Retrievals of All-Weather Daily Air Temperature Using MODIS and AMSR-E Data

Satellite optical-infrared remote sensing from the Moderate Resolution Imaging Spectroradiometer (MODIS) provides effective air temperature (Ta) retrieval at a spatial resolution of 5 km. However, frequent cloud cover can result in substantial signal loss and remote sensing retrieval error in MODIS Ta. We presented a simple pixel-wise empirical regression method combining synergistic information from MODIS Ta and 37 GHz frequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for estimating surface level Ta under both clear and cloudy sky conditions in the United States for 2006. The instantaneous Ta retrievals showed favorable agreement with in situ air temperature records from 40 AmeriFlux tower sites; mean R2 correspondence was 86.5 and 82.7 percent, while root mean square errors (RMSE) for the Ta retrievals were 4.58 K and 4.99 K for clear and cloudy sky conditions, respectively. Daily mean Ta was estimated using the instantaneous Ta retrievals from day/night overpasses, and showed favorable agreement with local tower measurements (R2 = 0.88; RMSE = 3.48 K). The results of this study indicate that the combination of MODIS and AMSR-E sensor data can produce Ta retrievals with reasonable accuracy and relatively fine spatial resolution (~5 km) for clear and cloudy sky conditions

Monitoring daily evapotranspiration in Northeast Asia using MODIS and a regional Land Data Assimilation System

We applied an approach for daily estimation and monitoring of evapotranspiration (ET) over the Northeast Asia monsoon region using satellite remote sensing observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Frequent cloud cover results in a substantial loss of remote sensing information, limiting the capability of continuous ET monitoring for the monsoon region. Accordingly, we applied and evaluated a stand-alone MODIS ET algorithm for representative regional ecosystem types and an alternative algorithm to facilitate continuous regional ET estimates using surface meteorological inputs from the Korea Land Data Assimilation System (KLDAS) in addition to MODIS land products. The resulting ET calculations showed generally favorable agreement (root-mean-square error  \u3c 1.3 mm d−1) with respect to in situ measurements from eight regional flux tower sites. The estimated mean annual ET for 3 years (2006 to 2008) was approximately 362.0 ± 161.5 mm yr−1 over the Northeast Asia domain. In general, the MODIS and KLDAS-based ET (MODIS-KLDAS ET) results showed favorable performance when compared to tower observations, though the results were overestimated for a forest site by approximately 39.5% and underestimated for a cropland site in South Korea by 0.8%. The MODIS-KLDAS ET data were generally underestimated relative to the MODIS (MOD16) operational global terrestrial ET product for various biome types, excluding cropland; however, MODIS-KLDAS ET showed better agreement than MOD16 ET for forest and cropland sites in South Korea. Our results indicate that MODIS ET estimates are feasible but are limited by satellite optical-infrared remote sensing constraints over cloudy regions, whereas alternative ET estimates using continuous meteorological inputs from operational regional climate systems (e.g., KLDAS) provide accurate ET results and continuous monitoring capability under all-sky conditions

Satellite-based assessments on regional summer and winter conditions triggering massive livestock loss (Dzud) in Mongolia

Includes bibliographical references.Presented at the Building resilience of Mongolian rangelands: a trans-disciplinary research conference held on June 9-10, 2015 in Ulaanbaatar, Mongolia.Dzud is a term referring either to conditions when melting snow refreezes to form an icy layer covering the grass, or to unusually heavy snow falls in Eurasian arid and semi-arid regions. Under dzud condition, animals cannot obtain food under snow or ice layer, which sometimes results in a dzud disaster, i.e. massive livestock kills. It has been recognized that the dzud disaster is directly induced by the harsh winter conditions but often influenced by drought in the previous summer. In this study, a data-intensive reanalysis on regional determinants of dzud disaster was conducted for more than 300 soums (an administrative unit equivalent with county in US) in Mongolia. Various climatic, hydrological, and vegetation variables were developed from satellite remote sensing (RS) data, which includes daily mean air temperature, dew-point temperature, and evapotranspiration, monthly precipitation, and 16-day NDVI from 2003 to 2010. Annual livestock census data were collected for every soum in Mongolia. Each variable was standardized to z-score and utilized for stepwise multiple regression analysis to identify factors statistically significant for explaining soum-level livestock mortality. The regression models were successfully constructed for two-third of total soums. Considerable spatial variability in the determinants of livestock mortality were found across soums in Mongolia. As the primary determinants, summer NDVI and dryness equally explained 22% of the soum mortality, while 33% and 16% of the mortality were explained with winter temperature and precipitation, respectively. Spatial patterns were also identified with winter precipitation and temperature being primary determinants in mountain regions and northern cool and semi-arid regions, while summer NDVI and dryness were important in southern hot and arid regions. Our results indicate combined efforts of monitoring RS-based summer NDVI and dryness and forecasting winter temperature and precipitation can provide useful tools for dzud disaster early warning

Estimation of 10-Hour Fuel Moisture Content Using Meteorological Data: A Model Inter-Comparison Study

