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1.
Government and NGO funded conservation programs are being implemented in developing countries with the potential benefit of reduced sediment inflow into fresh water lakes. However, these claims are difficult to verify due to limited historical sediment concentration data in lakes and rivers. Remote sensing can potentially aid in monitoring sediment concentration. With almost daily availability over the past ten years and consistent atmospheric correction applied to the images, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter images are potential resources capable of monitoring future concentrations and reconstructing historical sediment concentration records. In this paper, site-specific relationships are developed between reflectance in near-infrared (NIR) images and three factors: total suspended solids (TSS), turbidity and Secchi depth for Lake Tana near the mouth of the Gumara River. The first two sampling campaigns on November 27, 2010 and May 13, 2011 are used in calibration. Reflectance in the NIR varies linearly with turbidity (R2 = 0.89) and TSS (R2 = 0.95). Secchi depth fit best to an exponential relation with R2 of 0.74. The relationships are validated using a third sample set collected on November 7, 2011 with RMSE of 11 Nephelometric Turbidity Units (NTU) for Turbidity, 16.5 mg l−1 for TSS and 0.12 meters for Secchi depth. The MAE was 10% for TSS, 14% for turbidity and 0.1% for Secchi depth. Using the relationship for TSS, a 10-year time series of sediment concentration in Lake Tana near the Gumara River was plotted. It was found that after the severe drought of 2002 and 2003 the concentration in the lake increased significantly. The results showed that MODIS images are potential cost effective tools to monitor suspended sediment concentration and obtain a past history of concentration for evaluating the effect of best management practices. 相似文献
2.
Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92%) and a robust usability of MODIS data to predict water fraction (R2 = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies. 相似文献
3.
Inventories of mixed broad-leaved forests of Iran mainly rely on terrestrial measurements. Due to rapid changes and disturbances and great complexity of the silvicultural systems of these multilayer forests, frequent repetition of conventional ground-based plot surveys is often cost prohibitive. Airborne laser scanning (ALS) and multispectral data offer an alternative or supplement to conventional inventories in the Hyrcanian forests of Iran. In this study, the capability of a combination of ALS and UltraCam-D data to model stand volume, tree density, and basal area using random forest (RF) algorithm was evaluated. Systematic sampling was applied to collect field plot data on a 150 m × 200 m sampling grid within a 1100 ha study area located at 36°38′- 36°42′N and 54°24′–54°25′E. A total of 308 circular plots (0.1 ha) were measured for calculation of stand volume, tree density, and basal area per hectare. For each plot, a set of variables was extracted from both ALS and multispectral data. The RF algorithm was used for modeling of the biophysical properties using ALS and UltraCam-D data separately and combined. The results showed that combining the ALS data and UltraCam-D images provided a slight increase in prediction accuracy compared to separate modeling. The RMSE as percentage of the mean, the mean difference between observed and predicted values, and standard deviation of the differences using a combination of ALS data and UltraCam-D images in an independent validation at 0.1-ha plot level were 31.7%, 1.1%, and 84 m3 ha−1 for stand volume; 27.2%, 0.86%, and 6.5 m2 ha−1 for basal area, and 35.8%, −4.6%, and 77.9 n ha−1 for tree density, respectively. Based on the results, we conclude that fusion of ALS and UltraCam-D data may be useful for modeling of stand volume, basal area, and tree density and thus gain insights into structural characteristics in the complex Hyrcanian forests. 相似文献
4.
With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images. 相似文献
5.
Moderate Resolution Imaging Spectroradiometer (MODIS) imagery provides a good data source for timely and accurate monitoring of dust storms. However, effective MODIS-based dust indices are inadequate. In this study, we proposed an improved brightness temperature adjusted dust index (BADI) by integrating the brightness temperatures of three thermal infrared MODIS bands: band20 (3.66–3.84 μm), band31 (10.78–11.28 μm) and band32 (11.77–12.27 μm). We used the BADI to monitor several representative dust storms over the Northeast Asia between 2000 and 2011. When compared to commonly used MODIS-based dust indices, such as the brightness temperature difference index in band32 and band31 (BTD32-31) and the normalized difference dust index (NDDI), the BADI captured the spatial extent and density of dust storms more accurately. The BADI detected dust storm extent with an overall accuracy >90%, which was 7% and 29% higher than the results derived from BTD32-31 and NDDI, respectively. The BADI also demonstrated good agreement with the density indicator of MODIS Deep Blue Aerosol Optical Depth (R2 = 0.59, P < 0.01). We suggest that the BADI is an effective tool to monitor large-scale dust storms. 相似文献
6.
