Comparison of multiple models for remote sensing of carbon exchange using MODIS data in conifer-dominated forests

Boreal forests in the northern hemisphere provide important sinks for storing carbon dioxide (CO₂). However, the size and distribution of these sinks remain uncertain. In particular, many remote-sensing models show a strong bias in the simulation of carbon fluxes for evergreen needleleaf forest. The objective of this study is to improve these predictive models for accurately quantifying temporal changes in the net ecosystem exchange (NEE) of conifer-dominated forest solely based on satellite remote sensing, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daytime land-surface temperature (LST), night-time LST′, enhanced vegetation index (EVI), land–surface water index (LSWI), fraction of absorbed photosynthetically active radiation (FPAR), and leaf area index (LAI). Considering that the component fluxes, gross primary production (GPP), and ecosystem respiration (R_e), are strongly influenced by vegetation phenology, seasonality information was extracted from time-series MODIS EVI data based on non-linear least-squares fits of asymmetric Gaussian model functions with a software package for analysing the time-series of satellite sensor data (TIMESAT). The results indicated that models directly incorporating phenological information failed to improve their performance for temperate deciduous forest. Instead, three methods to retrieve the component fluxes – GPP and R_e – including direct estimates, models incorporating the phenological information, and models developed based on the threshold value (LST 273 K), were explored respectively. All methods improved NEE estimates markedly and models developed based on the threshold value performed best, and provided a future framework for accurate remote sensing of NEE in evergreen forest.     

Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data

Vegetation phenology is the chronology of periodic phases of development. It constitutes an efficient bio-indicator of impacts of climate changes and a key parameter for understanding and modelling vegetation-climate interactions and their implications on carbon cycling. Numerous studies were devoted to the remote sensing of vegetation phenology. Most of these were carried out using data acquired by AVHRR instrument onboard NOAA meteorological satellites. Since 1999, multispectral images were acquired over the whole earth surface every one to two days by MODIS instrument onboard Terra and Aqua platforms. In comparison with AVHRR, MODIS constitutes a significant technical improvement in terms of spatial resolution, spectral resolution, geolocation accuracy, atmospheric corrections scheme and cloud screening and sensor calibration. In this study, 250 m daily MODIS data were used to derive precise vegetation phenological dates over deciduous forest stands. Phenological markers derived from MODIS time-series and provided by MODIS Global Land Cover Dynamics product (MOD12Q2) were compared to field measurements carried out over the main deciduous forest stands across France and over five years. We show that the inflexion point of the asymmetric double-sigmoid function fitted to NDVI temporal profile is a good marker of the onset of green-up in deciduous stands. At plot level, the prediction uncertainty is 8.5 days and the bias is 3.5 days. MODIS Global Land Cover Dynamics MOD12Q2 provides estimates of onset of green-up dates which deviate substantially from in situ observations and do not perform better than the null model. RMSE values are 20.5 days (bias -17 days) using the onset of greenness increase and 36.5 days (bias 34.5 days) using the onset of greenness maximum. An improvement of prediction quality is obtained if we consider the average of MOD12Q2 onset of greenness increase and maximum as marker of onset of green-up date. RMSE decreases to 16.5 days and bias to 7.5 days.     

Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.     

Satellite multi-sensor mapping of suspended particulate matter in turbid estuarine and coastal ocean,China

