Moderate Resolution Imaging Spectroradiometer Bands Research Articles

Evaluation of the monitoring capability of various vegetation indices and mainstream satellite band settings for grassland drought

In the context of global climate change and increasing human activities, grassland drought has become increasingly severe and complex. The monitoring of grassland drought is crucial for reducing drought-related losses and ensuring national ecological security. This study used the coupled PROSPECT and SAIL radiative transfer models (PROSAIL) to simulate canopy reflectance, considering factors such as grassland growth stages and varying drought conditions. Our objective was to reveal the spectral response characteristics of grasslands to varying drought conditions and identify sensitive spectral bands suitable for drought monitoring during different grassland growth stages. We aligned commonly available satellite bands from moderate resolution imaging spectroradiometer (MODIS), Sentinel 2, Landsat 8, WorldView 2, and Gaofen 2 (GF 2) with these sensitive bands to assess the capabilities of existing satellite data for drought monitoring. Furthermore, this research evaluated the suitability of 16 commonly used remote sensing vegetation indices for grassland drought monitoring, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Difference Vegetation Index (DVI), Modified Soil Adjusted Vegetation Index (MSAVI), Atmospherically Resistant Vegetation Index (ARVI), Modified Normalized Difference Water Index (MNDWI), Global Vegetation Moisture Index (GVMI), Land Surface Water Index (LSWI), Visible and Shortwave Infrared Drought Index (VSDI), Water index(WI), Moisture Stress Index(MSI), Normalized Difference Water Index(NDWI), Normalized Difference Infrared Index (NDII), Photochemical Reflectance Index (PRI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). The simulation and analysis results revealed: 1) Grassland in different growth stages exhibit similar sensitivities to certain spectral bands, namely those within the ranges of 540 nm–720 nm, 1250 nm–1690 nm, 1805 nm–2190 nm, and 2264 nm–2500 nm, which are more sensitive to various drought conditions. 2) Suitable vegetation indices for both growing and stable stages include NDII, MSI, PRI, LSWI, and GVMI, with silhouette coefficients exceeding 0.6 for the growing stage and 0.7 for the stable stage. The least suitable vegetation index is DVI, with an average silhouette coefficient of 0.15 over the entire growth stage. 3) From the spectral band perspective, among the five assessed satellites, MODIS Band 7 exhibits the highest sensitivity to water content across all satellite bands. MODIS's band configuration is most suitable for monitoring grassland drought during different growth stages, while WorldView 2's band configuration is the least suitable.

Arctic sea ice drift fields extraction based on feature tracking to MODIS imagery

Moderate-resolution optical imagery holds great potential in deriving Arctic sea ice drift fields because of its higher resolution than microwave radiometers and scatterometers, as well as its larger swath widths than most other optical and synthetic aperture radar (SAR) images. However, the application of such imagery is hindered by cloud influences and a lack of texture. In this study, we propose a method of deriving Arctic sea ice drift fields based on applying feature tracking to Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. To enhance the quality of the feature tracking step, a bundle of digital image processing techniques is first introduced, including histogram equalization which is based on the Cumulative Distribution Function (CDF) of the sea ice area, and Laplacian filtering which enhances image texture. Various MODIS bands and A-KAZE parameter settings are subsequently compared to balance the quality of sea ice drifting fields and calculation efficiency. Three pairs of MODIS images observed in different zones of the Arctic Ocean are selected to evaluate the performance of the proposed method. International Arctic Buoy Programme (IABP) buoy data are employed for validating the derived drift vectors with MODIS imagery. The results show that our proposed method effectively increases the number of vectors and their coverage rates of the sea ice drift fields extracted with MODIS images. The coverage rates of sea ice drift fields in three regions increase from 4.8%, 2.3%, and 2.5% to 56.5%, 23.5%, and 53.0% compared to using the A-KAZE algorithm directly, respectively. The MAEs of the derived sea ice motion vectors are 707 m/d in speed and 6.4° in direction, superior to the sea ice drift products based on the Advanced Very High Resolution Radiometer (AVHRR) imagery. The proposed method enables MODIS and other medium-resolution optical data to be utilized in deriving Arctic sea ice drift fields, which is of great significance to the long-term and large-scale Arctic environment, climate, and oceanography research in the future.