Forest fire modeling often requires estimates of fuel moisture status. Among the various fuel variables used for fire modeling studies, the 10-h fuel moisture content (10-h FMC) is a promising predictor since it can be automatically measured in real time at study sites, yielding more information for fire models. Here, the performance of 10-h FMC models based on three different approaches, including regression (MREG), machine learning algorithms (MML) with random forest and support vector machine, and a process-based model (MFSMM), were compared. In addition, whole-year models of each type were compared with their respective seasonal models to explore whether the development of separate seasonal models yielded better estimates. Meteorological conditions and 10-h FMC were measured each minute for 18 months in and near a forest site and used for constructing and examining the 10-h FMC models. In the assessments, MML showed the best performance (R2 = 0.77–0.82 and root mean squared error [RMSE] = 2.05–2.84%). The introduction of the correction coefficient into MREG improved its estimates (R2 improved from 0.56–0.58 to 0.68–0.70 and RMSE improved from 3.13–3.85% to 2.64–3.27%) by reducing the errors associated with high 10-h FMC values. MFSMM showed the worst performance (R2 = 0.41–0.43 and RMSE = 3.70–4.39%), which could possibly be attributed to the lack of radiation input from the study sites as well as the particular fuel moisture stick sensor that was used. Whole-year models and seasonal models showed almost equal performance because 10-h FMC varied in response to atmospheric moisture conditions rather than specific seasonal patterns. The adoption of a hybrid modeling approach that blends machine-learning and process-based approaches may yield better predictability and interpretability. This study provides additional evidence of the lagged response of 10-h FMC after rainfall, and suggests a new way of accounting for this response in a regression model. Our approach using comparisons among models can be utilized for other fire modeling studies, including those involving fire danger ratings

Drought Monitoring Based on Vegetation Type and Reanalysis Data in Korea

Droughts affect economic, social, and environmental aspects in regions such as the Korean Peninsula, where more than 70% of the area comprises forests; hence, their monitoring is imperative. Despite the many indices and methodologies developed for monitoring, diagnosing droughts using reanalysis data is challenging as the data are characterized by low resolution and simplified vegetation classification. This study utilized a recently released ERA5 reanalysis dataset and its vegetation type information to derive indices that represent meteorological drought. Furthermore, their accuracy in South Korea, based on observation data, was evaluated. The spatio-temporal variability of droughts was analyzed using various factor and correlation analysis methods considering different atmospheric variables and soil moisture. The validity of the method was verified by comparing the observed and obtained data. Soil moisture in the first and second soil layers was sensitive to droughts in low-vegetation areas, thus requiring relatively frequent monitoring of precipitation and evapotranspiration near the topsoil. High-vegetation areas were most affected in the third layers one month after the drought. Hence, forest drought monitoring should consider precipitation, evapotranspiration, and runoff one month in advance. The results obtained herein can be used for forest drought monitoring one month before its occurrence

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Leaf area index (LAI) provides valuable information necessary forsustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel 2 satellite images, they have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed model-estimated LAI was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ~ 0.97 and a difference of root-meansquare-error ~ 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea

Drought Monitoring Based on Vegetation Type and Reanalysis Data in Korea

Droughts affect economic, social, and environmental aspects in regions such as the Korean Peninsula, where more than 70% of the area comprises forests; hence, their monitoring is imperative. Despite the many indices and methodologies developed for monitoring, diagnosing droughts using reanalysis data is challenging as the data are characterized by low resolution and simplified vegetation classification. This study utilized a recently released ERA5 reanalysis dataset and its vegetation type information to derive indices that represent meteorological drought. Furthermore, their accuracy in South Korea, based on observation data, was evaluated. The spatio-temporal variability of droughts was analyzed using various factor and correlation analysis methods considering different atmospheric variables and soil moisture. The validity of the method was verified by comparing the observed and obtained data. Soil moisture in the first and second soil layers was sensitive to droughts in low-vegetation areas, thus requiring relatively frequent monitoring of precipitation and evapotranspiration near the topsoil. High-vegetation areas were most affected in the third layers one month after the drought. Hence, forest drought monitoring should consider precipitation, evapotranspiration, and runoff one month in advance. The results obtained herein can be used for forest drought monitoring one month before its occurrence

An Artificial Intelligence Approach to Predict Gross Primary Productivity in the Forests of South Korea Using Satellite Remote Sensing Data

Many process-based models for carbon flux predictions have faced a wide range of uncertainty issues. The complex interactions between the atmosphere and the forest ecosystems can lead to uncertainties in the model result. On the other hand, artificial intelligence (AI) techniques, which are novel methods to resolve complex and nonlinear problems, have shown a possibility for forest ecological applications. This study is the first step to present an objective comparison between multiple AI models for the daily forest gross primary productivity (GPP) prediction using satellite remote sensing data. We built the AI models such as support vector machine (SVM), random forest (RF), artificial neural network (ANN), and deep neural network (DNN) using in-situ observations from an eddy covariance (EC) flux tower and satellite remote sensing data such as albedo, aerosol, temperature, and vegetation index. We focused on the Gwangneung site from the Korea Regional Flux Network (KoFlux) in South Korea, 2006–2015. As a result, the DNN model outperformed the other three models through an intensive hyperparameter optimization, with the correlation coefficient (CC) of 0.93 and the mean absolute error (MAE) of 0.68 g m−2 d−1 in a 10-fold blind test. We showed that the DNN model also performed well under conditions of cold waves, heavy rain, and an autumnal heatwave. As future work, a comprehensive comparison with the result of process-based models will be necessary using a more extensive EC database from various forest ecosystems