In this work, five ocean-colour sensors, the Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite (Terra MODIS), Moderate Resolution Imaging Spectroradiometer aboard the Aqua satellite (Aqua MODIS), Medium Range Imaging Spectrometer aboard the Environmental Satellite (Envisat MERIS), Medium Resolution Spectral Imager aboard the FY-3 satellite (FY-3 MERSI), and Geostationary Ocean Colour Imager (GOCI), were selected to examine the compatibility of an algorithm proposed for suspended particulate matter (SPM) retrieval and concordance of satellite products retrieved from different ocean-colour sensors. The results could effectively increase revisit frequency and complement a temporal gap of time series satellites that may exist between on-orbit and off-orbit. Using in situ measurements from 17 cruise campaigns between 2004 and 2012, the SPM retrieval algorithm was recalibrated so as to be universal and adapted for multi-sensor retrievals. An inter-comparison of multi-sensor-derived products showed that GOCI-derived SPM and Envisat MERIS-derived SPM had the best fitting on a 1:1 scatterplot, with a statistic regression slope of 0.9617 and an intercept of 0.0041 (in units of g l^–1), respectively. SPM products derived from three sensors with nearly synchronous transit, Envisat MERIS, Terra MODIS, and FY-3 MERSI, exhibited excellent accordance with mean differences of 0.056, 0.057, and 0.013 g l^–1 in three field fixed stations, respectively, in the Yangtze estuary. Terra MODIS-derived SPM with GOCI-derived SPM, except in the high SPM waters of Hangzhou Bay, and Aqua MODIS-derived SPM with GOCI-derived SPM, except in the moderate SPM waters of the South Branch and south of the Subei Coast, showed a good correspondence. Meanwhile, synchronous multi-sensor-derived SPM with concurrent in situ SPM time series observed in fixed field stations mostly displayed a good correspondence. Results suggest that the algorithm is feasible and compatible for SPM retrieval by multiple sensors.     

Satellite remote sensing of tropical forest canopies and their seasonal dynamics

Seasonal patterns of tropical evergreen forest green-up in Amazonia, corresponding to drought and the dry season, have recently been detected by the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) products of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. These observations provide additional evidence for solar radiation as the primary limiting factor regulating wet-tropical ecosystem processes. However, in situ structural mechanisms for forest canopy green-up are unclear and frequently inconsistent with observations. Here, we investigate the signal of seasonal green-up at several intensively measured sites, applying a rigorous series of filters to minimize error from atmospheric contamination that is common in tropical regions. We find that, while satellite-observed forest seasonality is sensitive to data-quality filtering, statistical noise reduction and spatial averaging, the signal is robust at sites where field observations are available, and in particular for the EVI. For the sites where field data are unavailable, it appears that additional filters to those commonly used to remove cloud effects and aerosols also reduce the seasonal magnitude of the LAI. These findings imply that seasonal tropical evergreen forest green-up remains sensitive to the methodology used in removing seasonal contamination from atmospheric conditions and that further field measurements and comparisons to remote sensing are required to reduce this uncertainty.     

Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet

This study evaluates the performance of the beta-test MODIS (MOD10A1) daily albedo product using in situ data collected in Greenland during summer 2004. Results indicate the beta-test product tracks the general seasonal variability in albedo but exhibits significant more temporal variability than observed at the stations. This may indicate problems with the cloud detection algorithm, and/or failure of the BRDF model to adequately model the bidirectional reflectance of snow. Comparisons with in situ observations at five automatic weather stations in Greenland indicate an overall RMSE of 0.067 for the Terra instrument and an RMSE of 0.075 on Aqua. The Terra-retrieved-albedo are slightly better correlated with the in situ data than the Aqua retrievals (r = 0.79 versus r = 0.77). Comparisons were also made between the MODIS daily albedo product and the MODIS 16-day albedo product (MOD43B3). Results indicate general correspondence between the two products, with better agreement found using the Terra-retrieved-albedo than the Aqua-retrieved albedo. The reason for the differences in albedo between the Aqua and Terra satellites remains unclear. At the stations examined, both the Terra and Aqua retrievals were made at nearly the same time of the day and therefore the differences in albedo between the satellites cannot be explained by differences in solar illumination. Finally, the albedo derived using MODIS data and the direct estimation algorithm (DEA) was also compared with 2004 Greenland in situ data. Results from this comparison suggest that the DEA performs well as long as the solar zenith angle of the observation is not greater than 70°.     

