Daily Moderate Resolution Imaging Spectroradiometer Research Articles

Effects of satellite temporal resolutions on the remote derivation of trends in phytoplankton blooms in inland waters

Satellite temporal resolutions are vital for the timely detection of phytoplankton blooms in inland waters using satellite observations due to rapid phytoplankton migration and reproduction. However, the effects of satellite temporal resolutions on the remote derivation of trends in phytoplankton have not yet been assessed. To address this issue, we first used the daily Moderate–resolution Imaging Spectroradiometer (MODIS)–derived phytoplankton blooms in a typical bloom–frequent lake (Lake Taihu, China) between 2001 and 2020 to simulate a series of phytoplankton bloom data sets with temporal resolutions of 2 days to 30 days. We then revealed the differences in the trends of the phytoplankton blooms and their responses to the climatic factors derived from these data sets; we subsequently quantified the relationships between the differences and the temporal resolutions at daily, monthly, and yearly scales. We found that there were significant inconsistencies in the trends in the phytoplankton bloom areas derived from the simulated series data sets with different temporal resolutions; the relations of the MODIS–derived phytoplankton bloom areas to those derived from the simulated series data sets became weaker as the temporal resolutions of the simulated series data sets decreased; the responses of the phytoplankton blooms to the climatic factors quantified by the simulated series data sets were also varied with the temporal resolutions at daily, monthly, and yearly scales. These results from simulated data were also validated by satellite data of Lake Taihu, Lake Chaohu, and Lake Dianchi, three eutrophic lakes in China. In brief, our results cautioned against the use of satellite data with low temporal resolutions to reveal long–term phytoplankton–bloom dynamics.

Assessment of MODIS Surface Temperature Products of Greenland Ice Sheet Using In-Situ Measurements

Satellite-based data have promoted the research progress in polar regions under global climate change, meanwhile the uncertainties and limitations of satellite-derived surface temperatures are widely discussed over Greenland. This study validated the accuracy of ice surface temperature (IST) from the moderate-resolution imaging spectroradiometer (MODIS) over the Greenland ice sheet (GrIS). Daily MODIS IST was validated against the observational surface temperature from 24 automatic weather stations (AWSs) using the mean bias (MB), the root mean square (RMSE), and the correlation coefficient (R). The temporal and spatial variability over the GrIS spanning from March 2000 to December 2019 and the IST melt threshold (−1 °C) were analyzed. Generally, the MODIS IST was underestimated by an average of −2.68 °C compared to AWSs, with cold bias mainly occurring in winter. Spatially, the R and RMSE performed the better accuracy of MODIS IST on the northwest, northeast, and central part of the GrIS. Furthermore, the mean IST is mainly concentrated between −20 °C and −10 °C in summer while between −50 °C and −30 °C in winter. The largest positive IST anomalies (exceeds 3 °C) occurred in southwestern GrIS during 2010. IST shows the positive trends mainly in spring and summer and negative in autumn and winter.

Assessment of snow cover variability and its sensitivity to hydrometeorological factors in the Karakoram and Himalayan region

ABSTRACT Snow cover plays an important role in hydrological modelling and influences the energy balance. Daily Moderate Resolution Imaging Spectroradiometer (MODIS) snow products are widely used for snow cover monitoring; however, the cloud cover causes a discontinuity in spatial and temporal scale. Therefore, we have implemented a composite (five-step) methodology for cloud removal over the Karakoram and Himalayan (KH) region during 2000–2019. The cloud-gap-filled snow cover area (SCA) was compared with Landsat-8 data, and the relationship with in situ observations (snowfall and temperature) was examined. The annual SCA illustrates an increasing trend for the entire KH except in the eastern Himalayas. The snow cover day and nine snow cover timing indices were assessed to explore the snow cover characteristics. The relationships between SCA and meteorological variables were evaluated, suggesting a higher correlation with temperature and shortwave radiation. A sensitivity analysis was performed, indicating higher sensitivity of SCA to radiation than to other selected variables.