FABIAN: A daily product of Fractional Austral-summer Blue Ice over ANtarctica during 2000–2021 based on MODIS imagery using Google Earth Engine

Antarctic blue ice areas are exposed due to erosion and sublimation of snow. At the same time, surface melt can form surface types that are spectrally similar to blue ice, especially at low elevations. These are termed melt-induced blue ice areas. Both types of blue ice are sensitive indicators of climate change. Satellite remote sensing is a powerful technique to retrieve the spatial extent of blue ice areas and their variation in time. Yet, existing satellite-derived blue ice area products are either mono-temporal for the entire Antarctic ice sheet, or multi-temporal for a limited area. Here, we present FABIAN, a product of blue ice fraction over Antarctica, derived from the moderate resolution imaging spectroradiometer (MODIS) archive covering the period 2000–2021. A spectral mixture analysis (SMA) in Google Earth Engine, based on a careful selection of endmember spectra, accurately reconstructs the reflectance observed by MODIS in blue ice areas. Based on a validation with contemporaneous Sentinel-2 images, FABIAN has a root mean square error in blue ice fraction of approximately 10% ∼ 20% in wind-induced blue ice areas, and 20% ∼ 30% in melt-induced blue ice areas across six selected test sites in the coastal East Antarctic ice sheet. FABIAN is challenged in regions with shallow melt streams and lakes, since their spectral profiles are similar to those from blue ice areas in MODIS bands. For further analyses and applications, FABIAN holds the potential for (1) deriving annual blue ice area maps, (2) distinguishing between wind-and melt-induced blue ice types, (3) evaluating and correcting (regional) climate models, and (4) analyzing temporal variations in blue ice abundance and exposure.

Characterization of the on-orbit response versus scan angle for Terra MODIS SWIR bands in Collection 7

The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua platforms has 20 reflective solar bands (0.4 to 2.1 μm), for which the on-orbit detector gain changes are tracked using the on-board solar diffuser (SD) and SD stability monitor. SD and lunar observation data, supplemented by Earth-view (EV) observations for some bands, are used to characterize the scan-mirror response versus scan angle (RVS). The short-wave infrared (SWIR) bands (1.2 to 2.1 μm) have issues, such as electronic crosstalk and an out-of-band optical leak from the thermal bands, which complicate the use of the Moon for reliably characterizing the on-orbit RVS. Before Collection 7 (C7), the pre-launch RVS was deemed sufficient for both Aqua and Terra MODIS SWIR bands throughout the missions. However, recent studies have found that the RVS behavior does change with time in some SWIR bands, up to 2% and 1% for Terra MODIS bands 5 and 26, respectively. We assess the residual RVS effects present in the current MODIS calibration at desert sites and deep convective clouds (DCC). Due to the large uncertainties caused by the water vapor absorption effects from desert sites, especially for band 26 (1.375 μm), EV observations from DCCs are chosen to characterize the on-orbit RVS behavior for the SWIR bands. A time-dependent RVS characterization method for the SWIR bands is described and assessed by comparing the results with Collection 6.1 (C6.1) Level 1B (L1B) reflectance products. This new algorithm will be used in the upcoming C7 L1B reprocessing for Terra MODIS, providing more stable and accurate SWIR band reflectance data over time and across the scan range compared with the C6.1 results. For Aqua MODIS SWIR bands, performance evaluations with desert and DCC data indicate stable performance, so the pre-launch RVS will continue to be used in C7.

