-typhoons. After the turning point, both inclination angles θ2of winter and summer super-typhoons varied with a wider range of 25°-65° and -35° to 50°, respectively. The winter super-typhoons exhibit higher intensities than the summer super-typhoons. Third, it is further confirmed that a mechanism through which cold fronts and southwest airflows enhance the typhoons causes them to become super-typhoons. The cold front is a new aspect of the leading edge of cooler air masses in typhoon environments exerting a greater temperature gradient between the front and typhoon circulation that ultimately affects the track of typhoons and increases the intensity of typhoons.

Groundwater Arsenic (As) contamination in three groundwater tables (Holocene (qh), Pleistocene (qp2-3), and Pleistocene (qp1)) was examined by using 300, 423, and 204 samples in qh, qp2-3, and qp1, respectively, in Hanoi City in 2000-2003. Land subsidence was derived from JERS-1 images in 1995-1998, and Envisat-ASAR from Jan to April in 2004. Results show that Arsenic was lower in surface water and wastewater than groundwater. Also, Arsenic in groundwater was lower near surface (qh) than far from surface (qp2-3and qp1). Land subsidence was significant in downtown areas where large and medium groundwater supply stations are located. Spatial correlation between Arsenic in qh, qp2-3, and qp1and land subsidence indicates more subsidence was observed in regions with higher Arsenic contamination. Such findings support the hypothesis that increasing groundwater abstraction results in enhancing oxygenation to boost redox reaction to release Arsenic from ore into groundwater.

This paper incorporates remote sensing imagery and image processing techniques to analyze typhoons' evolution into supertyphoons through interactions with cold fronts in the Western Pacific Ocean. The purpose is to enhance understanding and predictability of the tracks and profiles of supertyphoons. Evolutions of typhoons Haiyan (2013) and Hagupit (2014) into supertyphoons were studied. The 3-D profiles of weather systems were reconstructed using multifunctional transport satellite IR cloud images. When interactions between typhoon and cold front happened, enhancements of typhoons were observed causing typhoons Haiyan and Hagupit to strengthen power and evolve into supertyphoons. The origins of typhoons Haiyan and Hagupit are closely located at 152°50E/05°12N and 151°30E/04°19N, respectively. Both typhoons went through The Philippine Sea in the zone of 112°52E-146°11E. The lowest values of central pressures of typhoons Haiyan and Hagupit occurred along their paths at the positions 128°41E/10°16N and 131°06E/11°00N, respectively. Their distances from cold front and average speed of their movement were dominating parameters for the formation of winter supertyphoons. The supertyphoon Haiyan showed higher intensity than the supertyphoon Hagupit. Each of the two typhoons roughly consisted of two paths with the first path moving westerly and the second path northerly. The two typhoons evolved into supertyphoons at the turning points of the two paths. The first paths were about 2330 and 2050 km for typhoons Haiyan and Hagupit, respectively.

To continue the groundwater monitoring networks in the plain areas for further demand of clean water resources, the groundwater exploration of mountainous regions becomes a crucial issue in Taiwan. In this study, 75 boreholes were drilled for conducting investigation by the integration of subsurface exploration technologies, including drilling, core inspection, geophysical logging, and hydraulic testing. Besides, 48 groundwater monitoring wells were constructed with pumping testing. For further understanding the characteristic of potential groundwater areas, analysis of hydrogeological unit (HGU) and Landsat imagery layer stacking were considered. Results show that the trend of potential well yields in each rock type unit is related with features extracted from Landsat images.

This paper presents a framework of using remote sensing imagery and image processing techniques to analyze the interaction among typhoons or tropical cyclones (TCs) with and without the involvement of mid-tropical depressions (TDs) or tropical storms (TSs). The cyclone-cyclone interaction (named the Fujiwhara effect) could happen when two TCs are located at a distance within 1400 km. However, when there exists a mid-TD between the two TCs even with a distance of more than 1400 km, but usually within a distance of 1500-2700 km, the mid-TD may induce impacts on the two cyclones, defined as a TD-inlaid cyclone-cyclone interaction. The existence of the mid-TD significantly complicates weather forecasting due to difficulty in modeling its influence on the two cyclones. In this paper, we proposed innovative empirical formulas of threshold distance to characterize cyclone-cyclone interactions either with or without the involvement of the TD(s). We generalize empirical formulas for interaction distances between TCs and TD/TS related to current-intensity number, size factor, height difference, and rotation factor. To validate the appropriateness of the generalized empirical formulas, we choose and analyze seven sets of two-typhoon events. It is shown that the generalized formulas successfully predicted the impact of mid inlaid TC/TS quantitatively. The mid-TD's effects on the two cyclones represent a new aspect and a significantly improved understanding for the prediction of more complicated cyclone-cyclone interactions. Note that the empirical formulas are derived from a very small set of events and that they must be verified with a sufficient data set for future practical use.