Research on surface water temperature (SWT) variations in large lakes over the Qinghai–Tibet Plateau (QTP) has been limited by lack of in situ measurements. By taking advantage of the increased availability of remotely sensed observations, this study investigated SWT variation of Siling Co in central QTP by processing complete MODIS Land surface temperature (LST) images over the lake covering from 2001 to 2013. The temporal (diurnal, intra-annul and inter-annul) variations of Siling Co SWT as well as the spatial patterns were analyzed. The results show that on average from late December to mid-April the lake is in a mixing state of water and ice and drastic diurnal temperature differences occur, especially along the shallow shoreline areas. The extent of spatial variations in monthly SWT ranges from 1.25 °C to 3.5 °C, and particularly large at nighttime and in winter months. The spatial patterns of annual average SWT were likely impacted by the cooling effect of river inflow from the west and east side of the lake. The annual cycle of spatial pattern of SWT is characterized by seasonal reversions between the shallow littoral regions and deep parts due to different heat capacity. Compared to the deep regions, the littoral shallow shoreline areas warms up quickly in spring and summer, and cool down drastically in autumn and winter, showing large diurnal and seasonal variation amplitudes of SWT. Two cold belt zones in the western and eastern side of the lake and warm patches along the southwestern and northeastern shorelines are shaped by the combined effects of the lakebed topography and river runoff. Overall, the lake-averaged SWT increased at a rate of 0.26 °C/decade during 2001–2013. Faster increase of temperature was found at nighttime (0.34 °C/decade) and in winter and spring, consistent with the asymmetric warming pattern over land areas reported in prior studies. The rate of temperature increase over Siling Co is remarkably lower than that over Bangoin station, which is probably attributable to the large heat capacity of water and partly reflects the sensitive of alpine saltwater lake to climate change. 相似文献
7.
Vegetation indices derived from satellite image time series have been extensively used to estimate the timing of phenological events like season onset. Medium spatial resolution (≥250 m) satellite sensors with daily revisit capability are typically employed for this purpose. In recent years, phenology is being retrieved at higher resolution (≤30 m) in response to increasing availability of high-resolution satellite data. To overcome the reduced acquisition frequency of such data, previous attempts involved fusion between high- and medium-resolution data, or combinations of multi-year acquisitions in a single phenological reconstruction. The objectives of this study are to demonstrate that phenological parameters can now be retrieved from single-season high-resolution time series, and to compare these retrievals against those derived from multi-year high-resolution and single-season medium-resolution satellite data. The study focuses on the island of Schiermonnikoog, the Netherlands, which comprises a highly-dynamic saltmarsh, dune vegetation, and agricultural land. Combining NDVI series derived from atmospherically-corrected images from RapidEye (5 m-resolution) and the SPOT5 Take5 experiment (10m-resolution) acquired between March and August 2015, phenological parameters were estimated using a function fitting approach. We then compared results with phenology retrieved from four years of 30 m Landsat 8 OLI data, and single-year 100 m Proba-V and 250 m MODIS temporal composites of the same period. Retrieved phenological parameters from combined RapidEye/SPOT5 displayed spatially consistent results and a large spatial variability, providing complementary information to existing vegetation community maps. Retrievals that combined four years of Landsat observations into a single synthetic year were affected by the inclusion of years with warmer spring temperatures, whereas adjustment of the average phenology to 2015 observations was only feasible for a few pixels due to cloud cover around phenological transition dates. The Proba-V and MODIS phenology retrievals scaled poorly relative to their high-resolution equivalents, indicating that medium-resolution phenology retrievals need to be interpreted with care, particularly in landscapes with fine-scale land cover variability. 相似文献
8.
In this study medium resolution remote sensing data of the AVHRR and MODIS sensors were used for derivation of inland water bodies extents over a period from 1986 till 2012 for the region of Central Asia. Daily near-infrared (NIR) spectra from the AVHRR sensor with 1.1 km spatial resolution and 8-day NIR composites from the MODIS sensor with 250 m spatial resolution for the months April, July and September were used as input data. The methodological approach uses temporal dynamic thresholds for individual data sets, which allows detection of water pixel independent from differing conditions or sensor differences. The individual results are summed up and combined to monthly composites of areal extent of water bodies. The presented water masks for the months April, July, and September were chosen to detect seasonal patterns as well as inter-annual dynamics and show diverse behaviour of static, decreasing, or dynamic water bodies in the study region. The size of the Southern Aral Sea, as the most popular example for an ecologic catastrophe, is decreasing significantly throughout all seasons (R2 0.96 for April; 0.97 for July; 0.96 for September). Same is true for shallow natural lakes in the northern Kazakhstan, exemplary the Tengiz-Korgalzhyn lake system, which have been shrinking in the last two decades due to drier conditions (R2 0.91 for July; 0.90 for September). On the contrary, water reservoirs show high seasonality and are very dynamic within one year in their areal extent with maximum before growing season and minimum after growing season. Furthermore, there are water bodies such as Alakol-Sasykol lake system and natural mountainous lakes which have been stable in their areal extent throughout the entire time period. Validation was performed based on several Landsat images with 30 m resolution and reveals an overall accuracy of 83% for AVHRR and 91% for MODIS monthly water masks. The results should assist for climatological and ecological studies, land and water management, and as input data for different modelling applications. 相似文献
9.
