Surface Reflectance Products Research Articles

LANDSAT-BASED WOODY VEGETATION COVER MONITORING IN SOUTHERN AFRICAN SAVANNAHS

Abstract. Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000 km2 covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5 m-pixel colour aerial photography to collect > 15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80 % and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot-spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.

A New Neighboring Pixels Method for Reducing Aerosol Effects on the NDVI Images

A new algorithm was developed in this research to minimize aerosol effects on the normalized difference vegetation index (NDVI). Simulation results show that in red-NIR reflectance space, variations in red and NIR channels to aerosol optical depth (AOD) follow a specific pattern. Based on this rational, the apparent reflectance in these two bands of neighboring pixels were used to reduce aerosol effects on NDVI values of the central pixel. We call this method the neighboring pixels (NP) algorithm. Validation was performed over vegetated regions in the border area between China and Russia using Landsat 8 Operational Land Imager (OLI) imagery. Results reveal good agreement between the aerosol corrected NDVI using our algorithm and that derived from the Landsat 8 surface reflectance products. The accuracy is related to the gradient of NDVI variation. This algorithm can achieve high accuracy in homogeneous forest or cropland with the root mean square error (RMSE) being equal to 0.046 and 0.049, respectively. This algorithm can also be applied to atmospheric correction and does not require any information about atmospheric conditions. The use of the moving window analysis technique reduces errors caused by the spatial heterogeneity of aerosols. Detections of regions with homogeneous NDVI are the primary sources of biases. This new method is operational and can prove useful at different aerosol concentration levels. In the future, this approach may also be used to examine other indexes composed of bands attenuated by noises in remote sensing.

Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product.

The surface reflectance, i.e., satellite derived top of atmosphere (TOA) reflectance corrected for the temporally, spatially and spectrally varying scattering and absorbing effects of atmospheric gases and aerosols, is needed to monitor the land surface reliably. For this reason, the surface reflectance, and not TOA reflectance, is used to generate the greater majority of global land products, for example, from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Even if atmospheric effects are minimized by sensor design, atmospheric effects are still challenging to correct. In particular, the strong impact of aerosols in the Visible and Near Infrared spectral range can be difficult to correct, because they can be highly discrete in space and time (e.g., smoke plumes) and because of the complex scattering and absorbing properties of aerosols that vary spectrally and with aerosol size, shape, chemistry and density. This paper presents the Landsat 8 Operational Land Imager (OLI) atmospheric correction algorithm that has been developed using the Second Simulation of the Satellite Signal in the Solar Spectrum Vectorial (6SV) model, refined to take advantage of the narrow OLI spectral bands (compared to Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+)), improved radiometric resolution and signal-to-noise. In addition, the algorithm uses the new OLI Coastal aerosol band (0.433-0.450μm), which is particularly helpful for retrieving aerosol properties, as it covers shorter wavelengths than the conventional Landsat, TM and ETM+ blue bands. A cloud and cloud shadow mask has also been developed using the "cirrus" band (1.360-1.390 μm) available on OLI, and the thermal infrared bands from the Thermal Infrared Sensor (TIRS) instrument. The performance of the surface reflectance product from OLI is analyzed over the Aerosol Robotic Network (AERONET) sites using accurate atmospheric correction (based on in situ measurements of the atmospheric properties), by comparison with the MODIS Bidirectional Reflectance Distribution Function (BRDF) adjusted surface reflectance product and by comparison of OLI derived broadband albedo from United States Surface Radiation Budget Network (US SURFRAD) measurements.

MODIS observations of water color of the largest 10 lakes in China between 2000 and 2012

ABSTRACTForel-Ule (FU) index of water color is an important parameter in traditional water quality investigations. We retrieved the FU index of the largest 10 lakes in China during 2000-2012 from MODerate-resolution Imaging Spectroradiometer surface reflectance product (MOD09) images. Since FU index is an optical parameter, it can be derived from optical remote sensing data by direct formulas, which is invariant with region and season. Based on validation by in situ measured reflectance data, the FU index products are reliable, with average relative error of 7.7%. FU index can be used to roughly assess water clarity: the clearer a water body is, and the bluer it is in color, the smaller its FU index is. FU index can also be used to roughly classify trophic state into three classes: oligotrophic, mesotrophic, and eutrophic. We analyzed the spatial, interannual, and seasonal variations of the FU index and its implications for water clarity and trophic state, and the findings are mostly consistent with the results from related literature. All in all, it might be a feasible way to roughly assess inland water quality by FU index in large region and over long time period.

