Moderate Resolution Imaging Spectroradiometer Sensor Research Articles

A simple method for surface radiation estimating using FY-4A data

AbstractFY-4A is a geostationary meteorological satellite with four advanced payloads, which can be used to quantitatively detect the earth's atmospheric system with multi spectral and high spatial-temporal resolution. However, the applicable model limits the application of the FY-4A satellite data. In this paper, the empirical statistical model developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is extended for FY-4A Advanced Geosynchronous Radiation Imager (AGRI), and it is applied to observed data to evaluate the applicability of the model for AGRI measurements. To improve the accuracy of radiation estimation, the artificial intelligent particle swarm optimization (PSO) algorithm was used for model optimizing. Results show that the estimated radiation has diurnal variation, which accords with the characteristics of radiation variation. The estimated net surface shortwave radiation (Sn) and observed values show good correlation. However, large deviations from observations are found in the estimated values when the empirical model based on MODIS is directly used to process AGRI data. Thus, the empirical statistical model based on MODIS can be applied to AGRI data, but the empirical parameters need to be revised. Optimization of the empirical statistical model by the PSO algorithm can effectively improve the accuracy of radiation estimate. The Mean absolute percentage error (MAPE) of Sn estimated by optimized models is reduced to 15%. The MAPE of the net surface long-wave radiation (Ln) estimated by optimized models is reduced to 31%, and the MAPE of the net radiation (Rn) estimated by optimized models is reduced to 27%. However, for the uncertainty caused by error accumulation effect, the influence of PSO optimization on Rn is not as obvious as that of Ln. However, from the analysis of error distribution, it shows that PSO optimization does improve the estimation results of Rn. Based on AGRI data, the surface radiation can be estimated simply, and the regional or larger scale surface radiation retrieval can quickly realize by this method which has large application potential and popularization value.

Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products

The science teams of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) have been supporting various global climate, biogeochemistry, and energy flux research efforts by producing valuable long-term Leaf Area Index (LAI) products. Although intensive LAI validation studies have been carried out globally, retrieval accuracy has been assessed only over short time spans and product quality is reported as a constant based on the assumption that it is stationary. However, a preliminary evaluation found a time-dependent signal degradation of the MODIS sensors which may cause the uncertainty of the Bidirectional Reflectance Factor (BRF) product to exceed the preset uncertainty buffer in the retrieval algorithm and result in MODIS LAI quality variation and time-series inconsistency with VIIRS LAI. Therefore, to ensure the reliability of trend-detection studies and to answer the critical question of whether the algorithm configuration is still suitable for the uncertainty level of present BRF product, there is a need for a more comprehensive investigation regarding the performance stability of LAI retrieval algorithm due to model uncertainty and input uncertainties. This paper reports analyses of inter-annual stability and trends of LAI uncertainties (inferred from product quality flag rather than ground-based validation) as well as LAI magnitudes using multi-year (MODIS: 2001–2019, VIIRS: 2013–2019) and multi-site (445 sites) datasets. We found that the quality metrics of the two products are consistent across different biome types and LAI values (R2 ranged from 0.88 to 0.99). In the 19-year MODIS period, we found a significant increasing trend in LAI magnitudes (4.5%/decade) which agrees with previous reports, while there was no significant trend in product quality. Moreover, quality metrics showed much smaller inter-annual variations than LAI values, which confirms the performance stability of retrieval algorithms and suggests that the configuration of MODIS algorithm is still compatible with the uncertainty of algorithm inputs. Additionally, this study provides support to the “Greening Asia” studies by eliminating the possibility that the “greening” could be attributed to artificial trends caused by sensor degradation. We also found very small inter-annual variations in atmospheric conditions (e.g., cloud and aerosols), which further assures that the performance stability of retrieval algorithm. Overall, our results demonstrate the robustness of the retrieval algorithm in the presence of changes in input uncertainties. The temporal stability of the algorithm performance indirectly strengthen the confidence in the continuous use of MODIS LAI products either independently or in combination with its successor - VIIRS LAI - to study global and regional vegetation dynamics

Drought Extent and Severity on Arable Lands in Romania Derived from Normalized Difference Drought Index (2001–2020)