Quality assessment and validation of the MODIS global land surface temperature

This paper presents an evaluation of the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands and the status of land surface temperature (LST) version-3 standard products retrieved from Terra MODIS data. The accuracy of daily MODIS LST products has been validated in more than 20 clear-sky cases with in situ measurement data collected in field campaigns in 2000–2002. The MODIS LST accuracy is better than 1°C in the range from ?10 to 50°C. Refinements and improvements were made to the new version of MODIS LST product generation executive code. Using both Terra and Aqua MODIS data for LST retrieval improves the quality of the LST product and the diurnal feature in the product due to better temporal, spatial and angular coverage of clear-sky observations.     

Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have ∼ 30% of the world population and ∼ 2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries.     

Detection rates of the MODIS active fire product in the United States

MODIS active fire data offer new information about global fire patterns. However, uncertainties in detection rates can render satellite-derived fire statistics difficult to interpret. We evaluated the MODIS 1 km daily active fire product to quantify detection rates for both Terra and Aqua MODIS sensors, examined how cloud cover and fire size affected detection rates, and estimated how detection rates varied across the United States. MODIS active fire detections were compared to 361 reference fires (≥ 18 ha) that had been delineated using pre- and post-fire Landsat imagery. Reference fires were considered detected if at least one MODIS active fire pixel occurred within 1 km of the edge of the fire. When active fire data from both Aqua and Terra were combined, 82% of all reference fires were found, but detection rates were less for Aqua and Terra individually (73% and 66% respectively). Fires not detected generally had more cloudy days, but not when the Aqua data were considered exclusively. MODIS detection rates decreased with fire size, and the size at which 50% of all fires were detected was 105 ha when combining Aqua and Terra (195 ha for Aqua and 334 ha for Terra alone). Across the United States, detection rates were greatest in the West, lower in the Great Plains, and lowest in the East. The MODIS active fire product captures large fires in the U.S. well, but may under-represent fires in areas with frequent cloud cover or rapidly burning, small, and low-intensity fires. We recommend that users of the MODIS active fire data perform individual validations to ensure that all relevant fires are included.     

Analysis of leaf area index products from combination of MODIS Terra and Aqua data

A prototype product suite, containing the Terra 8-day, Aqua 8-day, Terra-Aqua combined 8- and 4-day products, was generated as part of testing for the next version (Collection 5) of the MODerate resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products. These products were analyzed for consistency between Terra and Aqua retrievals over the following data subsets in North America: single 8-day composite over the whole continent and annual time series over three selected MODIS tiles (1200 × 1200 km). The potential for combining retrievals from the two sensors to derive improved products by reducing the impact of environmental conditions and temporal compositing period was also explored. The results suggest no significant discrepancies between large area (from continent to MODIS tile) averages of the Terra and Aqua 8-day LAI and surface reflectances products. The differences over smaller regions, however, can be large due to the random nature of residual atmospheric effects. High quality retrievals from the radiative transfer based algorithm can be expected in 90-95% of the pixels with mostly herbaceous cover and about 50-75% of the pixels with woody vegetation during the growing season. The quality of retrievals during the growing season is mostly restricted by aerosol contamination of the MODIS data. The Terra-Aqua combined 8-day product helps to minimize this effect and increases the number of high quality retrievals by 10-20% over woody vegetation. The combined 8-day product does not improve the number of high quality retrievals during the winter period because the extent of snow contamination of Terra and Aqua observations is similar. Likewise, cloud contamination in the single-sensor and combined products is also similar. The LAI magnitudes, seasonal profiles and retrieval quality in the combined 4-day product are comparable to those in the single-sensor 8-day products. Thus, the combined 4-day product doubles the temporal resolution of the seasonal cycle, which facilitates phenology monitoring in application studies during vegetation transition periods. Both Terra and Aqua LAI products show anomalous seasonality in boreal needle leaf forests, due to limitations of the radiative transfer algorithm to model seasonal variations of MODIS surface reflectance data with respect to solar zenith angle. Finally, this study suggests that further improvement of the MODIS LAI products is mainly restricted by the accuracy of the MODIS observations.     