Landsat 8: Providing continuity and increased precision for measuring multi-decadal time series of total suspended matter

Abstract The water clarity of many inland water bodies is under threat due to intensifying land use pressures in conjunction with changes in water levels that result from increasing demand and climate variability. The recent launch of Landsat 8 coupled with Geoscience Australia's recent reprocessing of the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM +) archives over the whole of Australia to a consistent surface reflectance product enables sub continental scale spatio-temporal analysis of freshwater optical water quality in support of monitoring and decision making for water management agencies. In this research, we present an objective assessment of the potential of Landsat 5 TM, Landsat 7 ETM + and Landsat 8 Operational Land Imager (OLI) data for monitoring inland water quality dynamics over a number of lakes and reservoirs with a range of optical water types in New South Wales and Queensland, Australia. We used bio-optical modelling to develop sensor-specific total suspended matter (TSM) retrieval algorithms that account for the difference in relative spectral response between Landsat 7 ETM + and Landsat 8 OLI. We were able to compare the suitability of the different sensors for optical water quality measurements using water bodies that fell within Landsat path overlaps where Landsat images of surface reflectance were acquired within 24 h between Landsat 5 TM and Landsat 7 ETM + or Landsat 7 ETM + and Landsat 8 OLI. These water bodies represent a range of hydrological and limnological conditions, and enabled us to assess: 1) the comparability of TSM measurements retrieved from each sensor, and 2) the surface reflectance to image noise characteristics of Landsat 7 ETM + and Landsat 8 OLI. Comparisons of lake surface reflectance and noise equivalent reflectance difference show that the improved radiometric resolution and increased quantization of Landsat 8 OLI relative to Landsat 7 ETM + significantly reduce image noise and spectral heterogeneity, indicating that Landsat 8 OLI data are expected to provide more precise water quality retrievals relative to Landsat 7 ETM +. We found that: 1) the TSM retrievals from the different sensors are highly comparable; 2) Landsat 5 TM overestimated TSM relative to Landsat 7 ETM + by 6.4%; and 3) Landsat 7 ETM + overestimated TSM relative to Landsat 8 OLI by only 1.4%. Retrieved TSM values were highly correlated with independent in situ data acquired within 24 h of satellite overpass ( r = 0.99) with a mean average error of 14 mg/L. The results demonstrate that time series analysis of TSM retrievals can be conducted across a wide range of lakes at the sub-continental scale to characterise the multi-decadal TSM dynamics.

The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)

Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles.

An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations

Abstract The newly launched Landsat 8 satellite continues the long and extremely important record of Earth observation from the Landsat program. We analyzed differences between Landsat 7 and Landsat 8 surface reflectances and cirrus cloud characterization to address how substitutable Landsat 8 observations are within this long archive. Comparison of surface reflectance estimates acquired near simultaneously during Landsat 8's underflight orbital placement shows that Landsat 8 surface reflectance is consistently darker in the blue, green, and red bands and brighter in the near infrared than in Landsat 7. Vegetation indices that rely on the visible and near infrared bands should be used with caution as individual biases in index components can be amplified to create large biases in vegetation indices. We also analyzed time series datasets from the Landsat Climate Data Record (CDR) surface reflectance product across four scenes that contained only Landsat 7 data, Landsat 7 data and only Landsat 8 data post-launch, and Landsat 7 data and data from both sensors post-launch to investigate how sensor differences propagate in time series analysis. If left uncorrected or unexplained, the difference in reflectance between Landsat 7 and Landsat 8 creates spurious time trends in visible wavelengths and in the Normalized Difference Vegetation Index (NDVI). The introduction of Landsat 8 into time series of Landsat 7 data also biases the mean reflectance or vegetation index value as measured by a time series model intercept while increasing the Root Mean Squared Error of such models. We characterized the spectral reflectance of cirrus clouds in the underflight data that were omitted from Landsat 7 cloud masks but were detected in Landsat 8's cloud mask due to the use of the newly added cirrus band. While these cirrus cloud observations missed in Landsat 7's cloud mask are only slightly brighter in the visible bands, a simulation of time series containing Landsat 8 data that does not use the cirrus band shows that omission of cirrus clouds can result in anomalously brighter time series intercepts and positive time trends. Our results indicate that while Landsat 8 has improved on the legacy of previous sensors through increased radiometric resolution, better cloud identification, and better geometric accuracy, difference in reflectance between sensors in the current Landsat CDR product must be corrected or explained within time series analysis to avoid deleterious consequences. Future efforts should identify the contributions of target specific effects versus differences in atmospheric correction methods to better inform approaches to synthesize the two sensors.

