Search Results (14)

Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product. Full article

HJ-1 charge-coupled device (CCD) data with high temporal and medium spatial resolution are widely used in environmental and disaster monitoring in China. However, due to bad weather, it is difficult to obtain sufficient time-continuous HJ-1 CCD data for environmental monitoring. In this study, the mountain valley with farmland and forestland in North China is selected as the experimental area, and HJ-1 CCD and moderate resolution imaging spectroradiometer (MODIS) data are used in the case study. An improved method of fusing data and inversing surface reflectivity is presented to obtain the HJ-1 inversion network-based application resolution (NBAR) data using linear matching of the Ross Thick-Li Sparse Reciprocal (RTLSR) model, and then predicted reflectivity using the seasonal autoregressive integrated moving average (SARIMA) model. The fusion data have advantages of high spatial and temporal resolution, as well as meeting the requirements of high quality and quantity of small-scale regional data. This case study provides a feasibility method for the HJ-1 satellites to produce the secondary products for small-scale remote sensing ground surface research. It also provides a reference for dynamic information acquisition and application of small satellite data, contributing to the improvement in RS estimation of surface environment variables. Full article

Land Surface Phenology is an important characteristic of vegetation, which can be informative of its response to climate change. However, satellite-based identification of vegetation transition dates is hindered by inconsistencies in different observation platforms, including band settings, viewing angles, and scale effects. Therefore, time-series data with high consistency are necessary for monitoring vegetation phenology. This study proposes a data harmonization approach that involves band conversion and bidirectional reflectance distribution function (BRDF) correction to create normalized reflectance from Landsat-8, Sentinel-2A, and Gaofen-1 (GF-1) satellite data, characterized by the same spectral and illumination-viewing angles as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Nadir BRDF Adjusted Reflectance (NBAR). The harmonized data are then subjected to the spatial and temporal adaptive reflectance fusion model (STARFM) to produce time-series data with high spatio–temporal resolution. Finally, the transition date of typical vegetation was estimated using regular 30 m spatial resolution data. The results show that the data harmonization method proposed in this study assists in improving the consistency of different observations under different viewing angles. The fusion result of STARFM was improved after eliminating differences in the input data, and the accuracy of the remote-sensing-based vegetation transition date was improved by the fused time-series curve with the input of harmonized data. The root mean square error (RMSE) estimation of the vegetation transition date decreased by 9.58 days. We concluded that data harmonization eliminates the viewing-angle effect and is essential for time-series vegetation monitoring through improved data fusion. Full article

Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0°, 15°, 30°, 45°, and 60°) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI/EVI time series. Full article

Time series of vegetation biophysical variables (leaf area index (LAI), fraction canopy cover (FCOVER), fraction of absorbed photosynthetically active radiation (FAPAR), canopy chlorophyll content (CCC), and canopy water content (CWC)) were estimated from interpolated Sentinel-2 (S2-LIKE) surface reflectance images, for an agricultural region located in central Canada, using the Simplified Level 2 Product Prototype Processor (SL2P). S2-LIKE surface reflectance data were generated by blending clear-sky Sentinel-2 Multispectral Imager (S2-MSI) images with daily BRDF-adjusted Moderate Resolution Imaging Spectrometer images using the Prediction Smooth Reflectance Fusion Model (PSFRM), and validated using thirteen independent S2-MSI images (RMSE $\le$ 6%). The uncertainty of S2-LIKE surface reflectance data increases with the time delay between the prediction date and the closest S2-MSI image used for training PSFRM. Vegetation biophysical variables from S2-LIKE products are validated qualitatively and quantitatively by comparison to the corresponding vegetation biophysical variables from S2-MSI products (RMSE = 0.55 for LAI, ~10% for FCOVER and FAPAR, and 0.13 g/m² for CCC and 0.16 kg/m² for CWC). Uncertainties of vegetation biophysical variables derived from S2-LIKE products are almost linearly related to the uncertainty of the input reflectance data. When compared to the in situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 field campaign, uncertainties of LAI (0.83) and FCOVER (13.73%) estimates from S2-LIKE products were slightly larger than uncertainties of LAI (0.57) and FCOVER (11.80%) estimates from S2-MSI products. However, equal uncertainties (0.32 kg/m²) were obtained for CWC estimates using SL2P with either S2-LIKE or S2-MSI input data. Full article

Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m. Full article

Optical wavelength satellite data have directional reflectance effects over non-Lambertian surfaces, described by the bidirectional reflectance distribution function (BRDF). The Sentinel-2 multi-spectral instrument (MSI) acquires data over a 20.6° field of view that have been shown to have non-negligible BRDF effects in the visible, near-infrared, and short wave infrared bands. MSI red-edge BRDF effects have not been investigated. In this study, they are quantified by an examination of 6.6 million (January 2016) and 10.7 million (April 2016) pairs of forward and back scatter reflectance observations extracted over approximately 20° × 10° of southern Africa. Non-negligible MSI red-edge BRDF effects up to 0.08 (reflectance units) across the 290 km wide MSI swath are documented. A recently published MODIS BRDF parameter c-factor approach to adjust MSI visible, near-infrared, and short wave infrared reflectance to nadir BRDF-adjusted reflectance (NBAR) is adapted for application to the MSI red-edge bands. The red-edge band BRDF parameters needed to implement the algorithm are provided. The parameters are derived by a linear wavelength interpolation of fixed global MODIS red and NIR BRDF model parameters. The efficacy of the interpolation is investigated using POLDER red, red-edge, and NIR BRDF model parameters, and is shown to be appropriate for the c-factor NBAR generation approach. After adjustment to NBAR, red-edge MSI BRDF effects were reduced for the January data (acquired close to the solar principal where BRDF effects are maximal) and the April data (acquired close to the orthogonal plane) for all the MSI red-edge bands. Full article

Fractional covers of photosynthetic and non-photosynthetic vegetation are key indicators for land degradation surveillance in the dryland of China. However, there are no available, well validated, and multispectral-based products. Aiming for this, we selected the Beijing and Tianjin Sandstorm Source Region as the study area, and utilized the linear spectral mixture model for generating the fractional cover of PV, NPV, and bare soil, with endmember spectra retrieved from the field measured endmember spectral library, based on the MODIS NBAR data from 2001 to 2015. The unmixing results were validated through comparison with the field samples. The results show the method adopted could acquire rational and accurate estimation of fractional cover of photosynthetic vegetation (R² = 0.6297, RMSE = 0.2443) and non-photosynthetic vegetation (R² = 0.3747, RMSE = 0.2568). The dataset could provide key data support for the users in land degradation surveillance fields. Full article

Remote sensing data from multi-source optical and SAR (Synthetic Aperture Radar) sensors have been widely utilized to detect forest dynamics under a variety of conditions. Due to different temporal coverage, spatial resolution, and spectral characteristics, these sensors usually perform differently from one another. To conduct statistical modeling accuracies evaluation and comparison among several sensors, a linear statistical model was applied in this study for retrieval and comparative analysis based on remote-sensing indices from optical sensors of ALOS AVNIR-2 (Advanced Land Observing Satellite Advanced Visible and Near Infrared Radiometer type 2), Landsat-5 TM (Thematic Mapper), MODIS NBAR (Moderate Resolution Imaging Spectroradiometer Nadir BRDF-Adjusted Reflectance), and the SAR sensor of ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar), respectively. This modeling used the forest leaf area index (LAI) as the field measured variable. During modeling, six optical vegetation indices were selected for evaluation and comparison between the three optical sensors, while simultaneously, two radar indices were calculated for the comparison between ALOS AVNIR-2 and PALSAR sensors. The gap between the spatial resolution of remote-sensing data and field plot size can account for the different accuracies found in this study. This study provides a reference for the selection of remote-sensing data types and spatial resolution in specific forest monitoring applications with different data acquisition costs and accuracy needs. Normally, at regional and national scales, remote sensing data with 30 m spatial resolution (e.g., Landsat) could provide significant results in the statistical modelling and retrieval of LAI while the MODIS cannot always meet the requirements. Full article

Understanding the effects that the Urban Heat Island (UHI) has on plant phenology is important in predicting ecological impacts of expanding cities and the impacts of the projected global warming. However, the underlying methods to monitor phenological events often limit this understanding. Generally, one can either have a small sample of in situ measurements or use satellite data to observe large areas of land surface phenology (LSP). In the latter, a tradeoff exists among platforms with some allowing better temporal resolution to pick up discrete events and others possessing the spatial resolution appropriate for observing heterogeneous landscapes, such as urban areas. To overcome these limitations, we applied the Spatial and Temporal Adaptive Reflectance Model (STARFM) to fuse Landsat surface reflectance and MODIS nadir BRDF-adjusted reflectance (NBAR) data with three separate selection conditions for input data across two versions of the software. From the fused images, we derived a time-series of high temporal and high spatial resolution synthetic Normalized Difference Vegetation Index (NDVI) imagery to identify the dates of the start of the growing season (SOS), end of the season (EOS), and the length of the season (LOS). The results were compared between the urban and exurban developed areas within the vicinity of Ogden, UT and across all three data scenarios. The results generally show an earlier urban SOS, later urban EOS, and longer urban LOS, with variation across the results suggesting that phenological parameters are sensitive to input changes. Although there was strong evidence that STARFM has the potential to produce images capable of capturing the UHI effect on phenology, we recommend that future work refine the proposed methods and compare the results against ground events. Full article

