Moderate Resolution Imaging Spectroradiometer Satellite Data Research Articles

Global patterns of vegetation carbon use efficiency and their climate drivers deduced from MODIS satellite data and process-based models

Carbon use efficiency (CUE), defined as the ratio of net primary production (NPP) to gross primary production (GPP), represents the capacity of plants in converting assimilated atmospheric carbon dioxide to ecosystem carbon storage. Process-based models are important tools for simulating NPP and GPP; yet the model performance in simulating vegetation CUE has not been fully explored. The goal of this paper is thus to investigate the spatial variations in CUE from different process-based carbon cycle models in comparison with that from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, and to analyze their linkage with climate factors. The global average CUE derived from the five process-based models is 0.45 ± 0.05 (range from 0.38 to 0.52), slightly lower than the value of 0.48 obtained from MODIS data. A strong latitudinal gradient of CUE, with greater CUE at high latitudes, is well agreed by these different datasets. However, there also exist considerable discrepancies in CUE estimations among those products, especially in temperate Northern Hemisphere. Furthermore, for both the satellite-based dataset and results from process-based models, vegetation CUE declines non-linearly with increase in temperature, but remains relatively stable with enhanced precipitation. Our results also indicate that the differences in global patterns of CUE estimated by different approaches could be primarily resulted from their systematic differences in autotrophic respiration (Ra) rather than in GPP. Understanding mechanisms behind spatio-temporal changes in Ra is therefore a critical step towards better quantifying global CUE.

Application of Landsat 8 for Monitoring Impacts of Wastewater Discharge on Coastal Water Quality

In this study, we examine the capabilities of the Landsat 8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS), and Aqua Moderate resolution Imaging Spectroradiometer (MODIS) for monitoring the environmental impact of the 2015 Hyperion Treatment Plant (HTP) wastewater diversion in Santa Monica Bay, California. From 21 September – 2 November 2015, the HTP discharged approximately 39×103 m3 h-1 of treated wastewater into Santa Monica Bay through their emergency 1-mile outfall pipe. Multi-sensor satellite remote sensing was employed to determine the biophysical impact of discharged wastewater in the shallow nearshore environment. Landsat 8 TIRS observed decreased sea surface temperatures (SST) associated with the surfacing wastewater plume. Chlorophyll-a (chl-a) concentrations derived from Landsat 8 OLI and Aqua MODIS satellite sensors were used to monitor the biological response to the addition of nutrient-rich wastewater. In situ chl-a and in situ remote sensing reflectance (Rrs) were measured before, during, and after the diversion event. These in situ data were paired with coincident OLI and MODIS satellite data to yield a more comprehensive view of the changing conditions in Santa Monica Bay due to the wastewater diversion. Two new local chl-a algorithms were empirically derived using in situ data for the OLI and MODIS sensors. These new local chl-a algorithms proved more accurate at measuring chl-a changes in Santa Monica Bay compared to the standard open ocean OC2 and OC3M algorithms, and the regional southern California CALFIT algorithm, as validated by in situ chl-a measurements. Additionally, the local OLI algorithm outperformed the local MODIS algorithm, especially in the nearshore region. A time series of chl-a, as detected by the local OLI chl-a algorithm, illustrated a very large increase in chl-a concentrations during the wastewater diversion, and a subsequent decrease in chl-a after the diversion. Our study demonstrates the capability of using Landsat 8 TIRS and OLI sensors for the monitoring of SST and surface chl-a concentrations at high spatial resolution in nearshore waters and highlights the value of these sensors for assessing the environmental effects of wastewater discharge in a coastal environment.

Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data

ABSTRACTForest plays a significant role in regulating the carbon budget and mitigating climate change in long term. However, lack of spatially explicit and accurate information on carbon exchange components from diverse forest ecosystem types in India limits carbon budgeting on a regional scale. Remote-sensing-driven ecosystem models are well-established tools for estimating gross primary productivity (GPP) over large areas but they are seldom found erroneous if implemented without proper calibration of biome-specific parameters. The present study evaluates the combined use of eddy covariance (EC) data and satellite-derived variables for estimating GPP over large areas. Four remote-sensing-driven models, (i) temperature–greenness (TG) model, (ii) greenness–radiation (GR) model, (iii) light use efficiency (LUE) model, and (iv) remote-sensing-based LUE (LUERS) model, were parameterized with EC measurements and compared with 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products for a moist Shorea robusta forest in northern part of India. EC observed 8-day average GPP varied from 5.38 to 12.42 g C m−2 day−1. Among the four tested models, TG model had the highest root mean square error (RMSE) of 1.28 g C m−2 day−1, while GR and LUERS models had moderate RMSE of 0.99 g C m−2 day−1 and 0.98 g C m−2 day−1, respectively. The closest GPP estimate was given by LUE model with RMSE of 0.93 g C m−2 day−1. The RMSE for all four models were four times lower than that of MODIS GPP. Lower maximum LUE (and uncertainty in the environmental scalar used in MODIS GPP algorithm could have contributed to higher RMSE. More accurate modelling of GPP can help in better understanding of forest ecological functions with the changing climate.