The aim of this study was to evaluate the frequency and severity of drought over the arable lands of Romania using the Normalized Difference Drought Index (NDDI). This index was obtained from the Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensor of the Terra satellite. The interval between March and September was investigated to study the drought occurrence from the early stage of crop growth to its harvest time. The study covered a long period (2001–2020), hence it is able to provide a sound climatological image of crop vegetation conditions. Corine Land Cover 2018 (CLC) was used to extract the arable land surfaces. According to this index, the driest year was 2003 with 25.6% of arable land affected by drought. On the contrary, the wettest year was 2016, with only 10.8% of arable land affected by drought. Regarding the multiannual average of the period 2001–2020, it can be seen that drought is not a phenomenon that occurs consistently each year, therefore only 11.7% of arable land was affected constantly by severe and extreme drought. The correlation between NDDI and precipitation amount was also investigated. Although the correlations at weekly or monthly levels are more complicated, the annual regional mean NDDI is overall negatively correlated with annual rainfall. Thus, from a climatic perspective, we consider that NDDI is a reliable and valuable tool for the assessment of droughts over the arable lands in Romania.

On the cause-and-effect relations between aerosols, water vapor, and clouds over East Asia

Atmosphere is a complex dynamical system. Here, we investigated the causal links between aerosols, water vapor, and clouds, using the convergent cross mapping (CCM) method, which is based on nonlinear state space reconstruction. We utilized remote sensing data of aerosol optical depth at 550 nm (AOD), water vapor (WV), cloud cover (CC), cloud optical depth (COD), cloud effective radius-ice (CERI), and cloud effective radius-liquid (CERL) from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor over East Asia, for the period 2003–2018. Our analysis shows that there is a bidirectional forcing between AOD, CC, and COD which could be attributed to the invigoration effect of aerosols on clouds. In addition, there is a bidirectional forcing between AOD and WV and AOD and CERL, which could be attributed to the first indirect effect of aerosols on clouds, while there is no causality among AOD and CERI, probably because of strong coupling among aerosols and ice nuclei. Based on our analysis, we conclude that CCM method can effectively be used in all aerosol–cloud interactions’ studies, searching for causality among the parameters.

A reconstruction of the recent fire regimes of Majete Wildlife Reserve, Malawi, using remote sensing

BackgroundFire is an important process that shapes the structure and functioning of African savanna ecosystems, and managers of savanna protected areas use fire to achieve ecosystem goals. Developing appropriate fire management policies should be based on an understanding of the determinants, features, and effects of prevailing fire regimes, but this information is rarely available. In this study, we report on the use of remote sensing to develop a spatially explicit dataset on past fire regimes in Majete Wildlife Reserve, Malawi, between 2001 and 2019. Moderate Resolution Imaging Spectroradiometer (MODIS) images were used to evaluate the recent fire regime for two distinct vegetation types in Majete Wildlife Reserve, namely savanna and miombo. Additionally, a comparison was made between MODIS and Visible Infrared Imager Radiometer Suite (VIIRS) images by separately evaluating selected aspects of the fire regime between 2012 and 2019.ResultsMean fire return intervals were four and six years for miombo and savanna vegetation, respectively, but the distribution of fire return intervals was skewed, with a large proportion of the area burning annually or biennially, and a smaller proportion experiencing much longer fire return intervals. Variation in inter-annual rainfall also resulted in longer fire return intervals during cycles of below-average rainfall. Fires were concentrated in the hot-dry season despite a management intent to restrict burning to the cool-dry season. Mean fire intensities were generally low, but many individual fires had intensities of 14 to 18 times higher than the mean, especially in the hot-dry season. The VIIRS sensors detected many fires that were overlooked by the MODIS sensors, as images were collected at a finer scale.ConclusionsRemote sensing has provided a useful basis for reconstructing the recent fire regime of Majete Wildlife Reserve, and has highlighted a current mismatch between intended fire management goals and actual trends. Managers should re-evaluate fire policies based on our findings, setting clearly defined targets for the different vegetation types and introducing flexibility to accommodate natural variation in rainfall cycles. Local evidence of the links between fires and ecological outcomes will require further research to improve fire planning.