An estimation of surface reflectance for Advanced Himawari Imager (AHI) data using 6SV

The surface reflectance is essential to retrieval various indicators related land properties such as vegetation index, albedo and etc. In this study, we estimated surface reflectance using Himawari-8 / Advanced Himawari Imager (AHI) channel data. In order to estimate surface reflectance from Top of Atmosphere (TOA) reflectance, the atmospheric correction is necessary because all of the TOA reflectance from optical sensor is affected by gas molecules and aerosol in the atmosphere. We used Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) Radiative Transfer Model (RTM) to correct atmospheric effect, and Look-Up Table (LUT) to shorten the calculation time. We verified through comparison Himawri- 8 / AHI surface reflectance and Proba-V S1 products. As a result, bias and Root Mean Square Error (RMSE) are calculated about -0.02 and 0.05.

Development and validation of the Landsat-8 surface reflectance products using a MODIS-based per-pixel atmospheric correction method

ABSTRACTLandsat satellites have the longest history of making global-scale Earth observations at medium spatial resolution of any series of satellites and have been widely used in various remote-sensing fields. However, many remote-sensing applications, including large-area or long-term land-cover monitoring, need Landsat reflectance data that have had accurate atmospheric correction carried out. In this research, a MODIS-based per-pixel atmospheric correction procedure was developed and employed to produce the surface reflectance (SR) product. A total of 510 Landsat-8 Operational Land Imager (OLI) scenes covering the whole of China in 2013 were collected and processed. The mean relative differences between the surface and top-of-atmosphere (TOA) reflectance for China, composited and expressed as percentages, were found to be 67, 47, 18, 13, 4, 4, and 7% for Landsat-8 OLI bands 1, 2, 3, 4, 5, 6, and 7, respectively. Then, the accuracy of MODIS atmospheric products was validated using ground-based sun-radiometer observation network data, including Sun/sky-radiometer Observation Network (SONET) and Aerosol Robotic Network (AERONET) data collected from 14 SONET/AERONET stations. The validation results showed that the MODIS atmospheric products are reliable for China, with an R2 value of 0.78 and a root mean square error (RMSE) value of 0.12 for aerosols, and an R2 value of 0.98 and an RMSE of 0.25 for water vapour. Third, the SR product using our per-pixel atmospheric correction method was evaluated by comparison with the MODIS daily surface reflectance product (MOD09GA) and the United States Geological Survey (USGS) provisional Landsat-8 SR product, with a mean R2 of 0.93 and an RMSE of 0.02 for MOD09GA; and with a mean R2 of 0.97 and an RMSE of 0.01 for the USGS SR product. Finally, the advantage of our per-pixel atmospheric correction method over the per-scene method was investigated by analysis of the spatial variation of the atmospheric parameters within one Landsat scene (about 1.51.5), with a mean standard deviation value of 0.03–0.09 for aerosol. When such aerosol variation was omitted as the per-scene atmospheric correction method, the SR absolute error due to aerosol optical thickness (AOT) spatial variation was about 0.027, 0.018, 0.005, 0.003, 0.002, 0.0007, and 0.003 for the seven reflectance bands of Landsat-8. Therefore, use of Landsat-8 SR products over China with our per-pixel atmospheric correction was proved reliable, and more promising than the per-scene method, especially for the short-wavelength bands.