As a preparatory study for future hyperspectral missions that can measure canopy chemistry, we introduce a novel approach to investigate whether multi-angle Moderate Resolution Imaging Spectroradiometer (MODIS) data can be used to generate a preliminary database with long-term estimates of chlorophyll. MODIS monthly chlorophyll estimates between 2000 and 2015, derived from a fully coupled canopy reflectance model (ProSAIL), were inspected for consistency with eddy covariance fluxes, tower-based hyperspectral images and chlorophyll measurements. MODIS chlorophyll estimates from the inverse model showed strong seasonal variations across two flux-tower sites in central and eastern Amazon. Marked increases in chlorophyll concentrations were observed during the early dry season. Remotely sensed chlorophyll concentrations were correlated to field measurements (r2 = 0.73 and r2 = 0.98) but the data deviated from the 1:1 line with root mean square errors (RMSE) ranging from 0.355 μg cm−2 (Tapajós tower) to 0.470 μg cm−2 (Manaus tower). The chlorophyll estimates were consistent with flux tower measurements of photosynthetically active radiation (PAR) and net ecosystem productivity (NEP). We also applied ProSAIL to mono-angle hyperspectral observations from a camera installed on a tower to scale modeled chlorophyll pigments to MODIS observations (r2 = 0.73). Chlorophyll pigment concentrations (ChlA+B) were correlated to changes in the amount of young and mature leaf area per month (0.59 ≤ r2 ≤ 0.64). Increases in MODIS observed ChlA+B were preceded by increased PAR during the dry season (0.61 ≤ r2 ≤ 0.62) and followed by changes in net carbon uptake. We conclude that, at these two sites, changes in LAI, coupled with changes in leaf chlorophyll, are comparable with seasonality of plant productivity. Our results allowed the preliminary development of a 15-year time series of chlorophyll estimates over the Amazon to support canopy chemistry studies using future hyperspectral sensors. 相似文献
10.
Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces. 相似文献
11.
Image composites are often used for earth surface phenomena studies at regional or national level. The compromise between residual clouds and the length of compositing period is a necessary corollary to the choice of satellite optical data for monitoring earth surface phenomena dynamics. This paper introduced a methodology for estimating availability of cloud-free image composites for optical sensors with various revisiting intervals, using MODIS MOD06 L2 cloud fraction product in the period of 2000–2008. The methodology starts with downscaling of the cloud fraction product to 1 km × 1 km cloud cover binary images. The binary images are then used for the exploration of spatial and temporal characteristics of cloud dynamics, and subsequently for the simulation of cloud-free composite availability with various revisiting intervals of optical sensors. Using Canada's southern provinces as an application case, the study explored several factors important for the design of environmental monitoring system using optical sensors of earth observation, in particular, cloud dynamics and its inter-annual variability, sensors’ revisiting intervals, and cloud-free threshold for targeting composites. While the cloud images used in the analysis are at 1 km × 1 km resolution, our analysis suggests that the simulated availabilities of cloud-free image composites may also provide reasonable estimates for optical sensors with higher than 1 km × 1 km resolution, though the closer to 1 km × 1 km resolution the optical sensor, the more pertinent the application. Also, the methodology can be parameterised to different temporal period and different spatial region, depending on applications. 相似文献
12.
Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77–0.94 compared to 0.01–0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r2 = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions. 相似文献
13.
A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAIL) alone (R2 = 0.77 and RMSE = 231 g m−2). Assimilation of GLAI derived from the fusion dataset (GLAIF) resulted in a R2 of 0.71 and RMSE of 193 g m−2 while assimilating the combination of GLAIL and GLAIF led to further improvements (R2 = 0.76 and RMSE = 176 g m−2). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited. 相似文献
14.