Terrain units, land use and land cover, and gross primary productivity of the largest fluvial basin in the Brazilian Amazonia/Cerrado ecotone: The Araguaia River basin

Integrity of most of tropical wetlands is threatened because they are often considered freely available resources of land and water. The Bananal, in the Araguaia River Basin, and the Pantanal in the Paraguai River Basin, are the two largest Brazilian wetlands influenced by the tropical seasonal floods. The purpose of this study is to integrate landscape structural and functional factors with the GPP dynamics over different terrain units present in the Araguaia River Basin. The study was conducted through the integration of different databases, including the topographic domains, protected areas and indigenous lands; land use and land cover map (year 2016); time series of GPP derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (period of 2000–2015); Shuttle Radar Topography Mission (SRTM) digital elevation models (DEM); and precipitation data produced by the WorldClim version 2 dataset. GPP time series were processed using statistical methods of time series decomposition. The results show that, in the alluvial domains, we find grasslands (mostly), alluvial forests with the highest levels of GPP, and the largest legally protected areas. Spatial distribution of pasturelands across the entire basin is broad while croplands are concentrated mainly in geologically old and high plateaus. The time series analysis revealed the presence of intra- and inter-annual vegetation patterns and a continuous increase of GPP over time. The results contribute to the understanding of the linkages between land use changes and global carbon cycles by considering land use as a response of how the landscape is organized. The results may provide subsidies for public policies for the carbon budget equilibrium, conservation of biodiversity, and rational use of natural resources.

A brief evaluation of the MOD17A2H product over a pasture in northeast Brazil

ABSTRACT This research aims to provide a brief evaluation of remotely sensed estimates in representing gross primary production (GPP) over pasture in the Northeast Region of Brazil. We used GPP data measured in situ during a micrometeorological experiment performed in a pasture area located in the NEB region using the eddy covariance technique. Remotely sensed GPP was obtained through the MOD17A2H product of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Terra satellite. We concluded that the MOD17A2H product moderately represents surface-measured GPP in the studied site, highlighting the need for improvements in the quality of the remotely sensed GPP estimates over this type of ecosystem.

Impact of COVID-19 on the Air Quality over China and India Using Long-term (2009-2020) Multi-satellite Data

We have examined the air quality over China, India and demonstrated marked differences in levels of air pollution resulted from the COVID-19 restrictions during December–April, 2019–20 to that of 11 years mean of 2009–19. The criteria air quality indicators i.e., nitrogen dioxide (NO2), sulphur dioxide (SO2), Aerosol Index (AI) and aerosol optical depth (AOD) data are retrieved from the Ozone Monitoring Instrument (OMI), TROPOspheric Monitoring Instrument (TROPOMI), and MODerate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra and Aqua satellites, respectively. Over China, during COVID-19 lockdown a significant drop in columnar abundances of tropospheric NO2 (–37%), SO2 (–64%) and AOD (–8%) for 2020 in comparison to 11 years mean (2009–19) has been observed. A noticeable difference in NO2 column burden is seen over SE (–35%), NE (–33%), NW (–13%) and SW (–5%) China. Over the SE and NE China, both NO2 and SO2 levels decreased dramatically in 2020 from that of 2009–19, by more than 40% and 65%, respectively, because of both stricter regulations of emissions and less traffic activity due to reduced social and industrial activities during COVID-19 restrictions. In contrast, the curve of monthly mean tropospheric columnar burden of NO2 and SO2 over India has shown moderate reduction of 16% and 20%, respectively because lockdown came into effect much later in March 2020. The mean NO2 and SO2 over IGP region is found to be 25% higher than whole India’s mean concentration due to large scale urban settlement and crop burning events. The statistical t-test analysis results confirm significant (p < 0.05) improvements in AQ during lockdown. The COVID-19 pandemic provided an unprecedented opportunity to investigate such large-scale reduction in emissions of trace gases and aerosols. Therefore, it is important to further strengthen environmental policies to tackle air quality, human health, and climate change in this part of the world.

Evaluating Drought Events by Time-Frequency Analysis: A Case Study in Aegean Region of Turkey