Assessment of temporal dynamics of snow cover and its validation with hydro-meteorological data in parts of Chenab Basin, western Himalayas

The present investigation was conducted to analyze the temporal patterns of snow cover area% (SCA%), air temperature, snowfall and river discharge in parts of Chenab basin, western Himalayas. The relationship of mean SCA% with mean air temperature and river discharge was also tested using Pearson’s product-moment correlation at 95% confidence limit and further sensitivity analysis of river discharge to SCA and SCA to air temperature was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day surface reflectance product MOD09A1 was used to delineate SCA during the period 2000–2013. Moreover, variation in the lowest elevation from where snow cover area starts (LESCA) was also analyzed and its relationship with mean air temperature was also studied. Non-parametric method, Mann-Kendall test was employed to determine the trend in the SCA%, air temperature, snowfall and river discharge. The investigation carried out for three meteorological stations i.e. Batote, Reasi and Tandi revealed significant findings. At Batote and Reasi, statistically significant decreasing trends were observed over the period 2000 to 2012, for maximum, minimum and mean air temperature. Mean minimum SCA% exhibited a significant upward trend during 2000–2013 which is corroborated by the significantly increasing trend of mean annual snowfall (Tandi station) from 2000 to 2010. Further, significant decreasing trend of river discharge for the winter season at Batote station from 2000 to 2011 and decreasing trends in the maximum, minimum and mean air temperature at Batote and Reasi stations are also consistent with the increasing trend of SCA%. At both Batote and Reasi stations, mean SCA% exhibited significant negative relationship with the mean air temperature. On the other hand, LESCA exhibited positive correlation with the mean air temperature except in a few months, where negative relationship was seen. Sensitivity analysis of river discharge to SCA exhibited very low values of sensitivity coefficient in most of the months, indicating less sensitivity of river discharge to SCA. On the other hand, sensitivity coefficient of SCA to air temperature exhibited comparatively higher values which indicate SCA is more sensitive to air temperature.

Remote monitoring of sediment dynamics in a coastal lagoon: Long-term spatio-temporal variability of suspended sediment in Chilika

We present a comprehensive analysis of sediment dynamics in a coastal lagoon by synthesizing various remote sensing datasets. The goal of the study was to monitor and analyze the spatio-temporal variability of total suspended sediment (TSS) concentration and associated environmental forcings in Chilika Lagoon, the largest brackish water lagoon in Asia. Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance cloud free data was used to calibrate a TSS model. MODIS daily 250 m surface reflectance (MOD09GQ) and 8-day composite products (MOD09Q1) were chosen because they are atmospherically corrected and available for free thus making them widely applicable for frequent monitoring of environmental phenomena. Three variants of Miller and McKee (2004) TSS model were recalibrated to establish the relationship between in situ TSS and surface reflectance value in band 1 (Rrs at 645 nm). A significant relationship (R2 = 0.91; n = 54; p < 0.001) was obtained between in situ TSS and MODIS Rrs (645 nm) using a polynomial model. The other two models, exponential and linear, showed comparatively low R2 (0.77 and 0.73 respectively). Accuracy of the models were assessed by comparing the field measured TSS with MODIS derived TSS. Based on R2 values, validation analysis (RMSE = 2.64 mg/L), and residual trend, the polynomial model was found to be the best performing TSS model with an estimation range of 6.5 mg/L – 200 mg/L. The model was then implemented to derive weekly time-series TSS maps of Chilika Lagoon for 14 years (2001–2014). Marked seasonal and inter-annual variations in TSS distribution were observed in different sectors (northern, central and southern) of the lagoon. It was found that the TSS variability is primarily driven by three factors: monsoon effect (precipitation and runoff), wind-driven bottom re-suspension, and river discharge into the lagoon. Further analysis of the relationship between MODIS derived time-series TSS and meteorological parameters revealed that the variability of TSS in the northern sector is highly correlated with precipitation and runoff, whereas, TSS dynamics in southern sector is mainly governed by wind induced re-suspension events. The applicability of the TSS model was demonstrated by analyzing the impact of a recent category-5 cyclone, Phailin, which showed a significant increase in TSS concentration in the lagoon. This is the first of its kind study to comprehensively examine the long term TSS dynamics of Chilika, moreover, the model developed in this research was implemented in various coastal lagoons and estuaries around the world and was proven effective in quantifying TSS.