A global sensitivity analysis utilizing the extended Fourier amplitude sensitivity test is used to determine the parameter sensitivity of the L-band microwave emission of the biosphere (L-MEB) model. The results are analyzed from two perspectives of calibration and inversion. First, the parameters of surface soil moisture, soil roughness factor, vegetation optical depth at nadir, and effective land surface temperature are the four most sensitive parameters in the L-MEB model, demonstrating their possibility to be retrieved in the multiparameter retrieval approaches. Then, the high total sensitivity index (TSI) values of surface soil temperature in the analyses emphasize the importance of high-precision land surface temperature data in the surface soil moisture retrievals, especially for rougher or more vegetated surface conditions. Finally, our analysis indicates that TSI values are high for the soil surface roughness and vegetation optical depth model parameters but low for the vegetation structure, single scattering albedo, and soil roughness coefficient model parameters at incidence angles near nadir. This suggests that calibration experiments performed at small incidence angles may be appropriate for some but not all of the model parameters, which characterize the effect of soil surface roughness and vegetation on the terrestrial brightness temperature. Consequently, new calibration procedures that account for the different relative sensitivities of these model parameters at larger incidence angles may need to be developed in the future.

Landsat Data Continuity Mission's or Landsat 8 was launched into orbit on February 11, 2013. It opened up a new diverse range of data for natural environment research, disaster monitoring, and risk assessment using satellite-derived environmental and climatic variables. In this paper, it is our intention to present major potentials of Landsat 8 data to provide environmental variables and to propose a framework for environmental risk assessment. Taoyuan city of Taiwan that has been experiencing frequent natural disasters and environmental problems is chosen as a case study.

This paper presents a framework for using remote sensing imagery and image processing techniques to analyze the interactions among tropical depressions (TDs) and typhoons. The purpose is to properly understand and predict this phenomenon, in order to reduce their influence on the regions where they pass by. Improvements on the understanding of the interactions among TDs and typhoons are obtained and presented. They carry the potential to improve forecast of severe weather system. In this paper, we analyzed the physical responses of TDs on two typhoons Tembin and Bolaven (2012), including TD's appearance, development, interaction, and mergence. According to the Central Weather Bureau of Taiwan, Tembin and Bolaven were formed about 1550 km apart. In general, the Fujiwhara effect happens when two storms are within a distance of 1400 km. Does the TD have impacts on the typhoons, and could we also call this impact as Fujiwhara effect? The 3-D profiles of the weather systems are reconstructed using the multifunctional transport satellite (MTSAT) infrared cloud images by the image reconstruction technique (IRT). The 3-D profiles of typhoons were implemented for investigating the in-depth distribution of the cloud top from a surface cloud image. The main parameters, which have been analyzed in this paper, are distance, height difference, rotation, and sizes of the two TDs and the two typhoons. The results showed that two TDs occurred between typhoons Tembin and Bolaven, which interacted with each other through upward convection and attraction between two typhoons and the TDs, respectively. The mergence of the two TDs caused the typhoons to start rotating and generate strengthened power. The findings of the TD effects on two typhoons represent a new aspect of the interaction between TD and typhoon.

Land subsidence in western Taiwan has been an issue since 1970's due to over extraction of ground water for aquaculture. This problem became serious in Yunlin County because high-speed rail way transects the center of land subsidence which may cause safety problem. Leveling survey is applied annually. A more efficient method with 6 continuous GPS stations is used in this study to observe the subsidence. The effectiveness and advantages are compared with leveling survey and underground monitoring wells in the period from 2007 to 2012. It is concluded that the trend of the subsidence can be fully observed by the GPS network and the observation frequency of GPS prevails the annual survey of traditional approach which is an important factor for early warning purposes.