The validation study of leaf area index (LAI) products over rugged surfaces not only gives additional insights into data quality of LAI products, but deepens understanding of uncertainties regarding land surface process models depended on LAI data over complex terrain. This study evaluated the performance of MODIS and GLASS LAI products using the intercomparison and direct validation methods over southwestern China. The spatio-temporal consistencies, such as the spatial distributions of LAI products and their statistical relationship as a function of topographic indices, time, and vegetation types, respectively, were investigated through intercomparison between MODIS and GLASS products during the period 2011–2013. The accuracies and change ranges of these two products were evaluated against available LAI reference maps over 10 sampling regions which standed for typical vegetation types and topographic gradients in southwestern China.The results show that GLASS LAI exhibits higher percentage of good quality data (i.e. successful retrievals) and smoother temporal profiles than MODIS LAI. The percentage of successful retrievals for MODIS and GLASS is vulnerable to topographic indices, especially to relief amplitude. Besides, the two products do not capture seasonal dynamics of crop, especially in spring over heterogeneously hilly regions. The yearly mean LAI differences between MODIS and GLASS are within ±0.5 for 64.70% of the total retrieval pixels over southwestern China. The spatial distribution of mean differences and temporal profiles of these two products are inclined to be dominated by vegetation types other than topographic indices. The spatial and temporal consistency of these two products is good over most area of grasses/cereal crops; however, it is poor for evergreen broadleaf forest. MODIS presents more reliable change range of LAI than GLASS through comparison with fine resolution reference maps over most of sampling regions. The accuracies of direct validation are obtained for GLASS LAI (r = 0.35, RMSE = 1.72, mean bias = −0.71) and MODIS LAI (r = 0.49, RMSE = 1.75, mean bias = −0.67). GLASS performs similarly to MODIS, but may be marginally inferior to MODIS based on our direct validation results. The validation experience demonstrates the necessity and importance of topographic consideration for LAI estimation over mountain areas. Considerable attention will be paid to the improvements of surface reflectance, retrieval algorithm and land cover types so as to enhance the quality of LAI products in topographically complex terrain. 相似文献
15.
16.
A new approach to estimate soil moisture (SM) based on evaporative fraction (EF) retrieved from optical/thermal infrared MODIS data is presented for Canadian Prairies in parts of Saskatchewan and Alberta. An EF model using the remotely sensed land surface temperature (Ts)/vegetation index concept was modified by incorporating North American Regional Reanalysis (NAAR) Ta data and used for SM estimation. Two different combinations of temperature and vegetation fraction using the difference between Ts from MODIS Aqua and Terra images and Ta from NARR data (Ts−Ta Aqua-day and Ts−Ta Terra-day, respectively) were proposed and the results were compared with those obtained from a previously improved model (ΔTs Aqua-DayNight) as a reference. For the estimation of SM from EF, two empirical models were tested and discussed to find the most appropriate model for converting MODIS-derived EF data to SM values. Estimated SM values were then correlated with in situ SM measurements and their relationships were statistically analyzed. Results indicated statistically significant correlations between SM estimated from all three EF estimation approaches and field measured SM values (R2 = 0.42–0.77, p values < 0.04) exhibiting the possibility to estimate SM from remotely sensed EF models. The proposed Ts−Ta MODIS Aqua-day and Terra-day approaches resulted in better estimations of SM (on average higher R2 values and similar RMSEs) as compared with the ΔTs reference approach indicating that the concept of incorporating NARR Ta data into Ts/Vegetation index model improved soil moisture estimation accuracy based on evaporative fraction. The accuracies of the predictions were found to be considerably better for intermediate SM values (from 12 to 22 vol/vol%) with square errors averaging below 11 (vol/vol%)2. This indicates that the model needs further improvements to account for extreme soil moisture conditions. The findings of this research can be potentially used to downscale SM estimations obtained from passive microwave remote sensing techniques. 相似文献
17.
Clumping index quantifies the level of foliage aggregation, relative to a random distribution, and is a key structural parameter of plant canopies and is widely used in ecological and meteorological models. In this study, the inter- and intra-annual variations in clumping index values, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF product, are investigated at six forest sites, including conifer forests, a mixed deciduous forest and an oak-savanna system. We find that the clumping index displays large seasonal variation, particularly for the deciduous sites, with the magnitude in clumping index values at each site comparable on an intra-annual basis, and the seasonality of clumping index well captured after noise removal. For broadleaved and mixed forest sites, minimum clumping index values are usually found during the season when leaf area index is at its maximum. The magnitude of MODIS clumping index is validated by ground data collected from 17 sites. Validation shows that the MODIS clumping index can explain 75% of variance in measured values (bias = 0.03 and rmse = 0.08), although with a narrower amplitude in variation. This study suggests that the MODIS BRDF product has the potential to produce good seasonal trajectories of clumping index values, but with an improved estimation of background reflectance. 相似文献
18.