Progressive spatiotemporal image fusion with deep neural networks

Spatiotemporal image fusion (STIF) provides a feasible and effective solution for generating satellite images with high spatial and temporal resolution. As deep learning-based fusion algorithms show great potential in generating high-quality images, we propose a novel deep learning model, namely a deep progressive spatiotemporal fusion network (DPSTFN), which is coupled with pansharpening and super-resolution learning processes to satisfy requirements of STIF based on Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat data. First, a pansharpening process is adopted to make full use of two MODIS bands with 250 m spatial resolution. Second, a super-resolution process enhances the spatial information that existed in coarse-resolution images to alleviate the enormous spatial resolution gap between MODIS and Landsat images. Third, combining the aforementioned two auxiliary processes, a progressive spatiotemporal fusion framework is proposed to generate deliberate and robust fusion results. Experiments are conducted using two MODIS-Landsat datasets of distinctive landforms to evaluate the performance of DPSTFN. The results of the subjective and objective evaluation show that our proposed network performs better than the state-of-the-art traditional STIF algorithms Fit-FC and RASTFM, and the deep learning-based algorithms EDCSTFN and StfNet.

A Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production Phenology

The chlorophyll/carotenoid index (CCI) is increasingly used for remotely tracking the phenology of photosynthesis. However, CCI is restricted to few satellites incorporating the 531 nm band. This study reveals that the Moderate Resolution Imaging Spectroradiometer (MODIS) broadband green reflectance (band 4) is significantly correlated with this xanthophyll-sensitive narrowband (band 11) ( R 2 = 0.98 , p < 0.001 ), and consequently, the broadband green-red vegetation index GRVI—computed with MODIS band 1 and band 4—is significantly correlated with CCI—computed with MODIS band 1 and band 11 ( R 2 = 0.97 , p < 0.001 ). GRVI and CCI performed similarly in extracting phenological metrics of the dates of the start and end of the season (EOS) when evaluated with gross primary production (GPP) measurements from eddy covariance towers. For EOS extraction of evergreen needleleaf forest, GRVI even overperformed solar-induced chlorophyll fluorescence which is seen as a direct proxy of plant photosynthesis. This study opens the door for GPP and photosynthetic phenology monitoring from a wide set of sensors with broadbands in the green and red spectral regions.

Nonlinear Detector Response of Aqua MODIS Land Imaging Bands

The detectors in the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) reflective solar bands (RSBs) are calibrated assuming a linear relationship between the observed top-of-atmosphere radiance and the instrument output response. Any nonlinear behavior in the system response can contribute to errors in NASA's Level 1B MODIS reflectance and radiance products, which are used in a wide variety of Earth science applications. While most RSB detectors continue to have very linear behavior even after more than 18 years of operation, we present evidence that the detectors in Aqua MODIS bands 1 (645 nm) and 2 (858 nm) have deviations from gain linearity that change over the mission by up to 3%. We show a clear divergence between gain measurements made with the onboard solar diffuser (SD) at two different radiance levels, achieved by collecting data both with and without an attenuation screen. Radiance trends from lunar calibrations and typical desert and ocean scenes are also compared. We present a simple method to characterize the response nonlinearity using a quadratic function by combining the results of the two sets of SD calibrations. Applying this quadratic calibration algorithm to the desert and lunar data results in improved agreement in their long-term radiance trends. A clear understanding of the magnitude of the gain nonlinearity could be useful to help improve the quality of numerous MODIS science products, including the land imaging applications that rely heavily on these bands.

Application of quasi-deep convective clouds method for VIIRS and MODIS TEB calibration assessments