A fundamental principle of local interaction of radio waves with a refractive spherical medium is formulated and illustrated using the radio occultation (RO) method of remote sensing of the atmosphere and the ionosphere of the Earth and the planets. In accordance with this principle, the main contribution to variations of the amplitude and the phase of radio waves propagating through a medium makes a neighborhood of a tangential point, where the gradient of the refractive index is perpendicular to the radio wave trajectory. A necessary and sufficient condition (a criterion) is established to detect the displacement of the tangential point from the radio ray perigee using analysis of the RO experimental data. This criterion is applied to the identification and the location of layers in the atmosphere and the ionosphere by the use of Global Positioning System RO data. RO data from the CHAllenge Minisatellite Payload (CHAMP) are used to validate the criterion introduced when significant variations of the amplitude and the phase of the RO signals are observed at the RO ray perigee altitudes below 80 km. The detected criterion opens a new avenue in terms of measuring the altitude and the slope of the atmospheric and ionospheric layers. This is important for the location determination of the wind shear and the direction of internal wave propagation in the lower ionosphere and possibly in the atmosphere. The new criterion provides an improved estimation of the altitude and the location of the ionospheric plasma layers compared with the backpropagation radio-holographic method previously used.

Northeastern Thailand is one of the representative rainfed lowland rice agriculture areas in Asia, where rice yield is limited due to unstable rainfall and poor soil. The area of rainfed lowland rice in northeast Thailand is approximately 5.27 million hectares, representing 57% of rice-growing area of the country. Heavy monsoon rainfall over central and northern Thailand began in July 2011 and lasted until October, causing a great impact on national agriculture. Huge tracts of farmland are submerged, threatening the annual rice crop. The objective of this paper is to assess the damage of regional rainfed rice after severe floods by using MODIS Surface Reflectance 8-Day L3 Global 250 m (MOD09Q1) and 500 m (MOD09A1). During the rice flooding period, Land Surface Water Index (LSWI) values are increased and even become higher than vegetation indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)). For this reason, the rice flooding period is a crucial indicator to identify the rice. The algorithm for mapping rice paddy uses time-series MODIS retrievals to identify the rice paddy in northeastern Thailand. The result indicates that the MODIS-derived rice evaluations are useful for obtaining spatial distribution maps of rice on a large-scale region.

In this research, a ray tracing model is built up to simulate the propagation of signal in global positioning system (GPS) radio occultation (RO) mission. When GPS signal propagate through the Earth's atmosphere, it will be bent and delayed due to the gradient atmosphere refractive index and received by low Earth orbit (LEO) satellite. Then the parameter profiles of atmosphere can be retrieved by using the received signal. In the previous research, in order to simplify the simulation, the Earth's atmosphere is assumed as spherical symmetry and the positions of GPS and LEO satellites are not considered in simulations. In the model, the shape of the Earth is assumed as an ellipse. The information of European Centre for Medium-Range Weather Forecasts (ECMWF) is used to construct the refractive index of Earth's atmosphere. And two aiming algorithm are developed to control the initial propagating direction of GPS signal to begin from the prescribed GPS satellite position and end at the LEO satellite position. The model is tested and verified by comparing with analytical and observational data.

Tropical countries are facing a rapid change in land use/ land cover with increasing water demand for agriculture and industry. Hydrological issues thus become more and more important in these regions than ever before. The root zone soil moisture content (SMC) derived from our proposed retrieval algorithm in conjunction with the apparent thermal inertia approach and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery from 2004 to 2008 over the tropical catchment in northern Thailand was presented in this study. The results showed that the Pearson correlation coefficient and Nash-Sutcliffe efficiency coefficient between ground truth and retrieved SMC under all different land cover sites varied respective 0.81-0.88 and 0.44-0.67. The retrieved precision referred to Root Mean Square Error value varied between 0.012 and 0.058 (m3· m-3). The successful root zone SMC estimation by using MODIS data will contribute to water management of the tropical or subtropical hydrological studies in the future.

The ionosphere and atmosphere may have significant impacts on the high-stable navigational signals of the Global Positioning System (GPS) in the communication link satellite to satellite. The classification of the different types of the ionospheric impact on the phase and amplitude of the GPS signals at altitudes of 40-90 km is introduced using the CHAllenging Minisatellite Payload (CHAMP) radio occultation (RO) data. An analytical model is elaborated for the description of the radio wave propagation in the stratified ionosphere and atmosphere. The propagation medium consists of sectors having the spherically symmetric distributions of refractivity. The newly developed model presents analytical expressions for the phase path and refractive attenuation of radio waves. The model explains significant amplitude and phase variations at altitudes of 40-90 km of the RO ray perigee associated with the influence of the inclined ionospheric layers. An innovative eikonal acceleration technique is described and applied to the identification and location of the inclined ionospheric layers using the comparative analysis of the amplitude and phase variations of the RO signals.