Drought is a slowly progressing and complex natural phenomenon, so the nature of drought events remains unclear. Remote sensing is preferred as an effective tool for finding, evaluating, and monitoring drought especially for large areas throughout long-term periods by providing near real-time and accurate data. Besides, Google Earth Engine (GEE) is a cloud-based service to provide analysis and visualization of geospatial datasets. The ability of GEE as a remote sensing platform to analyze high-impact societal issues, including water management diseases, disaster, deforestation, and climate monitoring environmental protection, offers it as the best option for drought monitoring. Here, Vegetation Health Index (VHI) index that combines Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) has been chosen for agricultural drought assessment in a case study (Aegean Region of Turkey) from 2000 to 2018 (19 years). Thence, the land surface temperature (LST) data and the Normalized Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been used and analyzed as remotely sensed data. The major data processing steps have been efficiently done on the GEE platform. Also, the time series and periodic behavior of these satellite-based indices have been examined. Besides, as the meteorological drought index, Standardized Precipitation Evapotranspiration Index (SPEI) time series were calculated for the region in question at different time scales (SPEI3, SPEI6, SPEI12, etc.) from 1980 to 2018 (39 years). Also, the frequency analysis of both satellite-based and meteorological station-based indices has been done. Fast Fourier Transform (FFT) for satellite-based indices frequency analyzing and Wavelet Transform (WT) for time-frequency analyzing of SPEI sequence have been used. Cross Wavelet (XWT), and Coherence Wavelet (CWT) were used to evaluate the time-frequency relationship of the satellite-based and the meteorological station-based time series.

Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem

We implement and test an analog-based post-processing method to improve short range forecasts of aerosol optical depth (AOD) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model postprocessing of AOD is performed using historical analog forecasts and a Kalman Filter (KF). Analog forecasts are selected from WRF-Chem simulations based on a set of environmental predictors (AOD, wind speed, precipitable water, and particulate matter) that exhibit past values similar to the current forecasts. Space-borne AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra and Aqua satellites corresponding to the analogs are used to build the analog ensemble. This study focuses on a spatial domain covering the AERONET sites in contiguous United States. We use the analog ensemble weighted mean (AN) and Kalman filter analog (KFAN) algorithms, which are both trained using WRF-Chem AOD forecasts for the months of June to August during 2008–2011 and tested during the same months for 2012. Overall, the AOD forecast are more skillful when the forecast errors are corrected using a combination of analogs and Kalman filter in KFAN. This is especially true for the western US where the correlation of AOD with PM2.5, PM10, and surface horizontal wind speed are higher than those for other predictors. In fact, the overall biases in AOD are significantly reduced close to zero, with KFAN AOD being statistically indistinguishable to MODIS. However, both methods show mixed results (albeit still showing overall improvements) in eastern and central U.S., where AOD and its variability are highest. We find that, during the summer, PM is not the only predominant factor driving AOD in these regions, unlike western United States (U.S.) (except New Mexico and Arizona). We note, however, that the quality of the analogs depends on the model's capability to accurately simulate total precipitable water, which in turn influences aerosol sources and sinks.

An assessment of the MOD17A2 gross primary production product in the Caatinga biome, Brazil

ABSTRACT Studying the dynamics of gross primary production (GPP) in seasonally dry tropical forests is of fundamental importance to understand the carbon dioxide (CO2) balance in this ecosystem, helping mitigate its potential impacts at the regional and global levels. Thus, the objective of this work was to evaluate the accuracy of GPP estimated via remote sensing in the Caatinga biome. A set of observed data retrieved from micrometeorological towers equipped with eddy covariance systems were used to validate remote sensing data. The set was measured in a preserved Caatinga fragment. Remotely sensed GPP data was retrieved from the MOD17A2 version 6.0 product of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The validation of MOD17A2 GPP estimates was carried out through the comparison with micrometeorological data measured in situ. In the Caatinga site the comparison between the two GPP data types showed a moderate correlation with Pearson’s correlation coefficient (r) = 0.65 and coefficient of determination (R 2) = 0.43 and the product performed better in representing GPP in the Caatinga during the dry season. Results showed that although the MOD17A2 product represents the annual behaviour of GPP, the algorithm could be improved in order to provide GPP information that is more similar to surface measured data over these land covers.

Urban Morphological Controls on Surface Thermal Dynamics: A Comparative Assessment of Major European Cities with a Focus on Athens, Greece