Evaluating a spatiotemporal shape-matching model for the generation of synthetic high spatiotemporal resolution time series of multiple satellite data

Time series of high spatiotemporal resolution satellite data are required to monitor land surface biophysical properties and their seasonal and inter-annual dynamics at sub-field to field scales. To generate such time series, various algorithms have been developed to fuse infrequent cloud-free Landsat observations with daily Moderate Resolution Imaging Spectroradiometer (MODIS) observations. An early and widely used Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was developed based on the assumptions that observations from the two sensors are consistent in terms of spatial aggregation and temporal variation. These assumptions are not always valid in the real world, especially in a complex heterogeneous region. In this study, we investigated a Spatiotemporal Shape-Matching Model (SSMM) to generate synthetic time series of high spatiotemporal resolution satellite data. The SSMM, which is conceptually different from the image pair-based STARFM and STARFM-like approaches, makes full use of all spatiotemporally matched fine and coarse resolution data in an entire time series to establish a temporally uniformed fusion model for a given fine resolution pixel. The SSMM assumes that the temporal shapes of both the fine and coarse resolution time series are similar, but their magnitudes and phenological phases could differ largely even for the same vegetation type. This study assessed the capability of the SSMM to generate high spatiotemporal resolution time series of two-band Enhanced Vegetation Index (EVI2). Specifically, we generated the synthetic 30 m time series using the SSMM and STARFM algorithms in the northeastern United States based on 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) data and 30 m Landsat 8 and Sentinel-2 observations. We then evaluated the SSMM-derived 30 m time series across 15 land cover types and various degrees of heterogeneity. The result indicates that the SSMM is able to effectively generate synthetic time series in all different land cover types, which has advantages over the STARFM approach. Although the SSMM performance is degraded in heterogeneous regions, it can explain 82–91% of variations in 30 m EVI2 time series and produce constant root mean square error (0.053–0.056) across various levels of heterogeneity. Moreover, the SSMM can explain 87%-93% and 69%-87% of variation in 30 m EVI2 time series, respectively, with the models established using more than 10 pairs and 4–10 pairs of fine and coarse resolution observations. This suggests that the SSMM is also capable of generating high spatiotemporal resolution time series using historical Landsat time series with limited cloud-free observations, which are critical for studying vegetation dynamics and monitoring crop conditions.

Daily sea ice concentration product based on brightness temperature data of FY-3D MWRI in the Arctic

ABSTRACT A daily sea ice concentration (SIC) product in the Arctic, derived from the brightness temperature (TB) data of the Microwave Radiation Imager (MWRI) sensor aboard on the FY-3D satellite, is described in this paper. The MWRI TB raw swath data were first processed into daily gridded data and then corrected using the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor. An ASI algorithm, which uses daily dynamic tie points, was adopted to calculate daily SIC at 12.5 km polar stereographic projection from January 2018 to June 2020. Our generated MWRI SIC product was compared with the AMSR2 SIC based on the ASI algorithm that uses fixed tie points. For more detailed comparison, we then compared our MWRI SIC with the SIC from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. The mean bias between our MWRI SIC and AMSR2 SIC is 4.24%. The absolute values of biases between the daily MWRI SIC and MODIS SIC range from 0.14% to 10.76%, better than the MWRI SIC product based on the NT2 algorithm published by the Chinese National Satellite Meteorological Center. The results show that our MWRI SIC product has a good quality and can be used as a basic dataset for sea ice extent records. The dataset is available at http://www.dx.doi.org/10.11922/sciencedb.00137.