Remote sensing-based timber volume estimation is key for modelling the regional potential, accessibility and price of lignocellulosic raw material for an emerging bioeconomy. We used a unique wall-to-wall airborne LiDAR dataset and Landsat 7 satellite images in combination with terrestrial inventory data derived from the National Forest Inventory (NFI), and applied generalized additive models (GAM) to estimate spatially explicit timber distribution and volume in forested areas. Since the NFI data showed an underlying structure regarding size and ownership, we additionally constructed a socio-economic predictor to enhance the accuracy of the analysis. Furthermore, we balanced the training dataset with a bootstrap method to achieve unbiased regression weights for interpolating timber volume. Finally, we compared and discussed the model performance of the original approach (r2 = 0.56, NRMSE = 9.65%), the approach with balanced training data (r2 = 0.69, NRMSE = 12.43%) and the final approach with balanced training data and the additional socio-economic predictor (r2 = 0.72, NRMSE = 12.17%). The results demonstrate the usefulness of remote sensing techniques for mapping timber volume for a future lignocellulose-based bioeconomy. 相似文献
19.
There are increasing societal and plant industry demands for more accurate, objective and near real-time crop production information to meet both economic and food security concerns. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to monitor large agricultural areas at acceptable pixel scale, cost and accuracy. Fitting parametric profiles to growing season vegetation index time series reduces the volume of data and provides simple quantitative parameters that relates to crop phenology (sowing date, flowering). In this study, we modelled various Gaussian profiles to time sequential MODIS enhanced vegetation index (EVI) images over winter crops in Queensland, Australia. Three simple Gaussian models were evaluated in their effectiveness to identify and classify various winter crop types and coverage at both pixel and regional scales across Queensland's main agricultural areas. Equal to or greater than 93% classification accuracies were obtained in determining crop acreage estimates at pixel scale for each of the Gaussian modelled approaches. Significant high to moderate correlations (log-linear transformation) were also obtained for determining total winter crop (R2 = 0.93) areas as well as specific crop acreage for wheat (R2 = 0.86) and barley (R2 = 0.83). Conversely, it was much more difficult to predict chickpea acreage (R2 ≤ 0.26), mainly due to very large uncertainties in survey data. The quantitative approach utilised here further had additional benefits of characterising crop phenology in terms of length of growing season and providing regression diagnostics of how well the fitted profiles matched the EVI time series. The Gaussian curve models utilised here are novel in application and therefore will enhance the use and adoption of remote sensing technologies in targeted agricultural application. With innate simplicity and accuracies comparable to other more convoluted multi-temporal approaches it is a good candidate in determining total and specific crop acreage estimates in future national and global food security frameworks. 相似文献
20.
Suspended particulate matter (SPM) is a key parameter describing water quality, and developing the retrieval model of SPM concentration (CSPM) is fundamental for obtaining the spatiotemporal information of CSPM and further for understanding, managing and protecting aquatic ecosystems. This study aimed to compare moderate resolution imaging spectroradiometer (MODIS)-based CSPM retrieval models in order to find the optimal model for improving the CSPM estimation in Poyang Lake. The CSPM measurements on 27 September 2007 and their coincident MODIS Terra image were used to calibrate retrieval models with the least-squares technique. The CSPM measurements on 31 August 2012 and the MODIS Terra image on 30 August 2012 were applied to validate the calibrated models, and the correlation coefficient (r) between the measured and estimated CSPM values, the root mean square error (RMSE) and relative root mean square error (RRMSE) of estimation as well as the model bias evaluation result were compared to determine the optimal model for estimating the CSPM values of Poyang Lake from MODIS images. Model calibration showed that, after two samples were removed, the exponential models of blue, green and red band, the linear model of infrared band, the cubic model of red band as well as the exponential model of red minus infrared band explained about 92%, 88%, 90%, 89%, 90% and 76% of the variation of CSPM, respectively; while model validation indicated that, after removing two samples, the exponential models of blue and green band got biased CSPM estimations, the agreement between the measured and estimated CSPM values was not very high (r = <0.8) for the models with single red and infrared band, and the exponential model of red minus infrared band got the best result among all calibrated models (r = 0.87, RMSE = 22.1 mg/l, RRMSE = 52.8%). We concluded that the exponential model of red minus infrared band obtained stable CSPM estimation and was the optimal model for CSPM estimation in this study, and more independent datasets should be obtained to further validate our finding for improving the CSPM estimation in Poyang Lake. 相似文献