Estimating the effective spatial resolution of the operational BRDF, albedo, and nadir reflectance products from MODIS and VIIRS

Abstract Satellite derived surface albedo and view-angle corrected surface reflectance products serve as the key inputs for an array of climate, biogeochemical, and hydrologic modeling efforts. This research effort is particularly focused on establishing the effective spatial resolution of the global MODIS and VIIRS Nadir BRDF-Adjusted Reflectance (NBAR) and Albedo products. The standard MODIS Products (MCD43) are created by fitting a kernel-driven, semi-empirical BRDF model to multi-date, multi-angular surface reflectance data to establish the surface reflectance anisotropy of a location. Emphasis on a particular date within the rolling multi-date period has resulted in a daily product reported on a 500 m Sinusoidal grid tiling system. While anecdotal and theoretical experiences have suggested that this product would be representative of a larger surface area than the 500 m grid, this research both quantifies that effect, and verifies that the spatial effective resolution is consistently less than 1 km for MODIS. Results for 500 m VIIRS NBAR product show an improvement of approximately 250 m in spatial effective resolution along the scan direction. In addition to their use in modeling, the MODIS BRDF/Albedo/NBAR products (and into the future with the analogous VIIRS products) are increasingly being relied upon to monitor vegetation phenology, identify land cover and land cover disturbance, track snow fall and melt, and establish surface energy balance variability. Thus, this research provides the quantification both for MODIS and VIIRS necessary for the effective use of these products by the global modeling and monitoring communities.

Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images

Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as urban and sandy areas. Land Surface Reflectance (LSR) is the key parameter that must be estimated accurately. Most current methods used to estimate AOD are applicable only in areas with low reflectance. It has historically been difficult to estimate the LSR for bright surfaces because of their complex structure and high reflectance. This paper provides a method for estimating LSR for AOD retrieval in bright areas, and the method is applied to AOD retrieval for Landsat 8 Operational Land Imager (OLI) images at 500 m spatial resolution. A LSR database was constructed with the MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09A1), and this database was also used to estimate the LSR of Landsat 8 OLI images. The AOD retrieved from the Landsat 8 OLI images was validated using the AOD measurements from four AErosol RObotic NETwork (AERONET) stations located in areas with bright surfaces. The MODIS AOD product (MOD04) was also compared with the retrieved AOD. The results demonstrate that the AOD retrieved with the new algorithm is highly consistent with the AOD derived from ground measurements, and its precision is better than that of MOD04 AOD products over bright areas.

Development of Dense Time Series 30-m Image Products from the Chinese HJ-1A/B Constellation: A Case Study in Zoige Plateau, China

Time series remote sensing products with both fine spatial and dense temporal resolutions are urgently needed for many earth system studies. The development of small satellite constellations with identical sensors affords novel opportunities to provide such kind of earth observations. In this paper, a new dense time series 30-m image product was proposed respectively based on an 8-day, 16-day and monthly composition. The products were composited by the Charge Coupled Device (CCD) images from the 2-day revisit small satellite constellation for environmental monitoring and disaster mitigation of China (HJ-1A/B). Taking the Zoige plateau in China as a case area where it is covered by highly heterogeneous vegetation landscapes, a detailed methodology was introduced on how to use 183 scenes of CCD images in 2010 to create composite products. The quality of the HJ CCD composites was evaluated by inter-comparison with the monthly 30-m global Web-Enabled Landsat Data (WELD), 16-day 500-m MODIS NDVI, and 8-day 500-m MODIS surface reflectance products. Results showed that the radiometric consistency between HJ and WELD composited Top Of Atmosphere (TOA) reflectance was in good agreement except for May, June, July and August when more clouds and invalid data gaps appeared in WELD. Visual assessment and temporal profile analysis also revealed that HJ possessed better visual effects and temporal coherence than that of WELD. The comparison between HJ and MODIS products indicated that HJ composites were radiometrically consistent with MODIS products over areas consisting of large patches of homogeneous surface types, but can better reflect the detailed spatial differences in regions with heterogeneous landscapes. This paper highlights the potential of compositing HJ-1A/B CCD images, allowing for providing a cloud free, time-space consistent, 30-m spatial resolution, and dense in time series image product. Meanwhile, the proposed products could also be treated as a reference to generate regional or even global composited products for the on-going satellite constellations and even for the forthcoming satellite missions such as Sentinel-2A/B.