Generating high spatial and temporal soil moisture data by disaggregation of SMAP product and its assessment in different land covers

ABSTRACT Surface soil moisture (SSM) is an important parameter for many applications. Soil Moisture Active Passive (SMAP) satellite mission provides an SSM map at global scale. But its spatial resolution (36 km) is a big restriction for agricultural and hydrological studies at the catchment scale. Therefore, the present study was conducted to disaggregate the passive SMAP soil moisture data using the retrieved Soil Evaporative Efficiency (SEE) at 1-km spatial and daily temporal resolution from Moderate Resolution Imaging Spectroradiometer (MODIS) data and to assess the effectiveness of the method for generating data for different land covers. For this purpose, SMAP data were disaggregated using the SEE retrieved from daily MODIS data located at the southwest part of the United States. The accuracy of spatial and temporal variability of the disaggregated SMAP data was evaluated against the recorded in-situ soil moisture data in 202 stations of the Soil Climate Analysis Network (SCAN) for a period of 1 year. Results indicate that the disaggregated SMAP data have a moderate correlation with in-situ soil moisture data, but it is strongly affected by land cover. The highest accuracy was observed in the pasture/hay land cover class with Correlation Coefficient (R) value of 0.683 and 0.632, Mean Difference (MD) of −0.004 and −0.001, Root-Mean Square Error (RMSE) of 0.049 and 0.056, and unbiased Root-Mean Square Error (ubRMSE) of 0.039 and 0.045 for the disaggregated and original SSM data with the unit of , respectively. The lowest accuracy was found in the barren land (rock/sand/clay) for the disaggregated and original SSM data with R of 0.0278 and 0.155, MD of −0:081 and −0.052, RMSE of 0.134 and 0.116, and ubRMSE of 0.106 and 0.103, respectively. Results indicate that in overall disaggregation of SMAP data using Disaggregation based on Physical And Theoretical scale Change (DisPATCh) algorithm and MODIS products has a good potential for generating high spatial and temporal resolution of SSM at the catchment scale. But it is strongly affected by the land cover class type, because the calculation of the SEE is based on the Normalized Difference Vegetation Index (NDVI). Therefore, it can be recommended to retrieve the SEE with the attention to land cover class type and employ the other vegetation indices or methods.

Variations in Winter Surface Temperature of the Purog Kangri Ice Field, Qinghai–Tibetan Plateau, 2001–2018, Using MODIS Data

In the context of global warming, the land surface temperature (LST) from remote sensing data is one of the most useful indicators to directly quantify the degree of climate warming in high-altitude mountainous areas where meteorological observations are sparse. Using the daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11A1 V6) after eliminating pixels that might be contaminated by clouds, this paper analyzes temporal and spatial variations in the mean LST on the Purog Kangri ice field, Qinghai–Tibetan Plateau, in winter from 2001 to 2018. There was a large increasing trend in LST (0.116 ± 0.05 °C·a−1) on the Purog Kangri ice field during December, while there was no evident LST rising trend in January and February. In December, both the significantly decreased albedo (−0.002 a−1, based on the MOD10A1 V6 albedo product) on the ice field surface and the significantly increased number of clear days (0.322 d·a−1) may be the main reason for the significant warming trend in the ice field. In addition, although the two highest LST of December were observed in 2017 and 2018, a longer data set is needed to determine whether this is an anomaly or a hint of a warmer phase of the Purog Kangri ice field in December.

Use of MODIS NDVI Products to Map Tree Mortality Levels in Forests Affected by Mountain Pine Beetle Outbreaks