Sensitivity of vegetation phenology detection to the temporal resolution of satellite data

Vegetation phenology derived from satellite data has increasingly received attention for applications in environmental monitoring and modelling. The accuracy of phenological estimates, however, is unknown at the regional and global level because field validation data are insufficient. To assess the accuracy of satellite‐derived phenology, this study investigates the sensitivity of phenology detection to both the temporal resolution of sampling and the number of consecutive missing values (usually representing cloud cover) in the time series of satellite data. To do this, time series of daily vegetation index data for various ecosystems are modelled and simulated using data from Moderate‐Resolution Imaging Spectroradiometer (MODIS) data. The annual temporal data are then fitted using piecewise logistic functions, which are employed to calculate curvature change rate for detecting phenological transition dates. The results show that vegetation phenology can be estimated with a high precision from time series with temporal resolutions of 6–16 days even if daily data contains some uncertainties. If the temporal resolution is no coarser than 16 days for time series sampled using an average composite, the absolute errors are less than 3 days. On the other hand, the phase shift of temporal sampling is shown to have limited impacts on phenology detection. However, the accuracy of phenology detection may be reduced greatly if missing values in the time series of 16‐day MODIS data occur around the onsets of phenological transition dates. Even so, the probability that the absolute error in phenological estimates is greater than 5 days is less than 4% when only one period is missing in the time series of 16‐day data during vegetation growing seasons; this probability increases to 20% if there are two consecutive missing values.     

Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥ 500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat (field scale, 30 m) to MODIS (500 m), and compare datasets derived from each instrument. Landsat and MODIS yield similar estimates of the start of greenness (r² = 0.60), although we find that a high degree of spatial phenological variability within coarser-scale MODIS pixels may be the cause of the remaining uncertainty. In addition, spatial variability is smoothed in MODIS, a potential source of error when comparing in situ or climate data to satellite phenology. We show that our method for deriving phenology from satellite data generates spatially coherent interannual phenology departures in MODIS data. We test these estimates from 2000 to 2005 against long-term records from Harvard Forest (Massachusetts) and Hubbard Brook (New Hampshire) Experimental Forests. MODIS successfully predicts 86% of the variance at Harvard forest and 70% of the variance at Hubbard Brook; the more extreme topography of the later is inferred to be a significant source of error. In both analyses, the satellite estimate is significantly dampened from the ground-based observations, suggesting systematic error (slopes of 0.56 and 0.63, respectively). The satellite data effectively estimates interannual phenology at two relatively simple deciduous forest sites and is internally consistent, even with changing spatial scale. We propose that continued analyses of interannual phenology will be an effective tool for monitoring native forest responses to global-scale climate variability.     

Autumn leaf phenology: discrepancies between in situ observations and satellite data at urban and rural sites

ABSTRACT

Autumn phenophases, such as leaf colouration (LC) and leaf fall (LF), have received considerably less attention than their spring counterparts (budburst and leaf unfolding) but are equally important determinants of the duration of the growing season and thus have a controlling in?uence on the carbon-uptake period. Here, we examined THE trends (1968–2016) in in situ observations of the timing of LC and LF from a suite of deciduous trees at three rural sites and one urban site in Ireland. Satellite-derived autumn phenological metrics including mid-senescence (MS) and end of senescence (ES) based on two-band enhanced vegetation index (EVI2) from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) from 1982 to 2016 at a spatial resolution of 5km² were also examined. The aim of this study was to assess the effectiveness of satellite remote sensing in capturing autumn phenology as determined by in situ observations . Analysis of in situ data (1968–2016) revealed the urban site to be significantly different from the rural sites as LC and LF occurred later in the season and the duration of the autumn season (LF-LC) became shorter over time. These trends may be partly driven by the presence of artificial light in the city. On average (1982–2016), there was a 6-day delay in the timing of MS compared to LC and a much larger difference (21 days) between ES and LF. This resulted in a 31-day autumn duration as defined by satellite data compared to 16 days from in situ observations. Furthermore, there was little overlap in timing between LC and MS, and LF and ES at the rural sites only. Discrepancies between in situ and satellite data may be attributed to the satellite data integrating a much broader vegetation signal across a heterogeneous landscape than in situ observations of individual trees. Therefore, at present, satellite-derived autumn phenology may be more successful in capturing in situ observations across large homogeneous landscapes of similar vegetation types (e.g. forested areas) than in heterogeneous landscapes (e.g. small mixed farms, urban areas, etc.) as is the case in Ireland where the in situ observations of trees may not be reflective of the overall vegetation. Matching the scale of satellite data with in situ observations remains a challenging task but may, at least in part, be overcome by increasing the extent of observations to include a wider range of species and in future as satellite data become available at higher spatial and temporal resolutions.     