Evapotranspiration (ET) plays a major role in the energy and water balances of the hydrological cycle. Monitoring ET at a regional level has become feasible with the advance of remote-sensing technology. This letter presents a module that incorporates satellite images, surface meteorological data, and topographic information in estimating ET over a tropical montane watershed. The satellite images used include Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper Plus, and Advanced Spaceborne Thermal Emission and Reflection Radiometer with visible, near infrared, shortwave infrared, and thermal infrared bands. The estimated surface energy fluxes are compared with thein situmeasurements. The results demonstrate that, compared to other model estimations, the proposed module with terrain correction provides the highest correlation (r= 0.75) between the estimated latent heat flux associated with ET and its correspondingin situmeasurement. The proposed module will be further refined and applied to monitor long-term ET over mountainous watersheds.

Radio-occultation (RO) technique, which has been used in planetary science, is a method to obtain the profiles of atmosphere and the global atmospheric data. In 2006, Taiwan launched six low-Earth-orbit satellites as a RO constellation mission, known as FORMOSAT-3. In order to thoroughly understand the process of the RO data retrieval and obtain as much as possible the information for the weather prediction, a National Central University Radio Occultation (NCURO) retrieval algorithm has been developed. The focus of the algorithm development is on the correction of the excess phase of the signal received with open-loop (OL) technique and the criteria for assessment of the data quality. When the OL is activated, the excess phase of the Global Positioning System (GPS) signal is modulated with navigation messages of satellites. In our algorithms, two methods are incorporated to recover the excess phase. Moreover, as the altitude of the received signal decreases, the quality of the GPS signal generally deteriorates, and eventually, the signal is too noisy to be processed. In order to assess the quality of the signal, instead of the signal-to-noise ratio, the degree of unclearness is defined and used in the algorithm. In this paper, the algorithm including the phase-correction methods and the criteria for the quality assessment will be described. The data retrieval using the algorithm will be compared with those obtained from the COSMIC Data Analysis and Archive Center at the University Corporation for Atmosphere Research and Pingtung radiosonde measurement. Some intermediate results of the NCURO algorithm will also be demonstrated.

The FORMOSA Satellite Series No. 3/Constellation Observing System for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC) spacecraft constellation consisting of six low-earth-orbiting satellites is the world's first operational Global Positioning System (GPS) radio occultation mission. The mission has been jointly developed by the National Space Organization of Taiwan and the University Corporation for Atmospheric Research of the U.S. in collaboration with the Jet Propulsion Laboratory, NASA, and the Naval Research Laboratory for three onboard payloads, including a GPS Occultation Receiver, a triband beacon, and a tiny ionospheric photometer. The FORMOSAT-3/COSMIC mission was successfully launched from Vandenberg into the same orbit plane of the designated 516-km circular parking orbit altitude on April 15, 2006. After the six satellites completed the in-orbit checkout activities, the mission was started immediately at the parking orbit for in-orbit checkout, calibration, and experiment of three onboard payloads. Individual spacecraft thrust burns for orbit raising were performed to begin the constellation deployment of the satellites into six separate orbit planes. All six FORMOSAT-3/COSMIC satellites are maintained in a good state of health except spacecraft flight model no. 2, which has had power shortages. Five out of the six satellites had reached their final mission orbits of 800 km as of November 2007. This paper provides an overview of the constellation spacecraft design, constellation mission operations, constellation deployment timeline evolution, associated spacecraft mass property and moment of inertia results, orbit-raising challenges, and lessons learned during the orbit-raising operations.

The FORMOSAT-3 mission, also known as constellation observing system for meteorology, ionosphere, and climate (COSMIC), is the third major project of the Formosa satellite (FORMOSAT) series implemented by the National Space Organization of Taiwan. FORMOSAT-3/COSMIC is a joint Taiwan/U.S. mission consisting of six identical low Earth orbit satellites. All six cluster satellites were successfully launched by a single Minotaur launch vehicle on April 15, 2006. The retrieved Global Positioning System (GPS) radio occultation (RO) data have been freely available online to the science community since shortly after the completion of satellite bus in-orbit checkout. Having completed the verification and validation, the worldwide science communities are highly satisfied with the RO data. Scientists have hailed the RO sensors as offering the most accurate, precise, and stable thermometers in space. After one year in orbit, all six FORMOSAT-3/COSMIC satellites were in good condition (except FM2, which had power shortage issues) and were on their way toward the final constellation of six separate orbit planes with 30degseparation. Four out of six satellites had already reached their final mission orbit of 800 km by mid-May 2007. Together, the six satellites have generated a total of more than 2500 RO data per day. However, only 50%-70% of the RO data as received one year after launch could be retrieved into useful atmosphere profiles. The retrieved RO data, about 1800 per day on average, have been assimilated into numerical weather prediction models by many major weather forecast centers and research institutes. This paper provides an overview of the constellation mission, the spacecraft system performance after one year in orbit, the technical challenges we have encountered, and the performance enhancements we have accomplished.