Variations in urban form lead to the development of distinctive intra-urban surface thermal patterns. Previous assessment of the relation between urban structure and satellite-based Land Surface Temperature (LST) has generally been limited to single-city cases. Here, examining 25 European cities (June–August 2017), we estimated the statistical association between surface parameters—the impervious fraction (λimp), the building fraction (λb), and the building height (H)—and the neighborhood scale (1000 × 1000 m) LST variations, as captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Correlation analysis, multiple linear regression, and spatial regression were used. As expected, λimp had a consistent positive influence on LSTs. In contrast, the relation of LST with λb and H was generally weaker or negative in the daytime, whereas at night it shifted to a robust positive effect. In particular, daytime LSTs of densely built, high-rise European districts tended to have lower values. This was especially the case for the city of Athens, Greece, where a more focused analysis was conducted, using further surface parameters and the Local Climate Zone (LCZ) scheme. For the urban core of the city, the canyon aspect ratio H/W had a statistically significant (p &lt;0.01) negative relationship with LST by day (Spearman’s rho = −0.68) and positive during nighttime (rho = 0.45). The prevailing intra-urban surface thermal variability in Athens was well reproduced by a 5-day numerical experiment using the meteorological Weather Research and Forecasting Model (WRF) model and a modified urban parameterization scheme. Although the simulation resulted in some systematic errors, the overall accuracy of the model was adequate, regarding the surface temperature (RMSE = 2.4 K) and the near-surface air temperature (RMSE = 1.7 K) estimations.

SPATIAL-TEMPORAL DISTRIBUTION OF DROUGHT IN THE WESTERN REGION OF PARAGUAY (2005–2017)

Abstract. The Western Region of Paraguay is naturally dry which makes it vulnerable to almost permanent drought events. Hence, low cost drought monitoring is necessary. Therefore, an index derived from satellite image information was used for this purpose. The Normalized Difference Drought Index (NDDI) was used with the objective of studying the characteristics (temporal and spatial distribution) of drought in the Western Region of Paraguay from years 2005 to 2017 and relate drought point values of NDDI to the land use cover of the study area. Terra satellite’s Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor images were used for the calculation of NDDI. The driest month of all years (July) and the wettest month of all years (December) were averaged to analyze spatial tendencies. The NDDI was contrasted spatially with the Land Use Cover of the Western Region to pinpoint the location of the highest values. The year 2015 had the highest value of 0.9847 in agricultural land use in the Department of Boqueron. The NDDI was a good indicator of drought throughout the region and could be a complement for in-situ measurements.

Estimation of the distribution of the total net radiative flux from satellite and automatic weather station data in the Upper Blue Nile basin, Ethiopia

Net radiation is an important factor in studies of land–atmosphere processes, water resource management, and global climate change. This is particularly true for the Upper Blue Nile (UBN) basin, where significant parts of the basin are dry and evapotranspiration (ET) is a major mechanism for water loss. However, net radiation has not yet been appropriately parameterized in the basin. In this study, we estimated the instantaneous distribution of the net radiation flux in the basin using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite and Automatic Weather Station (AWS) data. Downward shortwave radiation and air temperature usually vary with topography, so we applied residual kriging spatial interpolation techniques to convert AWS data for point locations into gridded surface data. Simulated net radiation outputs were validated through comparison with independent field measurements. Validation results show that our method successfully reproduced the downward shortwave, upward shortwave, and net radiation fluxes. Using AWS data and residual kriging spatial interpolation techniques makes our results robust and comparable to previous works that used satellite data at a finer spatial resolution than MODIS. The estimated net shortwave, longwave, and total radiation fluxes were in close agreement with ground truth measurements, with mean bias (MB) values of − 14.84, 5.7, and 20.53 W m−2 and root mean square error (RMSE) values 83.43, 32.54, and 78.07 W m−2, respectively. The method presented here has potential applications in research focused on energy balance, ET estimation, and weather prediction for regions with similar physiographic features to those of the Nile basin.

Spatial-temporal analysis of net primary production (NPP) and its relationship with climatic factors in Iran.