Characterisation of spatial patterns of regional paddy rice with time series remotely sensed data

Remote sensing has facilitated the identification of acreage and spatial distribution of field crops with obvious seasonal dynamics. The primary objective of this study is to identify the spatial patterns of double-season early rice, single-season middle rice and double-season late rice of Hunan Province and Yuanjiang City, China, in 2010, using two kinds of time series remotely sensed imagery: 8-day composite MODIS surface reflectance product data (MOD09A1) and HJ-1A/B satellite images with a 4-day revisit period. The available MODIS and HJ-1 CCD images of transplanting and heading stages were first assured in accordance with the schedules of local traditional paddy fields tillage. Based on the MOD09A1 data product, time series normalised difference vegetation index (NDVI) images were calculated and smoothed to remove the noise and the atmospheric effects. Second, the spatial distribution and planting acreage of three types of rice of Hunan Province were derived from combining the enhanced vegetation index and land surface water index according to the water background and variation characteristics of NDVI values at transplanting and heading stages. Conversely, a two-test procedure was used to finish the identification of paddy rice in Yuanjiang City using time series HJ-CCD imagery. The first test was to derive the potential rice pixels using the NDVI image (∆NDVI) between heading and transplanting stages, and the second test was to remove the non-rice pixels using maximum likelihood supervised classification. The experimental results showed that three types of rice were mainly distributed along the Dongting Lake Basin in Hunan Province and the relative errors were −10.99, 1.46 and −5.87 %, respectively, while they were primarily planted in the northern plains of Dongting Lake in Yuanjiang City, where the relative errors were 12.1, 16.7 and 0.8 %, respectively. We conclude that transplanting and heading stages are the best phenological combinations for identifying paddy rice through remote sensing time series analysis, and this study can provide a basis for evaluating paddy rice yield on a large scale .

Reconstructing Turbidity in a Glacially Influenced Lake Using the Landsat TM and ETM+ Surface Reflectance Climate Data Record Archive, Lake Clark, Alaska

Lake Clark is an important nursery lake for sockeye salmon (Oncorhynchus nerka) in the headwaters of Bristol Bay, Alaska, the most productive wild salmon fishery in the world. Reductions in water clarity within Alaska lake systems as a result of increased glacial runoff have been shown to reduce salmon production via reduced abundance of zooplankton and macroinvertebrates. In this study, we reconstruct long-term, lake-wide water clarity for Lake Clark using the Landsat TM and ETM+ surface reflectance products (1985–2014) and in situ water clarity data collected between 2009 and 2013. Analysis of a Landsat scene acquired in 2009, coincident with in situ measurements in the lake, and uncertainty analysis with four scenes acquired within two weeks of field data collection showed that Band 3 surface reflectance was the best indicator of turbidity (r2 = 0.55, RMSE &lt;&lt; 0.01). We then processed 151 (98 partial- and 53 whole-lake) Landsat scenes using this relation and detected no significant long-term trend in mean turbidity for Lake Clark between 1991 and 2014. We did, however, detect interannual variation that exhibited a non-significant (r2 = 0.20) but positive correlation (r = 0.20) with regional mean summer air temperature and found the month of May exhibited a significant positive trend (r2 = 0.68, p = 0.02) in turbidity between 2000 and 2014. This study demonstrates the utility of hindcasting turbidity in a glacially influenced lake using the Landsat surface reflectance products. It may also help land and resource managers reconstruct turbidity records for lakes that lack in situ monitoring, and may be useful in predicting future water clarity conditions based on projected climate scenarios.