Usability of noise-free daily satellite-observed green–red vegetation index values for monitoring ecosystem changes in Borneo

We examined the usability of daily green–red vegetation index (GRVI) observations from the Terra and Aqua Moderate Resolution Imaging Spectroradiometer satellite on cloud-free days for monitoring ecosystem changes in Bornean tropical forests at a 500 m spatial resolution over 11 years (2003–2013). The number of observational days of cloud-free GRVI data in the southwest monsoon period (May–October; 1–5 days/month) was greater than that in the northeast monsoon period (November–April; 0–2 days/month). Spatial variation in the observation frequency was noticed, with Terra (morning) and Aqua (afternoon) data showing different geographic distribution patterns of cloud-free data. The observation frequency in the western Kalimantan mountains (Sarawak and Sabah) was 1–2 days/month greater than that in the eastern mountains (Kalimantan). The quality of cloud-free GRVI data was validated by using sky images taken at the same time as the satellite observations and canopy surface images in a tropical rainforest. In oil palm and acacia plantations and peatlands, which were mainly distributed in coastal regions of Sarawak and West and Central Kalimantan, the cloud-free daily GRVI value fell below zero owing to deforestation and forest degradation caused by forest fire and increased with replanting and vegetation recovery. These results indicate that daily cloud-free GRVI data from multiple satellites collected at different times of the day are required for accurate monitoring of intra- and interannual phenological variation and forest degradation attributed to changes in climatic conditions and deforestation caused by human activities in tropical ecosystems.     

Improving continuity of MODIS terrestrial photosynthesis products using an interpolation scheme for cloudy pixels

The Moderate Imaging Spectroradiometer (MODIS) sensors onboard the NASA Terra and Aqua satellites provide the means for frequent measurement and monitoring of the status and seasonal variability in global vegetation phenology and productivity. However, while MODIS reflectance data are often interrupted by clouds, terrestrial processes like photosynthesis are continuous, so MODIS photosynthesis data must be able to cope with cloudy pixels. We developed cloud‐correction algorithms to improve retrievals of the MODIS photosynthesis product (PSNnet) corresponding to clear sky conditions by proposing four alternative cloud‐correction algorithms, which have different levels of complexity and correct errors associated with cloudy‐pixel surface reflectance. The cloud‐correction algorithms were applied at four weather stations, two fluxtower sites and the Pacific Northwest (PNW) region of the USA to test a range of cloud climatologies. Application of the cloud‐correction algorithms increased the magnitude of both daily and annual MODIS PSNnet results. Our results indicate that the proposed cloud correction methods improve the current MODIS PSNnet product considerably at both site and regional scales and weekly to annual time steps for areas subjected to frequent cloud cover. The corrections can be applied as a post‐processing interpolation of PSNnet, and do not require reprocessing of the MOD17A2 algorithm.     