The microwave remote sensing has long been considered as an important technique to determine and quantify the state and amount of soil moisture. With the improved knowledge of land-air interaction and the advance of sensor technology, using microwave remote sensing to estimate the soil moisture has achieved a practical level. In this paper, we compare and evaluate four soil moisture retrieval algorithms developed by Jackson [1], Liou et al. [2], Macelloni et al. [3] and Paloscia et al. [4]. Data used to analyze the performance of these retrieval algorithms includes TRMM-TMI data acquired in 1998 and 2001, and AMSR-E data taken in July 2006. The result indicates that the soil moisture retrieval algorithm developed by Liou et al. [2] has better correlation and less bias as compared with the in situ measurement. The Liou model is further applied to study the spatial variability of the soil moisture in the Tibetan Plateau and has achieved a promising result. In future, we attempt to further refine the Liou model for global soil moisture study.

The sea ice in polar region has been shrunk and thinned considerably with the rampant global temperature during the last few decades. It has already shown dramatic effects on regional water balance, ocean circulation and global climate. Nowadays, an extensive investigation and understanding of the environmental change in polar region becomes a crucial task. However, difficulties resulting from transportation, extreme weather, and seasonal cycle, as well as the lack of satellite coverage make the polar research extremely challenging. Successful launch of FORMOSAT-2 satellite by National Space Organization, Taiwan (NSPO) in 2006 is supposed to resolve the limitation of polar observation by providing daily high resolution images (8 m MS and 2 m Panchromatic) near two poles. This paper presents preliminary findings from time-series FORMOSAT-2 images over the Arctic sea and Amundsen-Scott South Pole Station. Results indicate that wind erosion and ocean circulation shear force may take part in shaping the Arctic ice. In addition, the wind field over the Antarctic may be estimated.

Positioning accuracy by the Global Positioning System (GPS) is of great concern in a variety of research tasks. It is limited due to error sources such as ionospheric effect, orbital uncertainty, antenna phase center variation, signal multipath, and tropospheric influence. In this study, the tropospheric influence, primarily due to water vapor inhomogeneity, on GPS positioning height is investigated. The data collected by the GPS receivers along with co-located surface meteorological instruments in 2003 are utilized. The GPS receivers are established as continuous operating reference stations by the Ministry of the Interior (MOI), Central Weather Bureau (CWB), and Industrial Technology Research Institute (ITRI) of Taiwan, and International GNSS Service (IGS). The total number of GPS receivers is 21. The surface meteorological measurements include temperature, pressure, and humidity. They are introduced to GPS data processing with 24 tropospheric parameters for the station heights, which are compared with those obtained without a priori knowledge of surface meteorological measurements. The results suggest that surface meteorological measurements have an expected impact on the GPS height. The daily correction maximum with the meteorological effect may be as large as 9.3 mm for the cases of concern.

The Formosa Satellite-3 and Constellation Observing System for the Meteorology, Ionosphere, and Climate (FORMOSAT-3/COSMIC) radio occultation (RO) mission has been successfully launched on April 14, 2006. The FORMOSAT-3/COSMIC mission uses global positioning system (GPS) signals to study the atmosphere and the ionosphere with global coverage. Receivers that are installed onboard of the six small FORMOSAT-3/COSMIC satellites register the phase and the amplitude of radio waves at two GPS frequencies. We give a preliminary analysis of the first RO measurements that are provided by the FORMOSAT-3/COSMIC mission. The geographical distribution of the first FORMOSAT-3/COSMIC RO experiments is shown. We demonstrate that the performance of the first measurements allows obtaining the vertical profiles of the refractivity, temperature, and pressure for the considered FORMOSAT-3/COSMIC RO events with expected accuracy, which is quite similar to the accuracy of the previous Challenging Mini-Satellite Payload and Gravity Recovery and Climate Experiment RO missions. New elements in the RO technology are suggested for further improving the accuracy and broadening the application range of the RO method. We emphasize new directions in applying the RO method to measure the vertical gradients of the refractivity in the atmosphere, to determine the temperature regime in the upper stratosphere, and to investigate the internal wave activity in the atmosphere. We find a significant correlation between the phase acceleration and the intensity variations in the RO signals that are emitted by GPS satellites and registered by the FORMOSAT-3/COSMIC satellites. This correlation opens a way to locate the layered structures in the propagation medium based on simultaneous observations of the radio wave intensity and the phase variations in trans-ionospheric satellite-to-satellite links.