Reconstruction of Satellite-Retrieved Land-Surface Reflectance Based on Temporally-Continuous Vegetation Indices

Land-surface reflectance, estimated from satellite observations through atmospheric corrections, is an essential parameter for further retrieval of various high level land-surface parameters, such as leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface albedo. Although great efforts have been made, land-surface reflectance products still contain considerable noise caused by, e.g., cloud or mixed-cloud pixels, which results in temporal and spatial inconsistencies in subsequent downstream products. In this study, a new method is developed to remove the residual clouds in the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface reflectance product and reconstruct time series of surface reflectance for the red, near infrared (NIR), and shortwave infrared (SWIR) bands. A smoothing method is introduced to calculate upper envelopes of vegetation indices (VIs) from the surface reflectance data and the cloud contaminated reflectance data are identified using the time series VIs and the upper envelopes of the time series VIs. Surface reflectance was then reconstructed according to cloud-free surface reflectance by incorporating the upper envelopes of the time series VIs as constraint conditions. The method was applied to reconstruct time series of surface reflectance from MODIS/TERRA surface reflectance product (MOD09A1). Temporal consistency analysis indicates that the new method can reconstruct temporally-continuous time series of land-surface reflectance. Comparisons with cloud-free MODIS/AQUA surface reflectance product (MYD09A1) over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) sites in 2003 demonstrate that the new method provides better performance for the red band (R2 = 0.8606 and RMSE = 0.0366) and NIR band (R2 = 0.6934 and RMSE = 0.0519), than the time series cloud detection (TSCD) algorithm (R2 = 0.5811 and RMSE = 0.0649; and R2 = 0.5005 and RMSE = 0.0675, respectively).

Coupled Ground- and Space-Based Assessment of Regional Inundation Dynamics to Assess Impact of Local and Upstream Changes on Evaporation in Tropical Wetlands

Modifications of human land use and climate change are known to be a threat for the health and proper functioning of tropical wetlands. They interfere with the seasonal flood pulse, which is seen as the most important driver for biodiversity and directly controls evaporation. In order to investigate the impact of local and upstream changes on wetlands, a regional assessment of evaporation is crucial but challenging in such often remote and poorly gauged ecosystems. Evaporation is the major water balance component of these wetlands and links the flood pulse with the ecosystem. It can therefore be seen as a proxy for their functioning. In the last decades, information from space became an important data source to assess remote wetland areas. Here, we developed a new approach to quantify regional evaporation driven by inundation dynamics as its dominant control. We used three water and vegetation indices (mNDWI (modified Normalized Difference Water Index), NDVI (Normalized Difference Vegetation Index), and EVI (Enhanced Vegetation Index)) from MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance products to assess regional inundation dynamics between the dry and wet seasons. Two years of continual in situ water level measurements at different locations in our study area, the Pantanal wetland of South America, provided the reference to evaluate our method. With process-based modeling that used the inundation dynamics to determine the water available for evaporation, we were able to estimate actual evaporation (AET) on a regional scale. Relating AET to changes in discharge due to upstream flow modifications and on local precipitation over the last 13 years, we found that the Pantanal is more vulnerable to alternated inundation dynamics than to changes in local precipitation. We concluded that coupling ground- and space-based information in this remote wetland area is a valuable first step to investigate the status of the Pantanal ecosystem.

Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring

Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series’ suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001–2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation.

Efficient and accurate atmospheric correction of high-resolution patches over heterogeneous terrain: applications to surface reflectance and vegetation index products

A new method for atmospheric correction of high-resolution patches over heterogeneous terrain is presented. This highly efficient method allows for the correction of high-resolution surface pressure variations of a patch surrounding an Aeronet site in heterogeneous terrain. The method efficiency stems from the smoothness of the functions used in the atmospheric correction with surface pressure. The smoothness of these functions is exploited to decouple the high-resolution variation of elevation/pressure in the atmospheric correction process, resulting in very few radiative transfer code evaluations independent of the number of high-resolution pixels in the patch. The method allows pressure correction at every point of a high-resolution scene, decreasing the errors in heterogeneous terrains by up to two orders of magnitude for the visible bands and vegetation indices NDVI and EVI. This technique can be applied for calibration and validation of surface reflectance and vegetation index products to provide much greater volume of data for performance evaluation.