Accuracy comparison of remotely sensed evapotranspiration products and their associated water stress footprints under different land cover types in Korean peninsula

Robust spatial information of evapotranspiration from multiple land cover types is deemed critical for several applications in agriculture and water balance studies. Energy balance models, used in association with satellite observations, are beneficial to map spatial variability of evapotranspiration which is mainly governed by different vegetation practices and local environmental conditions. This study utilize the Surface Energy Balance System model to estimate actual evapotranspiration and water scarcity footprints under complex landscape of Korean peninsula using Moderate-Resolution Imaging Spectroradiometer satellite data for a complete hydrological year of 2012. The modeled evapotranspiration was compared with flux tower measurements obtained from a subhumid cropland and temperate forest sites for the accuracy assessment. This accuracy comparison at daily scale had good agreement yielding reasonable coefficient of determination (0.72, 0.51), bias (0.41 mm day−1, 1.01 mm day−1) and root mean squared error (0.92 mm day−1, 1.53 mm day−1) at two observation (cropland, forest) sites, respectively. Furthermore, the monthly aggregated evapotranspiration from Surface Energy Balance System showed promising results than those of obtained from Moderate-Resolution Imaging Spectroradiometer based readymade global evapotranspiration product, i.e., MOD16, when both products were compared with unclosed and closed flux tower measurements. However, the variations in monthly evapotranspiration obtained from both products were significantly controlled by several climate factors and vegetation characteristics. Water stress mapping at regional and monthly scale also revealed strong contrast between the products of two approaches. Highest mean water stress (0.74) was observed for land use areas associated with evergreen forest and under sparsely vegetation condition by using estimated evapotranspiration from Surface Energy Balance System while an extreme mean water stress value of 0.56 by using end product of MOD16 evapotranspiration was raised from cropland regions. Overall, this study revealed the performance and suitability of two distinctive remote sensing approaches for characterizing the footprints of water fluxes in the Korean peninsula and provides a baseline for the policy makers to setup the sustainable use of existing water resources in this and other similar regions.

ESTIMATING PM2.5 IN THE BEIJING-TIANJIN-HEBEI REGION USING MODIS AOD PRODUCTS FROM 2014 TO 2015

Fine particulate matter with a diameter less than 2.5 μm (PM2.5) has harmful impacts on regional climate, economic development and public health. The high PM2.5 concentrations in China’s urban areas are mainly caused by the combustion of coal and gasoline, industrial pollution and unknown/uncertain sources. The Beijing-Tianjin-Hebei (BTH) region with a land area of 218,000 km<sup>2</sup>, which contains 13 cities, is the biggest urbanized region in northern China. The huge population (110 million, 8% of the China’s population), local heavy industries and vehicle emissions have resulted in severe air pollution. Traditional models have used 10 km Moderate-resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) products and proved the statistical relationship between AOD and PM2.5. In 2014, the 3 km MODIS AOD product was released which made PM2.5 estimations with a higher resolution became possible. This study presents an estimation on PM2.5 distributions in the BTH region from September 2014 to August 2015 by combining the MODIS satellite data, ground measurements of PM2.5, meteorological parameters and social-economic factors based on the geographically weighted regression model. The results demonstrated that the 10 km AOD product provided results with a slightly higher accuracy although the 3 km AOD product could provide more information about the spatial variations of PM2.5 estimations. Additionally, compared with the global regression, the geographically weighed model was able to improve the estimation results.