Extensive bark beetle outbreaks have recently occurred in western North American forests, resulting in overstory tree mortality across millions of hectares. Annual aerial surveys are currently used to operationally monitor bark beetle induced tree mortality, though this method is subjective and can exclude some forest areas. Daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data offer a potential alternative means to develop regional tree mortality maps. Accurate methods using such data could aid natural resource managers in surveys of forests with frequent overstory mortality, helping to prioritize forest treatment and restoration activities. This paper discusses a study to test the potential of using MODIS data to detect tree mortality. We developed and tested an approach to use 250-m resolution MODIS Normalized Difference Vegetation Index (NDVI) data products collected during a mountain pine beetle (MPB) outbreak and related tree mortality event in the northern Rocky Mountains of Colorado, USA. The 94 km2 study area is predominantly lodgepole pine forest with most of the MPB-caused mortality occurring between 2003 and 2008. We used a 2.4-m forest conditions map from 2008 aerial multispectral imagery to calculate percentage of mortality within 240-m pixels for use as reference data. Using either daily or 16-day products, MODIS NDVI change products were calculated for 2008 versus either 2000 or 2003 baselines. MODIS change products were used as predictors in linear regression analysis to assess correlation between MODIS data and the aerial percent forest mortality map. Depending on the MODIS product, linear regression analyses yielded r2 values ranging from 0.362 to 0.544 without outliers removed and from 0.406 to 0.570 with extreme outliers removed. Daily MODIS NDVI products from 2003 and 2008 were used with exponential regression to improve the r2 to 0.593. The project showed some MODIS NDVI data potential for mapping percent tree mortality in forests subjected to regional bark beetle outbreaks and severe drought.

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

The summer upwelling of the Eastern Aegean Sea detected from MODIS SST scenes from 2003 to 2015

ABSTRACTThe summer upwelling of the Eastern Aegean Sea is investigated by daily night-time MODerate resolution Imaging Spectroradiometer Sea Surface Temperature scenes in the periods from June to November (extended summer) 2003–2015. The analysis is supported by wind data downloaded from the European Centre for Medium-Range Weather Forecasts Re-Analysis archive. On average over these summers, upwelling and Etesian wind occurred 69% and 78% of time, respectively. The highest correlation between upwelling and wind was found from June to September, with the beginning of the upwelling events shifted between 24 and 48 h after the start of the Etesian wind. The upwelling always begins in the same areas of the Eastern Aegean coast with the only differences that these areas tesian wind pattern. The largest upwelling events occurred in summers 2003–2009 and not in the following years of the study period. The upwelled cold surface water, still pushed by the persistent Etesian wind, fills part of South-Eastern Aegean and eventually outflows the Aegean Sea through Kassos and Karpathos Straits of the eastern Cretan Arc.

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

Analysis of spatio-temporal dust aerosol frequency over Iran based on satellite data

Iran is located in the dust-belt region and so dust is a main environmental issue over the country. The main purpose of this study is to better understand of the spatial and temporal properties of the Dust Aerosol Frequency (DAF) over Iran using satellite data. Daily Moderate Resolution Imaging Spectroradiometer (MODIS) deep blue Aerosol Optical Depth (AOD), Angstrom Exponent (AE) (both Terra and Aqua, Level 3, Collection 6.1) and Ozone Monitoring Instrument (OMI) Absorption Aerosol Index (AAI) were obtained at 1° × 1° spatial resolution over Iran for the 10 years from 2006 to 2015. In this study observations with AOD higher than 0.3, AE < 0.75 and AAI >0.7 are considered to be dust aerosols. Results showed that the maximum areas with high DAF are located in the Khuzestan province in the southwest and Sistan in the east of the country. The other areas with peak DAF were observed in the low elevation areas of the country, namely coastal plain of the Oman Sea, Jaz Murian depression and Lut desert in the southeast, and Southern Dasht-e Kavir in central northern. DAF is decreased significantly at latitudes higher than 34.5° N. The maximum and minimum DAFs over Iran occur in July (Terra = 2.02, Aqua = 2.1) and November (Terra = 0.1, Aqua = 0.05), respectively. We found good spatial relationship between DAF with the meteorological codes frequency related to the dust from weather stations as well as with the fraction of dust AOD and from Monitoring Atmospheric Composition and Climate (MACC) project. In addition, results showed that dust frequencies based on both weather station and satellite data exhibited corresponding relationships with latitude, elevation, vegetation cover, precipitation and temperature.