Temperature inversion indicates that the atmospheric temperature decreases with increasing height. Its occurrence tends to inhibit vertical motion of the atmosphere. Under the occurrence of temperature inversion, air pollutants cannot be dissipated through vertical mixing of the atmosphere and are accumulated near the surface. When temperature inversion lasts for a long time, human health can be in jeopardy due to deterioration of air quality and secondary pollutants, which are formed through atmospheric photochemistry and more toxic than original ones. It is vital to investigate the dynamics of temperature inversion for understanding and resolving its resulting problems. In this paper, temperature inversion signatures over three major cities on Taiwan are analyzed. They are measured by ground- based microwave radiometers installed in Taipei, Taichung and Kaohsiung from 2002 to 2004 supported by the Environment Protection Agency (EPA) of Taiwan. Characteristics of temperature inversion at the three cities are extracted using different classification methods. The characteristics of temperature inversion in Taichung and Kaohsiung show a similar trend but are different from that in Taipei. The numbers of the occurrence of temperature inversion in Taichung and Kaohsiung were much larger than that in Taipei. The main types of temperature inversion in Taiwan are radiation and frontal inversions. Compared to frontal inversion, radiation inversion on average occurs at a lower altitude, lasts a longer period, has a deeper thickness, and reaches a higher temperature difference of inversion. Frontal inversion plays a significant role for the inversion event lasting over 12 hours.

Physically based land surface process/radiobrightness (LSP/R) models may characterize well the relationship between radiometric signatures and surface parameters. They can be used to develop and improve the means of sensing surface parameters by microwave radiometry. However, due to a lack in the skill to properly understand the behavior of the data, a statistical approach is often adopted. In this paper, we present the retrieval of wheat plant water content (PWC) and soil moisture content (SMC) profiles from the measured H-polarized and V-polarized brightness temperatures at 1.4 (L-band), and 10.65 (X-band) GHz by an error propagation learning back propagation (EPLBP) neural network. The PWC is defined as the total water content in the vegetation. The brightness temperatures were taken by the PORTOS radiometer over wheat fields through three month growth cycles in 1993 (PORTOS-93) and 1996 (PORTOS-96). Note that, through the neural network, there is no requirement of ancillary information on the complex surface parameters such as vegetation biomass, surface temperature, and surface roughness, etc. During both field campaigns, the L-band radiometer was used to measure brightness temperatures at incident angles from 0 to 50/spl deg/ at L-band and at an incident angle of 50/spl deg/ at X-band. The SMC profiles were measured to the depths of 10 cm in 1993 and 5 cm in 1996. The wheat was sampled approximately once a week in 1993 and 1996 to obtain its dry and wet biomass (i.e., PWC). The EPLBP neural network was trained with observations randomly chosen from the PORTOS-93 data, and evaluated by the remaining data from the same set. The trained neural network is further evaluated with the PORTOS-96 data.

L-band brightness predicted by the land surface process/radiobrightness (LSP/R) model for prairie grassland appears to be somewhat lower than expected. A crucial reason for the underestimate of the L-band brightness is that the soil surface was treated as smooth. In this paper, surface scattering of the soil determined by the IEM model is incorporated into the LSP/R model to examine its impact on the predicted L-band brightness. Eight sets of surface parameters, two correlation lengths (L) of 3 and 6 cm/spl times/4 root mean squared (RMS) heights (/spl sigma/) of 0.3, 0.6, 0.8, and 1.0 cm, are utilized to characterize the emission of the soil surface. It is found that H-polarized, L-band brightness is expectedly increased by different levels for all of the eight rough surface cases compared to the smooth surface case. The increase in the average of the H-polarized, L-band brightness is by as much as 13.2 K for the case with L=3 cm and /spl sigma/=1.0 cm. In addition, L-band's sensitivity to soil moisture is found to be approximately equal with and without the scattering effects. An increase in H-polarized, L-band brightness by about 12 K at the end of a 14-day simulation by the LSP/R model is in response to a decrease in soil moisture by 7% for all of the nine cases of concern (eight rough plus one smooth soil surfaces).