ESTIMATING PM2.5 IN THE BEIJING-TIANJIN-HEBEI REGION USING MODIS AOD PRODUCTS FROM 2014 TO 2015

Abstract. Fine particulate matter with a diameter less than 2.5 μm (PM2.5) has harmful impacts on regional climate, economic development and public health. The high PM2.5 concentrations in China’s urban areas are mainly caused by the combustion of coal and gasoline, industrial pollution and unknown/uncertain sources. The Beijing-Tianjin-Hebei (BTH) region with a land area of 218,000 km2, which contains 13 cities, is the biggest urbanized region in northern China. The huge population (110 million, 8% of the China’s population), local heavy industries and vehicle emissions have resulted in severe air pollution. Traditional models have used 10 km Moderate-resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) products and proved the statistical relationship between AOD and PM2.5. In 2014, the 3 km MODIS AOD product was released which made PM2.5 estimations with a higher resolution became possible. This study presents an estimation on PM2.5 distributions in the BTH region from September 2014 to August 2015 by combining the MODIS satellite data, ground measurements of PM2.5, meteorological parameters and social-economic factors based on the geographically weighted regression model. The results demonstrated that the 10 km AOD product provided results with a slightly higher accuracy although the 3 km AOD product could provide more information about the spatial variations of PM2.5 estimations. Additionally, compared with the global regression, the geographically weighed model was able to improve the estimation results.

An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data

ABSTRACTQuantitative remote sensing retrieval algorithms help understanding the dynamic aspects of Digital Earth. However, the Big Data and complex models in Digital Earth pose grand challenges for computation infrastructures. In this article, taking the aerosol optical depth (AOD) retrieval as a study case, we exploit parallel computing methods for high efficient geophysical parameter retrieval. We present an efficient geocomputation workflow for the AOD calculation from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. According to their individual potential for parallelization, several procedures were adapted and implemented for a successful parallel execution on multi-core processors and Graphics Processing Units (GPUs). The benchmarks in this paper validate the high parallel performance of the retrieval workflow with speedups of up to 5.x on a multi-core processor with 8 threads and 43.x on a GPU. To specifically address the time-consuming model retrieval part, hybrid parallel patterns which combine the multi-core processor’s and the GPU’s compute power were implemented with static and dynamic workload distributions and evaluated on two systems with different CPU–GPU configurations. It is shown that only the dynamic hybrid implementation leads to a greatly enhanced overall exploitation of the heterogeneous hardware environment in varying circumstances.

Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products

With the objective of reducing the large uncertainties in the estimations of emissions from crop residue open burning, an improved method for establishing emission inventories of crop residue open burning at a high spatial resolution of 0.25°×0.25° and a temporal resolution of 1month was established based on the moderate resolution imaging spectroradiometer (MODIS) Thermal Anomalies/Fire Daily Level3 Global Product (MOD/MYD14A1). Agriculture mechanization ratios and regional crop-specific grain-to-straw ratios were introduced to improve the accuracy of related activity data. Locally observed emission factors were used to calculate the primary pollutant emissions. MODIS satellite data were modified by combining them with county-level agricultural statistical data, which reduced the influence of missing fire counts caused by their small size and cloud cover. The annual emissions of CO2, CO, CH4, nonmethane volatile organic compounds (NMVOCs), N2O, NOx, NH3, SO2, fine particles (PM2.5), organic carbon (OC), and black carbon (BC) were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43, 1.09, 0.34, and 0.06Tg, respectively, in 2012. Crop residue open burning emissions displayed typical seasonal and spatial variation. The highest emission regions were the Yellow-Huai River and Yangtse-Huai River areas, and the monthly emissions were highest in June (37%). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of within ±126% for N2O to a high of within ±169% for NH3.

Ice cloud backscatter study and comparison with CALIPSO and MODIS satellite data.

An invariant imbedding T-matrix (II-TM) method is used to calculate the single-scattering properties of 8-column aggregate ice crystals. The II-TM based backscatter values are compared with those calculated by the improved geometric-optics method (IGOM) to refine the backscattering properties of the ice cloud radiative model used in the MODIS Collection 6 cloud optical property product. The integrated attenuated backscatter-to-cloud optical depth (IAB-ICOD) relation is derived from simulations using a CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite) lidar simulator based on a Monte Carlo radiative transfer model. By comparing the simulation results and co-located CALIPSO and MODIS (Moderate Resolution Imaging Spectroradiometer) observations, the non-uniform zonal distribution of ice clouds over ocean is characterized in terms of a mixture of smooth and rough ice particles. The percentage of the smooth particles is approximately 6% and 9% for tropical and midlatitude ice clouds, respectively.