A technique that utilizes quasi-deep convective clouds (qDCC) for the calibration assessment of the thermal emissive bands (TEB) on remote sensing instruments has been proven viable. The Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi-National Polar-orbiting Partnership (S-NPP) and NOAA-20 (N20) Visible Infrared Imaging Radiometer Suite (VIIRS) TEB calibration uses a nonlinear algorithm with nonlinear coefficients that rely on on-orbit blackbody (BB) warm-up and cool-down operations for updates. However, a limited BB temperature range affects the calibration’s accuracy, particularly for cold scenes. The deep convective clouds (DCC) core, one of the coldest Earth scenes, is suitable for MODIS calibration assessments, more specifically, for the evaluation of the offset term’s effect in its TEB quadratic calibration function. Moreover, nighttime qDCC measurements provide the advantage of removing solar reflectance effects during daytime, thus enhancing the assessment’s accuracy for the mid-wave infrared TEB. This qDCC method is applied to the Terra MODIS and VIIRS TEB, and their stabilities are assessed using long-term qDCC trending measurements over the instruments’ missions. The measurements from bands with an 11-μm wavelength are used to identify the DCC pixels. The 11-μm bands, MODIS band 31, and VIIRS bands M15 and I5 are stable throughout the MODIS and VIIRS missions and have shown excellent calibration accuracy and noise performance. Hence, using these bands as references, a normalization method is employed to enhance the accuracy of the stability and consistency assessments. The S-NPP VIIRS TEB show stable trends over the instrument’s mission. The S-NPP-to-N20 VIIRS comparison shows that their TEB measurements are consistent over qDCC. The Terra MODIS TEB also shows stable performance—except for bands 27, 29, and 30. Terra MODIS band 30 shows a large downward trend throughout the mission, whereas bands 27 and 29 show slight, upward drifts. Finally, a calibration correction using qDCC assessments is discussed and intended to be used in a future calibration algorithm collection.

Cross-Calibration of MODIS Reflective Solar Bands With Sentinel 2A/2B MSI Instruments

Moderate resolution imaging spectroradiometer (MODIS) is a 36-band spectroradiometer that measures the Earth's surface from 0.4 to 14.4 μm at three spatial resolutions, 250 m (two bands), 500 m (five bands), and 1000 m (29 bands). The wide-scale use of the science products derived from the two MODIS instruments on the Terra and Aqua spacecrafts is a result of their excellent on-orbit performance, calibration stability, and accuracy through the life of the mission, making them a benchmark against which the performance of newer instruments is frequently evaluated. The recently launched multispectral instrument (MSI) aboard the Sentinel 2A and Sentinel 2B spacecrafts are part of the European Union's Copernicus program designed to acquire high spatial resolution imagery in the reflective spectrum from 0.4 to 2.2 μm. One of the popular techniques to evaluate the on-orbit calibration is by comparing the top-of-atmosphere (TOA) reflectance with an independent (well-calibrated) sensor while viewing a pseudoinvariant desert target, such as Libya 4. In this work, the TOA reflectances from Terra and Aqua MODIS and Sentinel 2A and Sentinel 2B MSI are compared using the same-day scenes from Libya 4. The corrections for spectral response function mismatch and the bidirectional reflectance distribution function (BRDF) are formulated and applied to obtain an effective TOA reflectance difference between the spectrally matching bands of these sensors. The availability of off-nadir MODIS overpass pairs with MSI facilitates the comparison across the entire MODIS scanangle range and in turn an on-orbit evaluation of the response versus scan-angle (RVS) corrections is performed. Additionally, each MODIS instrument is used as a transfer mechanism to evaluate the calibration differences between the two MSIs, with an agreement to within 1% observed between the two MSIs. The radiometric calibration differences between Terra MODIS and the two MSIs at nadir is generally within 4%, with only the red-band pair (Terra MODIS band 1 and MSI band 4) showing disagreement beyond 4%. In general, the reflectance ratios are in better agreement with Aqua MODIS than with Terra MODIS. A better agreement between MODIS and the two MSIs is observed at nadir, indicating some residual effects associated with the BRDF correction that are observed in the off-nadir scene pairs. Also included in this article is a description of the various uncertainties associated with this cross-calibration.