US particulate matter air quality improves except in wildfire-prone areas

Using data from rural monitoring sites across the contiguous United States, we evaluated fine particulate matter (PM2.5) trends for 1988-2016. We calculate trends in the policy-relevant 98th quantile of PM2.5 using Quantile Regression. We use Kriging and Gaussian Geostatistical Simulations to interpolate trends between observed data points. Overall, we found positive trends in 98th quantile PM2.5 at sites within the Northwest United States (average 0.21 ± 0.12 µg·m-3·y-1; ±95% confidence interval). This was in contrast with sites throughout the rest of country, which showed a negative trend in 98th quantile PM2.5, likely due to reductions in anthropogenic emissions (average -0.66 ± 0.10 µg·m-3·y-1). The positive trend in 98th quantile PM2.5 is due to wildfire activity and was supported by positive trends in total carbon and no trend in sulfate across the Northwest. We also evaluated daily moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) for 2002-2017 throughout the United States to compare with ground-based trends. For both Interagency Monitoring of Protected Visual Environments (IMPROVE) PM2.5 and MODIS AOD datasets, we found positive 98th quantile trends in the Northwest (1.77 ± 0.68% and 2.12 ± 0.81% per year, respectively) through 2016. The trend in Northwest AOD is even greater if data for the high-fire year of 2017 are included. These results indicate a decrease in PM2.5 over most of the country but a positive trend in the 98th quantile PM2.5 across the Northwest due to wildfires.

Filling the missing data gaps of daily MODIS AOD using spatiotemporal interpolation

Aerosol is an important component of the atmosphere that affects the environment, climate, and human health. Remote sensing is an efficient observation method for monitoring global aerosol distribution and changes over time. The daily Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 aerosol optical depth (AOD) (Collection 6) product (10 km resolution) is often used to study climate change and air pollution. However, the product is prone to yielding large amounts of data gaps due to the unfeasibility of retrieving reliable estimates under cloudy conditions, and these data gaps inevitably affect the results and analysis of the product's application. In this study, a geostatistical data interpolation framework based on the spatiotemporal kriging method was implemented to interpolate satellite AOD products in Beijing, China. Compared to the ordinary kriging method for filling data gaps, the spatiotemporal interpolation not only utilizes spatial autocorrelation but also considers the temporal and spatiotemporal autocorrelations between different locations. In the study region, the completeness of the spatiotemporal-interpolated AOD product reaches 67.73%, which is significantly superior to the completeness of the original MODIS product (14.27%) and that of the spatial kriging-interpolated AOD product (33.3%). The cross-validation results show that the mean absolute error of the spatiotemporal kriging results (0.07) is lower than that of the ordinary kriging (0.09).

A Multi-Scale Validation Strategy for Albedo Products over Rugged Terrain and Preliminary Application in Heihe River Basin, China

The issue for the validation of land surface remote sensing albedo products over rugged terrain is the scale effects between the reference albedo measurements and coarse scale albedo products, which is caused by the complex topography. This paper illustrates a multi-scale validation strategy specified for coarse scale albedo validation over rugged terrain. A Mountain-Radiation-Transfer-based (MRT-based) albedo upscaling model was proposed in the process of multi-scale validation strategy for aggregating fine scale albedo to coarse scale. The simulated data of both the reference coarse scale albedo and fine scale albedo were used to assess the performance and uncertainties of the MRT-based albedo upscaling model. The results showed that the MRT-based model could reflect the albedo scale effects over rugged terrain and provided a robust solution for albedo upscaling from fine scale to coarse scale with different mean slopes and different solar zenith angles. The upscaled coarse scale albedos had the great agreements with the simulated coarse scale albedo with a Root-Mean-Square-Error (RMSE) of 0.0029 and 0.0017 for black sky albedo (BSA) and white sky albedo (WSA), respectively. Then the MRT-based model was preliminarily applied for the assessment of daily MODerate Resolution Imaging Spectroradiometer (MODIS) Albedo Collection V006 products (MCD43A3 C6) over rugged terrain. Results showed that the MRT-based model was effective and suitable for conducting the validation of MODIS albedo products over rugged terrain. In this research area, it was shown that the MCD43A3 C6 products with full inversion algorithm, were generally in agreement with the aggregated coarse scale reference albedos over rugged terrain in the Heihe River Basin, with the BSA RMSE of 0.0305 and WSA RMSE of 0.0321, respectively, which were slightly higher than those over flat terrain.

Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain

Water-limited ecosystems encompass approximately 40% of terrestrial land mass and play a critical role in modulating Earth’s climate and provisioning ecosystem services to humanity. Spaceborne remote sensing is a critical tool for characterizing ecohydrologic patterns and advancing the understanding of the interactions between atmospheric forcings and ecohydrologic responses. Fine to medium scale spatial and temporal resolutions are needed to capture the spatial heterogeneity and the temporally intermittent response of these ecosystems to environmental forcings. Techniques combining complementary remote sensing datasets have been developed, but the heterogeneous nature of these regions present significant challenges. Here we investigate the capacity of one such approach, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, to map Normalized Difference Vegetation Index (NDVI) at 30 m spatial resolution and at a daily temporal resolution in an experimental watershed in southwest Idaho, USA. The Dry Creek Experimental Watershed captures an ecotone from a sagebrush steppe ecosystem to evergreen needle-leaf forests along an approximately 1000 m elevation gradient. We used STARFM to fuse NDVI retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat during the course of a growing season (April to September). Specifically we input to STARFM a pair of Landsat NDVI retrievals bracketing a sequence of daily MODIS NDVI retrievals to yield daily estimates of NDVI at resolutions of 30 m. In a suite of data denial experiments we compared these STARFM predictions against corresponding Landsat NDVI retrievals and characterized errors in predicted NDVI. We investigated how errors vary as a function of vegetation functional type and topographic aspect. We find that errors in predicting NDVI were highest during green-up and senescence and lowest during the middle of the growing season. Absolute errors were generally greatest in tree-covered portions of the watershed and lowest in locations characterized by grasses/bare ground. On average, relative errors in predicted average NDVI were greatest in grass/bare ground regions, on south-facing aspects, and at the height of the growing season. We present several ramifications revealed in this study for the use of multi-sensor remote sensing data for the study of spatiotemporal ecohydrologic patterns in dryland ecosystems.

Evapotranspiration Mapping in a Heterogeneous Landscape Using Remote Sensing and Global Weather Datasets: Application to the Mara Basin, East Africa

Actual evapotranspiration (ET) is a major water use flux in a basin water balance with crucial significance for water resources management and planning. Mapping ET with good accuracy has been the subject of ongoing research. Such mapping is even more challenging in heterogeneous and data-scarce regions. The main objective of our research is to estimate ET using daily Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and Global Land Data Assimilation System (GLDAS) weather datasets based on the operational simplified surface energy balance (SSEBop) algorithm at a 1-km spatial scale and 8-day temporal resolution for the Mara Basin (Kenya/Tanzania). Unlike previous studies where the SSEBop algorithm was used, we use a seasonally-varying calibration coefficient for determining the “cold” reference temperature. Our results show that ET is highly variable, with a high inter-quartile range for wetlands and evergreen forest (24% to 29% of the median) and even up to 52% of the median for herbaceous land cover and rainfed agriculture. The basin average ET accounts for about 66% of the rainfall with minimal inter-annual variability. The basin scale validation using nine-years of monthly, gridded global flux tower-based ET (GFET) data reveals that our ET is able to explain 64% of the variance in GFET while the MOD16-NB (Nile Basin) explains 72%. We also observe a percent of bias (PBIAS) of 1.1% and 2.8%, respectively for SSEBop ET and MOD16-NB, indicating a good reliability in the ET estimates. Additionally, the SSEBop ET explains about 52% of the observed variability in the Normalized Difference Vegetation Index (NDVI) for a 16-day temporal resolution and 81% for the annual resolution, pointing to an increased reliability for longer aggregation periods. The annual SSEBop ET estimates are also consistent with the underlying primary (i.e., water and energy) and secondary (i.e., soil, topography, geology, land cover, etc.) controlling factors across the basin. This paper demonstrated how to effectively estimate and evaluate spatially-distributed and temporally-varying ET in data-scarce regions that can be applied elsewhere in the world where observed hydro-meteorological variables are limited.