Integrated assessment on multi-temporal and multi-sensor combinations for reducing cloud obscuration of MODIS snow cover products of the Pacific Northwest USA

MODIS (Moderate Resolution Imaging Spectroradiometer) snow cover products, of daily, freely available, worldwide spatial extent at medium spatial resolution, have been widely applied in regional snow cover and modeling studies, although high cloud obscuration remains a concern in some applications. In this study, various approaches including daily combination, adjacent temporal deduction, fixed-day combination, flexible multi-day combination, and multi-sensor combination are assessed to remove cloud obscuration while still maintain the temporal and spatial resolutions. The performance of the resultant snow cover maps are quantitatively evaluated against in situ observations at 244 SNOTEL stations over the Pacific Northwest USA during the period of 2006-2008 hydrological years. Results indicate that daily Terra and Aqua MODIS combination and adjacent temporal deduction can reduce cloud obscuration and classification errors although an annual mean of 37% cloud coverage remains. Classification errors in snow-covered months are actually small and tend to underestimate the snow cover. Primary errors of MODIS daily, fixed and flexible multi-day combination products occur during transient months. Flexible multi-day combination is an efficient approach to maintain the balance between temporal resolution and realistic estimation of snow cover extent since it uses two thresholds to control the combination processes. Multi-sensor combinations (daily or multi-day), taking advantage of MODIS high spatial resolution and AMSR-E cloud penetration ability, provide cloud-free products but bring larger image underestimation errors as compared with their MODIS counterparts.     

Satellite remote sensing of fine particulate matter (PM2.5) air quality over Beijing using MODIS

Fine particulate matter (aerodynamic diameters of less than 2.5 µm, PM_2.5) air pollution has become one of the major environmental challenges, causing severe environmental issues in urban visibility, climate, and public health. In this study, ground-level PM_2.5 concentrations, air-quality categories (AQCs), and health risk categories (HRCs) over Beijing, China, have been estimated based on mid-visible column aerosol optical depth (AOD) measurements extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data on board both Terra and Aqua satellites. Our results indicate that the MODIS AOD retrievals at 550 nm (AOD₅₅₀) match hourly aerosol robotic network (AERONET) measurements with correlation coefficients (r) of 0.950 for Terra and 0.895 for Aqua. The relationship between ground-level PM_2.5 and MODIS AOD₅₅₀ from March 2012 to February 2013 showed correlation coefficients of 0.69, 0.60, and 0.73 for spring, summer, and autumn, respectively. The atmospheric boundary layer height and relative humidity (RH) adjustments improved the AOD–PM_2.5 relationship in summer months. The estimates of daily average PM_2.5 from satellite measurements were used to predict both AQCs and HRCs, which are well matched with observations. Satellite remote sensing of atmospheric aerosols continues to show great potential for estimating ground-level PM_2.5 concentrations and can be further used to monitor the atmospheric environment in China.     

Phenology of vegetation in Southern England from Envisat MERIS terrestrial chlorophyll index (MTCI) data

Given the close association between climate change and vegetation response, there is a pressing requirement to monitor the phenology of vegetation and understand further how its metrics vary over space and time. This article explores the use of the Envisat MERIS terrestrial chlorophyll index (MTCI) data set for monitoring vegetation phenology, via its estimates of chlorophyll content. The MTCI was used to construct the phenological profile of and extract key phenological event dates from woodland and grass/heath land in Southern England as these represented a range of chlorophyll contents and different phenological cycles. The period 2003–2008 was selected as this was known to be a period with temperature and phenological anomalies. Comparisons of the MTCI-derived phenology data were made with ground indicators and climatic proxy of phenology and with other vegetation indices: MERIS global vegetation index (MGVI), MODIS normalized difference vegetation index (NDVI) and MODIS enhanced vegetation index (EVI). Close correspondence between MTCI and canopy phenology as indicated by ground observations and climatic proxy was evident. Also observed was a difference between MTCI-derived phenological profile curves and key event dates (e.g. green-up, season length) and those derived from MERIS MGVI, MODIS NDVI and MODIS EVI. The research presented in this article supports the use of the Envisat MTCI for monitoring vegetation phenology, principally due to its sensitivity to canopy chlorophyll content, a vegetation property that is a useful proxy for the canopy physical and chemical alterations associated with phenological change.