Spatio-temporal synoptic variability of aerosol optical depth and cloud properties over the Central North region of India through MODIS collection V satellite sensors

Aerosol optical depth (AOD) values at 550 nm derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensors have been analyzed in the present study. The retrieved MODIS satellite data have been used to investigate the temporal heterogeneity in columnar aerosol characteristics over the Central North region of India with a resolution of 1° × 1° grid in magnitude for the 10-year period of 2003–2012. Seasonal and inter-annual variations of AOD values are examined in light of regional synoptic climatic meteorology. On an annual basis, high annual average AOD values (>0.5) are found at Delhi (~0.75 ± 0.44), Kanpur (~0.69 ± 0.7) and Chandigarh (~0.55 ± 0.16), whereas low annual average AOD values (<0.5) are found at Raipur (~0.38 ± 0.046), Jaipur (~0.38 ± 0.096), Bhopal (~0.33 ± 0.087), Nainital (~0.19 ± 0.097) and Shimla (~0.19 ± 0.11). Highest annual mean increase in AOD (~75 %) has been found over Jaipur, whereas lowest over Chandigarh (~21 %) across Central North region of India during the study period of 2003–2012. We have investigated the relationship between Terra AOD values at 550 nm and other cloud parameters, namely cloud fraction, water vapor, cloud top temperature, cloud top pressure, cloud optical depth and cloud effective radius over this urban tropical zone of India so as to have a better understanding of aerosol–cloud interaction. A one-tailed distribution single paired Student’s t test has also been applied to the Terra AOD values and cloud parameters.

Spatio-Temporal Variations of Rainfall and SST Anomaly over Indonesia during ENSO Modoki Event in 2010

Remote sensing application is one of the best data to observing spatial and temporal situation on earth surface. Application of Tropical Rainfall Measuring Mission (TRMM) and MODIS (Moderate Resolution Imaging Spectro-radiometer) are use for spatial and temporal analysis of rainfall and sea surface temperature (SST) anomaly over Indonesia in 2010. Spatial and temporal rainfall and SST anomaly data is important, especially during El Niño–Southern Oscillation (ENSO) Modoki events, because has wide effect of social and economy in Indonesia. Monthly rainfall data measured by the TRMM 3B43 over the course of 13 years and Monthly SST collected by the MODIS was employed to analyze anomaly of rainfall and SST over Indonesia during ENSO Modoki Event in 2010. In spatio-temporal seen confirmed during ENSO Modoki in 2010 indicate an anomaly has occurred on rainfall and SST over Indonesia. The result shows that increasing of rainfall anomaly begin from April in Nusa Tenggara archipelago and eastern of Java and finish in November in those region. Meanwhile, analysis result of MODIS satellite data for SST anomaly is shown at the beginning of 2010, SST anomaly begin occurred in western of Indonesia and the biggest was happen in southern of Indonesia at August to September and finish at November. Spatio-temporal analysis TRMM dan MODIS data shows that increasing of SST anomaly could affect increasing of rainfall anomaly in those same regions excepted in January to March.

Evaluating the potential of MODIS satellite data to track temporal dynamics of autumn phenology in a temperate mixed forest

Autumn phenology plays a critical role in regulating growing season duration and can be estimated from satellite remote sensing. However, to date, little work has been undertaken to evaluate the performance of remotely sensed autumn phenology, mainly due to a lack of spatiotemporally compatible field observations. To address this limitation, we conducted intensive ground observations of leaf coloration and leaf fall from 610 deciduous trees at two 625×625m study areas within a mixed forest in northern Wisconsin, USA during 2010 and 2012. We derived landscape phenology (LP) indices by upscaling these plot-level observations to facilitate spatially compatible comparisons with coarse resolution satellite measures. The satellite-derived land surface phenology (LSP) was based on 250m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data from both standard 16-day composite (MOD13Q1) and daily Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products. The results revealed that LSP dormancy onset differed from the observed date of full leaf coloration by 5.25days on average (ranging from 0 to 12days). Furthermore, progression of the autumn season as determined from LSP and LP showed close agreement as increasing LP leaf coloration corresponded to declining NDVI and EVI values. In addition, the end of the leaf coloring phase was marked by a simultaneous stabilizing of both NDVI and EVI time series whereas the timing of the end of the growing season (leaf fall) closely corresponded to minimum NDVI values. These findings clearly support the use of satellite measurements to effectively monitor temporal dynamics of autumn phenology in a temperate mixed forest.