Variability in the correlation between satellite-derived liquid cloud droplet effective radius and aerosol index over the northern Pacific Ocean

The relationship between aerosol index (AI) and cloud effective radius (CER) was examined over five sea regions in the northern Pacific using daily Moderate Resolution Imaging Spectroradiometer data from 2002–2014. The results show that there tends to be a negative relationship between AI and CER at lower AI, becoming positive at higher AI, suggesting the Twomey effect held in low aerosol environment. Over the entire AI range, the correlation between AI and CER was significantly positive over the two marginal seas (the Bohai–Yellow Sea and the Sea of Japan), and it was significantly negative over the three open oceans (the North Pacific subtropical gyre, the western and eastern subarctic North Pacific) for all seasons, except that the correlations in summer were significantly positive over the two subarctic North Pacific regions. A series of statistical analyses showed that the AI–CER relationship (the regression slope) is significantly correlated with relative humidity (RH) and precipitable water vapour (PWV); on average, PWV accounts for 60% of the variance of the slope for the two marginal seas and higher for the three open oceans (79%). The slope can change from negative to positive at high PWV levels, suggesting that water vapour plays an important role in the variability of the slope. Aerosol hygroscopic growth and the growth of CER in humid condition may counteract the Twomey effect at high AI, particularly over the two marginal seas. The error high contribution of the low liquid cloud fraction (LCF) to AI–CER relationship may partially account for the positive slope over the two marginal seas, while the low LCF has a negligible impact on the three open oceans. Additionally, over the three open oceans, stable thermodynamic state may prevent the effect of aerosol on cloud at high AI.

An open-source method of constructing cloud-free composites of forest understory temperature using MODIS

ABSTRACTSurface air temperature (Tair) is a critical driver of ecosystem processes and phenological dynamics, and can be estimated in near-real time with satellite remote sensing. However, persistent cloud cover often creates large spatial and temporal gaps in our observation records. Previous studies have successfully mapped Tair; however, the challenges of mapping forest understory temperatures (Tust) are relatively unexplored. This study describes a methodology for constructing cloud-free composites of Tust at 250 m spatial resolution. We used generalized linear models to correlate daily average Tust with ground-surveyed forest structural characteristics and land surface temperature (LST) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Models were applied to all four daily MODIS overpasses and combined in to a single image to maximize cloud-free spatial coverage. Pixel temperatures within the remaining cloud gaps were estimated using a temporal averaging algorithm that incorporated a novel approach for factoring the relative cloudiness between days. Models predicted Tust to within 1.5°C (R2 ~ 0.87), with an overall final map accuracy having a mean absolute error of 2.2°C. Maps were produced for two growing seasons using in situ observation data from forested sites throughout the Rocky Mountains of Alberta, Canada. By avoiding complex physical models, our procedure is computationally efficient and capable of processing large volumes of data using open-source programming languages and desktop computers.