The authors present the retrievals of surface soil moisture (SM) from simulated brightness temperatures by a newly developed error propagation learning backpropagation (EPLBP) neural network. The frequencies of interest include 6.9 and 10.7 GHz of the advanced microwave scanning radiometer (AMSR) and 1.4 GHz (L-band) of the soil moisture and ocean salinity (SMOS) sensor. The land surface process/radiobrightness (LSP/R) model is used to provide time series of both SM and brightness temperatures at 6.9 and 10.7 GHz for AMSRs viewing angle of 55/spl deg/, and at L-band for SMOS's multiple viewing angles of 0/spl deg/, 10/spl deg/, 20/spl deg/, 30/spl deg/, 40/spl deg/, and 50/spl deg/ for prairie grassland with a column density of 3.7 km/m/sup 2/. These multiple frequencies and viewing angles allow the authors to design a variety of observation modes to examine their sensitivity to SM. For example, L-band brightness temperature at any single look angle is regarded as an L-band one-dimensional (1D) observation mode. Meanwhile, it can be combined with either the observation at the other angles to become an L-band two-dimensional (2D) or a multiple dimensional observation mode, or with the observation at 6.9 or 10.7 GHz to become a multiple frequency/dimensional observation mode. In this paper, it is shown that the sensitivity of radiobrightness at AMSR channels to SM is increased by incorporating L-band radiobrightness. In addition, the advantage of an L-band 2D or a multiple dimensional observation mode over an L-band 1D observation mode is demonstrated.

The authors' point-scale Land Surface Process/Radiobrightness (LSP/R) model for a prairie grassland in the northern Great Plains was adapted to winter wheat-stubble within the region of the Southern Great Plains 1997 (SGP'97) Hydrology Experiment. The model maintains running estimates of near-surface soil moisture and stored water in soil and vegetation when forced by weather, and predicts the microwave brightness of the terrain. LSP/R model predictions were compared with the field observations recorded during SGP'97. The model captures canopy and soil temperatures very well, with the maximum mean and variance of the difference between the model and field temperatures being 1.06 K and 3.28 K/sup 2/, respectively. It yields reasonable predictions for the moisture in deeper layers of the soil, but its predictions for the moisture in the upper layers are low by /spl sim/2.3% by volume. These underpredictions of near-surface soil moisture result in higher H-pol brightnesses at 19 GHz than those observed.

The authors present a biophysically based, one-dimensional hydrology/radiobrightness (1dWR) model for prairie grassland that is subject to solar heating, radiant heating and cooling, precipitation, and sensible and latent heat exchanges with the atmosphere. The 1dH/R model consists of two modules, a one-dimensional hydrology (1dH) module that estimates the temperature and moisture profiles of the soil and the canopy and a microwave emission module that predicts radiobrightness (R). The authors validate the 1dH/R model by comparing its predictions with data from a field experiment. The model was forced by meteorological and sky radiance data from their Radiobrightness Energy Balance Experiment (REBEX-1) on prairie grassland near Sioux Falls, SD, during the fall and winter of 1992-1993. Model predictions were compared with 995 consecutive REBEX-1 observations over a 14-day period in October. Average errors (predicted-measured) for canopy temperature are 1.1 K with a variance of 3.72 K/sup 2/, for soil temperatures at 2-, 4-, 8-, 16-, 32-, and 64-cm depths are 2 K with a variance of 4 K/sup 2/, and for H-polarized brightnesses are 0.06 K with a variance of 1.30 K/sup 2/ at 19 GHz and 6.01 K with a variance of 6.04 K/sup 2/ at 37 GHz. The model overestimates the 37-GHz brightness because they have not included scatter darkening within the vegetation canopy in the model. They use the 1dH/R model to simulate a 60-day dry-down of prairie grassland in summer. For grass with a column density of 3.7 kg/m/sup 2/ and soil with an initially uniform moisture content of 38% by volume, the upper 5 mm of soil dries to 27% by the end of the simulation. The corresponding L-band brightness increases from an initial 143 K to a final 163 K. In contrast, none of the special sensor microwave/imager (SSM/I) radiobrightnesses nor the radiobrightness thermal inertia (RTI) technique, either at L-band or at any SSM/I frequency, exhibits significant sensitivity to the soil dry-down.