The Upper Guinea Forest (UGF) region of West Africa is one of the most climatically marginal and human-impacted tropical forest regions in the world. Research on the patterns and drivers of vegetation change is critical for developing strategies to sustain ecosystem services in the region and to understand how climate and land use change will affect other tropical forests around the globe. We compared six spectral indices calculated from the 2001–2015 MODIS optical-infrared reflectance data with manually-interpreted measurements of woody vegetation cover from high resolution imagery. The tasseled cap wetness (TCW) index was found to have the strongest association with woody vegetation cover, whereas greenness indices, such as the enhanced vegetation index (EVI), had relatively weak associations with woody cover. Trends in woody vegetation cover measured with the TCW index were analyzed using Mann–Kendall statistics and were contrasted with trends in vegetation greenness measured with EVI. In the drier West Sudanian Savanna and Guinean Forest-Savanna Mosaic ecoregions, EVI trends were primarily positive, and TCW trends were primarily negative, suggesting that woody vegetation cover was decreasing, while herbaceous vegetation cover is increasing. In the wettest tropical forests in the Western Guinean Lowland Forest ecoregion, declining trends in both TCW and EVI were indicative of widespread forest degradation resulting from human activities. Across all ecoregions, declines in woody cover were less prevalent in protected areas where human activities were restricted. Multiple lines of evidence suggested that human land use and resource extraction, rather than climate trends or short-term climatic anomalies, were the predominant drivers of recent vegetation change in the UGF region of West Africa. Full article

Many previous studies have attempted to extract prior reflectance anisotropy knowledge from the historical MODIS Bidirectional Reflectance Distribution Function (BRDF) product based on land cover or Normalized Difference Vegetation Index (NDVI) data. In this study, the feasibility of the method is discussed based on MODIS data and archetypal BRDFs. The BRDF is simplified into six archetypal BRDFs that represent different reflectance anisotropies. Five-year time series of MODIS BRDF data over three tiles are classified into six BRDF archetype classes according to the Anisotropy Flat indeX (AFX). The percentage of each BRDF archetype class in different land cover classes or every 0.1-NDVI interval is determined. Nadir BRDF-Adjusted Reflectances (NBARs) and NDVIs simulated from different archetypal BRDFs and the same multi-angular observations are compared to MODIS results to study the effectiveness of the method. The results show that one land cover type, or every 0.1-NDVI interval, contains all the potential BRDF shapes and that one BRDF archetypal class makes up no more than 40% of all data. Moreover, the differences between the NBARs and NDVIs simulated from different archetypal BRDFs are insignificant. In terms of the archetypal BRDF method and MODIS BRDF product, this study indicates that the land cover or NDVI is not necessarily related to surface reflectance anisotropy. Full article

The monitoring of crop development can benefit from the increased frequency of observation provided by modern geostationary satellites. This paper describes a four-year testing period from 2010 to 2014, during which satellite images from the world's first Geostationary Ocean Color Imager (GOCI) were used for spectral analyses of paddy rice in South Korea. A vegetation index was calculated from GOCI data based on the bidirectional reflectance distribution function (BRDF)-adjusted reflectance, which was then used to visually analyze the seasonal crop dynamics. These vegetation indices were then compared with those calculated using the Moderate-resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) based on Nadir BRDF-adjusted reflectance. The results show clear advantages of GOCI, which provided four times better temporal resolution than the combined MODIS sensors, interpreting subtle characteristics of the vegetation development. Particularly in the rainy season, when data acquisition under clear weather conditions was very limited, it was possible to find cloudless pixels within the study sites by compiling GOCI images obtained from eight acquisition periods per day, from which the vegetation index could be calculated. In this study, ground spectral measurements from CROPSCAN were also compared with satellite-based vegetation products, despite their different index magnitude, according to systematic discrepancy, showing a similar crop development pattern to the GOCI products. Consequently, we conclude that the very high temporal resolution of GOCI is very beneficial for monitoring crop development, and has potential for providing improved information on phenology. Full article

Time series of images are required to extract and separate information on vegetation change due to phenological cycles, inter-annual climatic variability, and long-term trends. While images from the Landsat Thematic Mapper (TM) sensor have the spatial and spectral characteristics suited for mapping a range of vegetation structural and compositional properties, its 16-day revisit period combined with cloud cover problems and seasonally limited latitudinal range, limit the availability of images at intervals and durations suitable for time series analysis of vegetation in many parts of the world. Landsat Image Time Series (LITS) is defined here as a sequence of Landsat TM images with observations from every 16 days for a five-year period, commencing on July 2003, for a Eucalyptus woodland area in Queensland, Australia. Synthetic Landsat TM images were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm for all dates when images were either unavailable or too cloudy. This was done using cloud-free scenes and a MODIS Nadir BRDF Adjusted Reflectance (NBAR) product. The ability of the LITS to measure attributes of vegetation phenology was examined by: (1) assessing the accuracy of predicted image-derived Foliage Projective Cover (FPC) estimates using ground-measured values; and (2) comparing the LITS-generated normalized difference vegetation index (NDVI) and MODIS NDVI (MOD13Q1) time series. The predicted image-derived FPC products (value ranges from 0 to 100%) had an RMSE of 5.6. Comparison between vegetation phenology parameters estimated from LITS-generated NDVI and MODIS NDVI showed no significant difference in trend and less than 16 days (equal to the composite period of the MODIS data used) difference in key seasonal parameters, including start and end of season in most of the cases. In comparison to similar published work, this paper tested the STARFM algorithm in a new (broadleaf) forest environment and also demonstrated that the approach can be used to form a time series of Landsat TM images to study vegetation phenology over a number of years. Full article