Fluctuations of the climate variables have increased in the recent years. These fluctuations are different in each climatic region. Net primary production (NPP) indicating the plant growth and carbon stabilization over period of time is influenced by these fluctuations. Investigation of the variations in the NPP and analysis of its relationship with the climatic and environmental variables can play a key role in determining the effects of fluctuations of climatic variables on the NPP. Therefore, the present study was conducted to investigate the spatiotemporal changes in the NPP and its correlation with precipitation rate and temperature during 2000-2014 based on the annual NPP estimates determined by the moderate resolution imaging spectroradiometer (MODIS) sensor and precipitation and temperature data of the synoptic stations in eight climate regions in Iran. The slope of variations in the NPP was calculated in these climatic regions, and then, the changes in the NPP trend at two confidence levels of 95 and 99% were investigated based on the pixel-based method using the Mann-Kendall test. The sensitivity of NPP to climatic variables of temperature and precipitation was also estimated by calculating the correlation. The results showed the significant spatial distribution of NPP in the whole region under study indicating a declining trend from north to south and from west to east directions. The results also indicated the nonlinear variations in the temporal distribution of NPP. The annual mean NPP was found to follow the climatic boundaries in the climatic regions except for climate region 2, and region with the higher annual mean precipitation had higher annual mean NPP. Analysis of the trend by the Mann-Kendall method revealed that 3.2% of the pixels in the whole region followed a certain trend. Among the pixels, 70% of them followed a negative trend and the remaining 30% followed a positive trend. The greatest number of pixels with a certain trend was found in the Gulf of Oman coast climate region so that 93% of the pixels had a positive trend. The lowest number of pixels with a certain trend was observed in eastern Alborz foothills so that 87% of the pixels showed a negative trend. Slope variations of the NPP in the whole region varied from - 35 to 46 gC m2 year-1. The eastern plateau had the highest negative slope variations among the climate regions, and the highest positive slope variation of 42% was observed in the highlands climate region. In general, the precipitation rate and temperature showed a mean partial coefficient of 0.22 and 0.02, respectively, and the correlation between the NPP and temperature and precipitation was different in each climatic region. The temperature was negatively correlated with the NPP in four climatic regions with higher annual mean temperatures and in other climatic regions; it had a weak positive correlation. Therefore, the sensitivity of NPP to precipitation and temperature was different in each climatic region.

Dust Impacts of Rapid Agricultural Expansion on the Great Plains

AbstractClimate change and land use are altering the landscape of the U.S. Great Plains, producing increases in windblown dust. These increases are investigated by combining coarse mode aerosol observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor in addition to the Aerosol Robotic Network (AERONET) and Interagency Monitoring of Protected Visual Environments (IMPROVE) aerosol monitoring networks. Increasing trends of up to 5%/year in MODIS aerosol optical depth for dust observations are observed throughout the Great Plains (2000–2018). Cropland coverage has increased 5–10% over the majority of the Great Plains (2008–2018), and positive monthly trends in IMPROVE (1988–2018) and AERONET (1995–2018) coarse mode 90th percentile observations coincide with planting and harvesting seasons of predominant crops. Presently, results suggest increased dust due to agricultural expansion is negatively influencing human health and visibility in the Great Plains. Furthermore, results foreshadow a future where desertification becomes an increasing risk in the Great Plains.

Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area

Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP-GWO) models. The most accurate estimations were produced by the MLP-GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.

SUMMER URBAN HEAT ISLAND OF GALAȚI CITY (ROMANIA) DETECTED USING SATELLITE PRODUCTS

The paper presents the main features of the urban heat island of Galați city during the summer season for a period of 16 years (2003-2018) using MODIS products. The main objectives were to analyse the extension and geometry of the urban heat island (UHI), its intensity, as well as its connection with land cover. The study is based on LST (Land Surface Temperature), a product obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors located on board of the Terra (EOS AM-1) satellite since 2001 and Aqua (EOS PM-1) satellite since 2003. The boundaries, geometry and spatial extent of the UHI were delimited primarily through the Rodionov test. Through this method, the changing points along four transects over Galați city were identified (North-South, East-West, Northeast-Southwest and Northwest-Southeast) and have been used to delimitate the UHI. Overall, the Galați UHI assessed by satellite information is stronger during the day when the UHI is split in two centres, developed over the industrial area in the west and over the residential part of the town in the east, while during the night the UHI is weaker being developed more clearly over the oldest part of the city. One of the major characteristics of the Galați UHI is that it is highly influenced by the water bodies encircling the city. During the day the intensity of UHI reaches 2-2.5°C while during the night decreases below 2.0°C. The relation of the UHI with land cover and the attenuation of UHI in the surrounding area are also investigated.