Identifying sources of dust based on CALIPSO, MODIS satellite data and backward trajectory model

The total suspended particulate matter, total dust and PM10 mass concentrations and visibility data were measured using large flow total suspended particle (inhalable particles) sampler (KC-1000), dust storm sampler (SC-1) and visibility meter in Lanzhou, China. Furthermore, the dust origins of the event occurred during 9–14 March 2013 were accurately identified in this study using HYSPLIT (Hybrid-Single Particle Lagrangian Integrated Trajectory) trajectory model and multiple satellite data, including AOD (Aerosol Optical Depth) data from MODIS (Moderate Resolution Imaging Spectroradiometer), and vertical profiles of atmospheric aerosol properties from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations). It is found that the total suspended particulate matter mass concentration larger than 8 000 µg m-3 on 9 March was the highest among seven dust days with the visibility lower than 500 m. The dust at low levels (500 and 1 000m AGL) mainly originated from the Hexi (River West) Corridor and Western and Central Inner Mongolia Plateau, which moved very slowly and circulated around the desert regions in Western and Central Inner Mongolia before arriving at Lanzhou. While the air masses at higher altitudes (2 000 and 3 000m AGL) were transported from the Taklamakan Desert and the Qaidam basin, and arrived at Lanzhou. Most interesting, the air masses from Badain Jaran and Tengger Deserts and their outer edges brought dust particles on the transport pathways into atmosphere led to increase of particle pollutant concentrations due to tightly adherent ground movement of air masses.

Retrieval of the seawater reflectance for suspended solids monitoring in the East China Sea using MODIS, MERIS and GOCI satellite data

The objectives of this study are to: (i) compare the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS), the Medium Resolution Imaging Spectrometer (MERIS) and the Geostationary Ocean Color Imager (GOCI) in retrieving the particulate backscattering coefficient, robust proxy of the concentration of suspended particulate matter (SPM), in the turbid waters of the East China Sea, then (ii) combine ocean color satellite data to field data recorded by an autonomous bio-optical profiling float to reconstitute and explain the dynamics of SPM within the whole water column in the middle of the East China Sea. MODIS, MERIS and GOCI seawater reflectance products at green, red and near-infrared (NIR) wavelengths are generated using atmospheric correction algorithms designed for turbid coastal waters then compared. Good agreement is obtained in the green and red while significant differences are observed in the NIR. The standard GOCI algorithm and a new one based on a regional empirical relationship established using MODIS satellite data are both proved to perform well over the whole range of water turbidity. Finally the dynamics of SPM is reconstituted along a three-month period to highlight a steady-state and stratified situation before the overpass of a large typhoon which mixed the water column, destroyed the deep-chlorophyll maximum and enhanced resuspension of bottom sediments varying according to tidal cycles.

Monitoring of urban heat island in Shanghai, China, from 1981 to 2010 with satellite data

This paper investigates the urban heat island (UHI) characteristics of Shanghai, China, during 1981–2010 using Landsat thematic mapper/multispectral scanner and moderate-resolution imaging spectroradiometer satellite data from the perspectives of time and space, with further analysis of the landscape pattern, the urban heat island effect ratio, and formative factors. The results show the following: (1) From the interannual variation, the secondary-medium temperature area and the medium temperature area are the main bodies of Shanghai UHI, accounting for 67.16–83.69 % of the total area, and have a rising trend over time. (2) From the landscape pattern, the secondary-medium temperature area and the medium temperature area have a dominant position over the whole UHI landscape pattern of Shanghai; the relatively high-temperature area gradually decreases, and its dominance over the whole UHI landscape pattern declines. (3) From the spatial distribution, the pattern in which the urban area initially had complete domination has gradually transformed to become primarily urban area centered and other construction land supplemented with rapid urbanization. Rapid increase of the green land in the urban area has to some extent reduced the UHI characteristics of the Shanghai area in recent years.