A biophysically-based land-surface process/radiobrightness (LSP/R) model is integrated with a dynamic learning neural network (DLNN) to retrieve the land-surface parameters from its radiometric signatures. Predictions from the LSP/R model are used to train the DLNN and serve as the reference for evaluation of the DLNN retrievals. Both horizontally polarized and vertically polarized brightnesses at 1.4 GHz, 19 GHz, and 37 GHz for an incidence angle of 53/spl deg/ make up the input nodes of the DLNN. The corresponding output nodes are composed of land-surface parameters, canopy temperature and water content, and soil temperature and moisture (uppermost 5 mm). Under no-noise conditions, the maximum of the root mean-square (RMS) errors between the retrieved parameters of interest and their corresponding reference from the LSP/R model is smaller than 28 for a four-channel case with 19 GHz and 37 GHz brightnesses as the inputs of the DLNN. The maximum RMS error is reduced to within 0.5% if additional 1.4 GHz brightnesses are used (a six-channel case). This indicates that the DLNN produces negligible errors onto its retrievals. For the realization of the problem, two different levels of noises are added to the input nodes. The noises are assumed to be Gaussian distributed with standard deviations of 1 K and 2 K. The maximum RMS errors are increased to 9.3% and 10.3% for the 1 K-noise and 2 K-noise cases, respectively, for the four-channel ease. They are reduced to 6.0% and 9.1% for the 1 K-noise and 2 K-noise cases, respectively, for the six-channel case. This is an implication that 1.4 GHz is a better frequency in probing soil parameters than 19 GHz and 37 GHz.

Heat and moisture transport in soil are coupled processes that jointly determine temperature and moisture profiles. The authors present a physically based, one-dimensional (1D), coupled heat and moisture transport hydrology (1-DH) model for bare, unfrozen, moist soils subject to insolation, radiant heating and cooling, and sensible and latent heat exchanges with the atmosphere. A 60-day simulation is conducted to study the effect of dry-down on soil temperature and moisture distributions in summer for bare soil in the Midwest United States. Given a typical initial moisture content of 38% by volume, the authors find that temperature differences between the water transport and no water transport cases exhibit a diurnal oscillation with a slowly increasing amplitude, but never exceed 4.4 K for the 60-day period. However, moisture content of the surface decreases significantly with time for the water transport case and becomes only about 21% at the end of the same period. The 1-DH model is linked to a radiobrightness (1-DH/R) model as a potential means for soil moisture inversion. The model shows that radiobrightness thermal inertia (RTI) correlates with soil moisture if the two radiobrightnesses are taken from times near the thermal extremes, e.g., 2 a.m. and 2 p.m., and that RTI appears temperature-dependent at the ending stages of the drydown simulations where soils are dry and their moisture contents vary slowly. Near times of thermal crossover, the RTI technique is insensitive to soil moisture.

Phase change of water is an important sink and source of energy and moisture within soils as well as a significant influence upon soil temperature and moisture profiles. These profiles play a crucial role in governing energy and moisture fluxes between bare soils and the atmosphere. They also codetermine radiobrightness, so that the difference between modeled and observed radiobrightness becomes a measure of error in a model's estimate of temperature or moisture. The authors present a physically based, coupled-heat and moisture-transport, one-dimensional hydrology/radiobrightness (1 dH/R) model for bare, freezing and thawing, moist soils that are subject to insolation, radiant heating and cooling, and sensible and latent heat exchanges with the atmosphere. They use this model to examine thermal, hydrologic, and Special Sensor Microwave/Imager (SSMI) radiobrightness signatures for a three-month, dry-down simulation in the fall and winter of the northern United States Great Plains as part of an investigation of the effects of coupling heat and moisture transport. Given a typical initial moisture content of 38%, they find that coupled transport results in a reduction of ice in the surface soil by 21%. The range of diurnal variations in temperature are not significantly affected by coupled transport. Diurnal variations in the 19-GHz, H-polarized radiobrightness can be greater in the coupled transport case by 37 K. Total diurnal variation can exceed 57 K during periods of diurnal freezing and thawing.

The authors have developed physically based, diurnal, and annual models for freezing/thawing moist soils subject to annual insolation, radiant heating, and cooling, and sensible and latent heat exchanges with the atmosphere. Both models have the same weather forcing, numerical scheme, and soil constitutive properties. The authors find that surface temperature differences over a diurnal cycle between the annual and diurnal models are as much as -5 K in March, -7 K in June, -4 K in September, and 5 K in December for 38% (by volume fraction) moist soil. This difference occurs because the annual model includes the history of energy fluxes at the surface of the soil. The annual model is linked to microwave emission models for predictions of temporal radiobrightness signatures. The model predicts a relatively weak decrease in diurnal differences in soil temperature with increased moisture content, but a significant decrease in diurnal differences in radiobrightness. It also exhibits notable perturbations in radiobrightness when soils freeze and thaw. The moisture dependent, day-to-night radiobrightness difference is enhanced by as much as -42 K at 19.35 GHz horizontal polarization for frozen soil if daytime thawing occurs.