Validation and inter-comparison of the FY-3B/MERSI LAI product with GLOBMAP and MYD15A2H

ABSTRACT The leaf area index (LAI) describes the structure of vegetation and is a key variable for Earth system process modelling and eco-hydrological models at regional and global scales. The Medium-Resolution Spectral Imager (MERSI) onboard China’s new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) can provide continuous and global observations of the land surface. Therefore, it could be a potential data source for global LAI retrieval. In this study, a LAI product was generated from FY-3B/MERSI data using the GLOBCARBON LAI algorithm. Cross-calibration of the FY-3B/MERSI spectral response function with Land Remote-Sensing Satellite (Landsat) Thematic Mapper (TM), allowed to correct the influence of the spectral response function difference on the inversion results. Field measurements of LAI and scale-converted LAI reference images provided by the ImagineS project were used to validate and inter-compare the FY-3B/MERSI LAI product with two widely used medium-resolution LAI products, GLOBMAP LAI product and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product (MYD15A2 H). The results demonstrate that FY-3B/MERSI LAI has the lowest uncertainty of the three products. The low uncertainty of FY-3B/MERSI LAI in shrub-grass mixed areas (root mean square error, RMSE = 0.07), in part, by the generally underestimated LAI value. For deciduous broadleaf forest, of the three products tested, FY-3B/MERSI LAI is closest to the LAI obtained from the reference image (coefficient of determination, R 2 = 0.70) and yields the lowest uncertainty (RMSE = 0.81). GLOBMAP (R 2 = 0.58), which uses the same algorithm, and surface cover data as FY-3B/MERSI LAI, significantly overestimates the LAI. This overestimation may partly due to the use of a relatively lower clumping index. MYD15A2H shows a relatively weak correlation with the reference data (R 2 = 0.25) and a higher uncertainty (RMSE = 1.45). For mature crops, all three LAI products display systematic underestimation of LAI. FY-3B/MERSI LAI yields the greatest underestimation (about 50%), followed by GLOBMAP (about 35%) and MYD15A2H (about 15%). Our inter-comparison of the three LAI products demonstrates that all have higher correlation for low LAI values. FY-3B/MERSI shows similar capabilities and quality to those of the MODIS sensor with respect to the top of atmosphere observations. However, different atmospheric correction processes may cause large changes in the input Simple Ratio (SR), which in turn will cause bias when retrieving high LAI values. In addition, the temporal and spatial variation of clumping index was insufficiently considered in FY-3B/MERSI LAI product, further improvement is needed by using timely updated and high spatial resolution clumping index data.

Estimating wildfire fuel consumption with multitemporal airborne laser scanning data and demonstrating linkage with MODIS-derived fire radiative energy

Characterizing pre- and post-fire fuels remains a key challenge for estimating biomass consumption and carbon emissions from wildfires. Airborne laser scanning (ALS) data have demonstrated effectiveness for estimating canopy, and to a lesser degree, surface fuel components at fine-scale (i.e., 30 m) across landscapes. Using pre- and post-fire ALS data and corresponding field data, this study estimated consumption of canopy fuel (ΔCF), understory fuel (ΔUF), total fuel (ΔTF), and canopy bulk density (ΔCBD) for the 2012 Pole Creek fire in Oregon, USA (10,760 ha), and portions of the 2011 Las Conchas fire in New Mexico, USA (4,934 ha). Additionally, the feasibility of predicting fuel consumption was tested using separate pre- and post-fire models (PrePost), models combining all pre- and post-fire data (Pooled), and models using all data from both fires (Global). Estimates of ΔTF were then compared to fire radiative energy (FRE, units: MJ) derived from Fire Radiative Power (FRP, units: MW) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA Terra and Aqua satellites to mechanistically derive a biomass combustion coefficient (BCC, units: kg MJ−1). The PrePost and Pooled approaches yielded similar results at Las Conchas, but at Pole Creek insufficient pre-fire field data resulted in erroneous fuel consumption estimates outside the fire perimeter using the PrePost models. These results demonstrated that pre-fire field data were less important for these models than having field data which represent the full range of fuel conditions likely to exist across the landscape. Estimated total biomass consumed for the PrePost, Pooled, and Global models were 226 Gg, 224 Gg, and 224 Gg at Las Conchas, and 581 Gg, 713 Gg, and 552 Gg at Pole Creek. Comparisons between estimated ΔTF and FRE yielded an average BCC for both fires of 0.367 (s.d. ± 0.049) kg MJ−1 based on pixels with at least five MODIS observations. Both higher MODIS observations per pixel and accounting for canopy occlusion of FRE improved the relationship between ΔTF and MODIS-FRE. This study suggested a practical modelling approach for future efforts using only post-fire field observations and quantified a landscape-scale relationship between MODIS-derived FRE and fine-scale fuel consumption consistent with prior experiments.