Estimating carbon dioxide exchange rates at contrasting northern peatlands using MODIS satellite data

Northern hemisphere peatlands play an important role in the global carbon (C) cycle, accounting for about 30% of global soil C and ~10–25% of global natural methane (CH4) emissions. Satellite remote sensing has the potential for extracting continuous information related to C exchange rates at regional and global extents, yet, few studies have focused on peatlands. In this study we examined the potential of moderate resolution imaging spectroradiometer (MODIS) vegetation indices (normalized difference vegetation index, NDVI and simple ratio, SR), MODIS light use efficiency (LUE) based gross primary production (GPP) and a MODIS derived phenological index (annual peak photosynthetic rate) for the estimation of eddy covariance (EC) flux-derived GPP and net ecosystem production (NEP) at four contrasting northern peatlands. At the four sites of this study MODIS NDVI and SR explained between 39% and 71%, and between 42% and 69% of the variation in EC-derived GPP, respectively; and between 25% and 53%, and between 29% and 39% of the variation in EC-derived NEP, respectively. The relationships were mostly consistent across sites and within sites, suggesting that data may be pooled across years and sites, which could simplify the prediction of gross and net C dioxide (CO2) uptake over large areas dominated by northern peatlands based on MODIS data. MODIS GPP explained between 68% and 89% of the variation in EC-derived GPP at the four study sites. The root mean square errors ranged between 0.62 and 1.16gCm−2d−1 and were similar to errors from ecosystem process model estimates reported in the literature. Annual peak MODIS GPP, NDVI and SR rates explained up to 50% of the variations in annual cumulative EC-derived GPP and NEP at two of the study sites. Our results show the potentials and limitations of MODIS data to monitor the C dynamics of northern peatlands; among the three studied approaches the MODIS LUE-based GPP approach showed better performance as a predictor of GPP and NEP. The other approaches (VIs and phenology) can provide important input data for LUE models.

Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements

Vegetation phenology is the study of the timing of seasonal events that are considered to be the result of adaptive responses to climate variations on short and long time scales. In the field of remote sensing of vegetation phenology, phenological metrics are derived from time series of optical data. For that purpose, considerable effort has been specifically focused on developing noise reduction and cloud-contaminated data removal techniques to improve the quality of remotely-sensed time series. Comparative studies between time series composed of satellite data acquired under clear and cloudy conditions and from radiometric data obtained with high accuracy from ground-based measurements constitute a direct and effective way to assess the operational use and limitations of remote sensing for predicting the main plant phenological events. In the present paper, we sought to explicitly evaluate the potential use of MODerate resolution Imaging Spectroradiometer (MODIS) remote sensing data for monitoring the seasonal dynamics of different types of vegetation cover that are representative of the major terrestrial biomes, including temperate deciduous forests, evergreen forests, African savannah, and crops. After cloud screening and filtering, we compared the temporal patterns and phenological metrics derived from in situ NDVI time series and from MODIS daily and 16-composite products. We also evaluated the effects of residual noise and the influence of data gaps in MODIS NDVI time series on the identification of the most relevant metrics for vegetation phenology monitoring. The results show that the inflexion points of a model fitted to a MODIS NDVI time series allow accurate estimates of the onset of greenness in the spring and the onset of yellowing in the autumn in deciduous forests (RMSE≤one week). Phenological metrics identical to those provided with the MODIS Global Vegetation Phenology product (MDC12Q2) are less robust to data gaps, and they can be subject to large biases of approximately two weeks or more during the autumn phenological transitions. In the evergreen forests, in situ NDVI time series describe the phenology with high fidelity despite small temporal changes in the canopy foliage. However, MODIS is unable to provide consistent phenological patterns. In crops and savannah, MODIS NDVI time series reproduce the general temporal patterns of phenology, but significant discrepancies appear between MODIS and ground-based NDVI time series during very localized periods of time depending on the weather conditions and spatial heterogeneity within the MODIS pixel. In the rainforest, the temporal pattern exhibited by a MODIS 16-day composite NDVI time series is more likely due to a pattern of noise in the NDVI data structure according to both rainy and dry seasons rather than to phenological changes. More investigations are needed, but in all cases, this result leads us to conclude that MODIS time series in tropical rainforests should be interpreted with great caution.