祁连山不同植被类型的物候变化及其对气候的响应

PDF HTML阅读 XML下载 导出引用 引用提醒 祁连山不同植被类型的物候变化及其对气候的响应 DOI: 10.5846/stxb201510092039 作者: 作者单位: 西北师范大学地理与环境科学学院,西北师范大学地理与环境科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金资助项目（41161017） Phenological variation in different vegetation types and their response to climate change in the Qilian Mountains, China, 1982-2014 Author: Affiliation: northwest normal university,northwest normal university Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于1982-2006年GIMMS NDVI和2000-2014年MODIS NDVI遥感数据，利用double logistic拟合方法提取了1982-2014年祁连山区不同植被的生长季始期、生长季末期和生长季长度3个重要的物候参数，分析了不同植被物候期的时间变化趋势、空间分异特征及对气候因子的响应。结果表明：（1）祁连山区不同植被的生长季始期和生长季末期随年际变化表现出波动提前或推迟，其中沼泽植被的变化波动最大；草甸植被、灌丛植被、阔叶林植被和栽培植被生长季长度出现延长趋势；（2）祁连山区植被生长季始期集中在5月初，其中阔叶林植被生长季开始最早，荒漠植被生长季开始最晚，植被生长季末期集中在9月，栽培植被生长季结束较早，荒漠植被、沼泽植被生长季结束较晚，植被生长季长度集中在110-140 d，其中阔叶林植被、针叶林植被生长季长度较长，而荒漠植被、高山植被生长季长度较短；（3）祁连山植被物候期变化趋势的空间分布表明植被生长季始期、生长季末期主要表现为提前不明显和推迟不明显，生长季长度主要表现为缩短不明显和延长不明显；（4）物候要素与气候要素相关性表明前期温度的积累有利于植被的开始生长，但当年3月的降水量对植被生长季始期同样有重要作用，不同植被生长季末期与8月、9月温度相关性较大，而与10月、11月降水的相关性较大。 Abstract:Vegetation phenology is used in the study of climate change because of stable distribution, and ease of observation and interpretation. In this study, a 15-day time series of averaged normalized difference vegetation indices (NDVI) derived from the daily Global Inventory Modeling and Mapping Studies (GIMMS) NDVI dataset and 16-day averaged NDVI values derived from the daily Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI dataset were used to analyze trends in vegetation phenology. Firstly, using the ENVI tool to cut remote sensing data, NDVI time series data for the study area from 1982 to 2014 were obtained. Secondly, using a Savitzky-Golay filter, noise contamination caused by random factors was reduced by producing a smooth NDVI curve. Thirdly, using double logistic fitting, three important phenological parameters, including the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) were extracted for different vegetation types. Temporal change trends, as well as its spatial distribution characteristics, of the nine major vegetation types in the Qilian Mountains were analyzed. These types included meadow, steppe, desert vegetation, shrub vegetation, alpine vegetation, coniferous forest, broad-leaved forest, cultivated vegetation, and swamp vegetation. In addition, the effects of climatic factors on phenology were analyzed by correlation analysis. The results showed that:(1) The annual variation of SOS and EOS of different vegetation types in the Qilian Mountains fluctuated in its advance or or delay; the maximum variation was observed in the swamp vegetation. The LOS of meadow, shrub, coniferous forest, and cultivated vegetation was longer, but the LOS of desert vegetation shortened. (2) The SOS of vegetation was primarily in May; the growing season of the broad-leaved forests began the earliest and desert vegetation began the latest. The EOS of vegetation occurred primarily in September; the growing season of cultivated vegetation ended earlier, whereas the growing season of the desert and swamp vegetation ended later. The LOS of vegetation was from 110 days to 140 days, among which the LOS of broad-leaved and coniferous forests was longer, whereas the LOS of desert and alpine vegetation was shorter. (3) The spatial distribution of variation trends for vegetation phenology indicated that SOS and LOS were advanced or delayed but not substantially, and the LOS was primarily shortened or prolonged, but not substantially. (4) The correlation of phenological metrics and climatic factors indicated that the accumulation of early stage temperature was beneficial to the growth of vegetation, but the amount of precipitation in March was also important to the SOS of vegetation. The EOS of different vegetation types was related with temperature in August and September, and correlated with precipitation in October and November, but the correlation was not significant. At different altitudes, the phenological parameters were different. In particular, the LOS appeared to shorten with increasing elevation, which is consistent with the spatial distribution of phenological parameters. These results explain the relationship between climate change and phenological phases. Climate change has an obvious effect on vegetation phenological parameters, and the phenological period can explicitly indicate the climate change. 参考文献 相似文献 引证文献