Simulation of surface temperature and ice cover of large northern lakes with 1-D models: a comparison with MODIS satellite data and in situ measurements

ABSTRACTLake surface temperature (LST) and ice phenology were simulated for various points differing in depth on Great Slave Lake and Great Bear Lake, two large lakes located in the Mackenzie River Basin in Canada's Northwest Territories, using the 1-D Freshwater Lake model (FLake) and the Canadian Lake Ice Model (CLIMo) over the 2002–2010 period, forced with data from three weather stations (Yellowknife, Hay River and Deline). LST model results were compared to those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observing System Terra and Aqua satellite platforms. Simulated ice thickness and freeze-up/break-up dates were also compared to in situ observations. Both models showed a good agreement with daily average MODIS LSTs on an annual basis (0.935 ≤ relative index of agreement ≤ 0.984 and 0.94 ≤ mean bias error ≤ 4.83). The absence of consideration of snow on lake ice in FLake was found to have a large impact on estimated ice thicknesses (25 cm thicker on average by the end of winter compared to in situ measurements; 9 cm thicker for CLIMo) and break-up dates (6 d earlier in comparison with in situ measurements; 3 d later for CLIMo). The overall agreement between the two models and MODIS LST products during both the open water and ice seasons was good. Remotely sensed data are a promising data source for assimilation into numerical weather prediction models, as they provide the spatial coverage that is not captured by in situ data.

Mapping abandoned agriculture with multi-temporal MODIS satellite data

Agriculture is expanding and intensifying in many areas of the world, but abandoned agriculture is also becoming more widespread. Unfortunately, data and methods to monitor abandoned agriculture accurately over large areas are lacking. Remote sensing methods may be able to fill this gap though, especially with the frequent observations provided by coarser-resolution sensors and new classification techniques. Past efforts to map abandoned agriculture relied mainly on Landsat data, making it hard to map large regions, and precluding the use of phenology information to identify abandoned agriculture. Our objective here was to test methods to map abandoned agriculture at broad scales with coarse-resolution satellite imagery and phenology data. We classified abandoned agriculture for one Moderate Resolution Imaging Spectroradiometer (MODIS) tile in Eastern Europe (~1,236,000km2) where abandoned agriculture was widespread. Input data included Normalized Difference Vegetation Index (NDVI) and reflectance bands (NASA Global MODIS Terra and Aqua 16-Day Vegetation Indices for the years 2003 through 2008, ~250-m resolution), as well as phenology metrics calculated with TIMESAT. The data were classified with Support Vector Machines (SVM). Training data were derived from several Landsat classifications of agricultural abandonment in the study area. A validation was conducted based on independently collected data. Our results showed that it is possible to map abandoned agriculture for large areas from MODIS data with an overall classification accuracy of 65%. Abandoned agriculture was widespread in our study area (15.1% of the total area, compared to 29.6% agriculture). We found strong differences in the MODIS data quality for different years, with data from 2005 resulting in the highest classification accuracy for the abandoned agriculture class (42.8% producer's accuracy). Classifications of MODIS NDVI data were almost as accurate as classifications based on a combination of both red and near-infrared reflectance data. MODIS NDVI data only from the growing-season resulted in similar classification accuracy as data for the full year. Using multiple years of MODIS data did not increase classification accuracy. Six phenology metrics derived with TIMESAT from the NDVI time series (2003–2008) alone were insufficient to detect abandoned agriculture, but phenology metrics improved classification accuracies when used in conjunction with NDVI time series by more than 8% over the use of NDVI data alone. The approach that we identified here is promising and suggests that it is possible to map abandoned agriculture at broad scales, which is relevant to gain a better understanding of this important land use change process.

Inter-annual variations of snow days over Switzerland from 2000–2010 derived from MODIS satellite data

Abstract. Snow cover plays a vital role in the Swiss Alps and therefore it is of major interest to determine and understand its variability on different spatiotemporal scales. Within the activities of the National Climate Observing System (GCOS Switzerland) inter-annual variations of snow days over Switzerland were derived from 2000 to 2010 based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. To minimize the impact of cloud cover on the MODIS snow product MOD10C1, we implemented a post-processing technique based on a forward and backward gap-filling approach. Using the proposed methodology it was possible to determine the total number of annual snow days over Switzerland from 2000 to 2010 (SCDMODIS). The accuracy of the calculated snow days per year were quantitatively evaluated against three in situ snow observation sites representing different climatological regimes (SCDin_situ). Various statistical indices were computed and analysed over the entire period. The overall accuracy between SCDMODIS and SCDin_situ on a daily basis over 10 yr is 88% to 94%, depending on the regional characteristics of each validation site. Differences between SCDMODIS and SCDin_situ vary during the snow accumulation period in autumn and smaller differences after spring, in particularly for the Central Alps.