Estimation of CO2 Emissions from Wildfires Using OCO-2 Data

The biomass burning model (BBM) has been the most widely used method for estimation of trace gas emissions. Due to the difficulty and variability in obtaining various necessary parameters of BBM, a new method is needed to quickly and accurately calculate the trace gas emissions from wildfires. Here, we used satellite data from the Orbiting Carbon Observatory-2 (OCO-2) to calculate CO2 emissions from wildfires (the OCO-2 model). Four active wildfires in Siberia were selected in which OCO-2 points intersecting with smoke plumes identified by Aqua MODIS (MODerate-resolution Imaging Spectroradiometer) images. MODIS band 8, band 21 and MISR (Multi-angle Imaging SpectroRadiometer) data were used to identify the smoke plume area, burned area and smoke plume height, respectively. By contrast with BBM, which calculates CO2 emissions based on the bottom–top mode, the OCO-2 model estimates CO2 emissions based on the top–bottom mode. We used a linear regression model to compute CO2 concentration (XCO2) for each smoke plume pixel and then calculated CO2 emissions for each wildfire point. The CO2 mass of each smoke plume pixel was added to obtain the CO2 emissions from wildfires. After verifying our results with the BBM, we found that the biases were between 25.76% and 157.11% for the four active fires. The OCO-2 model displays the advantages of remote-sensing technology and is a useful tool for fire-emission monitoring, although we note some of its disadvantages. This study proposed a new perspective to estimate CO2 emissions from wildfire and effectively expands the applied range of OCO-2 satellite data.

Examination of Radiometric Deviations in Bands 29, 31, and 32 of MODIS Collection 6.0 and 6.1

The radiometric stability of selected thermal emissive bands (TEBs) of the Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites is examined via a direct intersensor comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The analysis adapts a “nadir-only” refinement of the simultaneous nadir overpasses (SNOs) analysis to evaluate radiometric deviations in MODIS Collection 6.0 and 6.1 data releases, the latter having been implemented with a version of crosstalk effect mitigation for Terra MODIS bands 27–30. The comparison time series result, with an average precision of 0.4%, shows radiometric deviation in Terra MODIS band 29 ( $8.55~\mu \text{m}$ ) up to 6%, or 3 K in brightness temperature (BT), in Collection 6.0, and correspondingly −1.5%, or −0.7 K in BT, in Collection 6.1, in particular significantly worsened by the safe mode event of February 2016. This paper includes the inter-TEB comparisons of MODIS bands B31 and B32 with SNPP VIIRS as two representative cases of nonideal spectral match demonstrating various key features and new information as revealed under the “nadir-only” framework, and also describes a procedure using both radiance-to-radiance comparison and BT-difference time series to estimate the BT-difference uncertainty specifications as well as the radiance-to-radiance time series baselines that are shifted away from unity. The finding for Terra MODIS band 29 highlights the continual nontrivial impact of crosstalk effect on numerous TEBs of MODIS Collection 6.1 not fully mitigated, which can further impact cloud masks, sea surface temperatures, and other product retrievals.

Sensitivity of Seven MODIS Vegetation Indices to BRDF Effects during the Amazonian Dry Season

We used Moderate Resolution Imaging Spectroradiometer (MODIS) data, processed by the multi–angle implementation of atmospheric correction (MAIAC) algorithm, to investigate the sensitivity of seven vegetation indices (VIs) to bidirectional reflectance distribution function (BRDF) effects in the dry season (June–September) of the Brazilian Amazon. The analysis was first performed over three sites, located from north to south of the Amazon, and then extended into the entire region. We inspected for differences in viewing–illumination parameters and pixel quality retrievals during MODIS data acquisition over the region. By comparing and correlating corrected and non–corrected data for bidirectional effects, we evaluated monthly changes in reflectance and VIs (2000–2014). Finally, we computed the effect size of the BRDF correction using non–parametric Mann–Whitney tests and Cohen’s r metrics. The results showed that the most anisotropic VIs were the enhanced vegetation index (EVI), photochemical reflectance index (PRI), and shortwave infrared normalized difference (SWND). These VIs presented the largest relative changes and the lowest correlation coefficients, between corrected and non–corrected data, because of the large effect size of the BRDF. The least anisotropic VI was the normalized difference water index (NDWI). The anisotropy of these VIs was stronger in the northern Amazon. It increased from the beginning to the end of the dry season, following changes in the relative azimuth angle (RAA) toward the BRDF hotspot in September. The modifications in the relative proportions of backscattering observations used in composite products caused a reflectance increase in all MODIS bands at the end of the dry season, especially in the near infrared (NIR). The reflectance decreased after BRDF correction. Because of the atmospheric effects, the view zenith angle (VZA) of the pixels selected in composite products decreased toward the south of the Amazon. In the southern Amazon, the seasonal amplitude in the solar zenith angle (SZA) reached values close to 18°. For the most anisotropic index, the BRDF correction removed, on average, 30% of the EVI signal in June, and 60% of the EVI signal in September, reducing dry season variations over time. The results reinforce the need for bidirectional correction of MODIS data before the seasonal and inter–annual analyses of the most anisotropic VIs.

Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data

ABSTRACT Rapid accurate estimation of the fractional cover of non-photosynthetic vegetation (f NPV) is essential for monitoring desertification, managing grassland resources, assessing soil erosion and grassland fire risk, and preserving the grassland ecological environment. However, there have been very few studies using multispectral remote sensing images (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS) images in this study) to estimate f NPV in typical grassland areas in northern China. In this study, using field spectra obtained from ground measurements in May and October 2017 and corresponding f NPV data, we calculated eight non-photosynthetic vegetation indices (NPVIs) from the simulated MODIS bands. We then determined the NPVIs that were suitable for the estimation of f NPV. Based on the determined NPVIs, we established a remote sensing estimation model for f NPV in typical grassland areas using MODIS image data. The spatial distribution of f NPV in the studied area was also investigated. The results indicated that the determined NPVIs, including the dead fuel index (DFI), shortwave-infrared ratio (SWIR32), normalized difference tillage index (NDTI), modified soil-adjusted crop residue index (MSACRI), and soil tillage index (STI), used bands 6 and 7 in the shortwave-infrared region of the MODIS data; the DFI had the best performance, with a coefficient of determination (R2 ) of 0.68 and root mean square error of leave-one-out cross-validation (RMSECV) of 0.1390. The models based on MODIS image data for the estimation of f NPV using NPVIs had relatively good regression relations, and we determined that the DFI linear regression model was the best remote sensing model for monitoring f NPV in typical grassland areas, with an estimation accuracy exceeding 73.00%. Additionally, our results indicated that the distribution of non-photosynthetic vegetation exhibited substantial spatial heterogeneity and that f NPV gradually decreased from the north-eastern to south-western portions of the study area.

A semi-empirical approach for the estimation of land-surface emissivity from satellite data based on spectral index fusion using ensemble regression

ABSTRACTLand Surface Emissivity (LSE) is a key parameter in the thermal remote sensing, with several important applications, most notably in Land Surface Temperature (LST) estimation. This paper presents a semi-empirical method of LSE estimation from remote sensing data based on a fusion of spectral indices using the ensemble regression methods. The performance of the proposed method for Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data was evaluated and compared with other semi-empirical methods developed for these sensors. The proposed method was designed in four stages. In the first stage, the reflectance of non-thermal bands and emissivity of thermal bands were simulated for different classes using the ASTER spectral library and the spectral response function of each sensor. In the second stage, the dataset to be used for the training of ensemble regression was arranged by calculating a number of spectral indices, which constitute the feature space along with non-thermal bands. In the third stage, the regression between emissivity of thermal bands of each sensor and the features extracted in the second stage was derived by the use of bagging, boosting and Random Forest (RF) regression methods. In the final stage Using Normalized Difference Vegetation Index (NDVI) values, the image was categorized into three classes including vegetation, non-vegetation and mixture areas using conditions NDVI > 0.5, NDVI < 0.2 and 0.2 ≤ NDVI ≤ 0.5, respectively. The non-vegetation class was then categorized to soil, rock, and man-made classes using land use map. The spectral indices of these classes were then calculated, and the corresponding model trained in the third stage was used to estimate the LSE for that band. The results of LSE estimations were compared with the standard product of each sensor. Due to the lack of standard product for Landsat-8, the ASTER product was used as a substitute. For better analysis, the proposed method was also evaluated with other semi-empirical methods developed for MODIS, ASTER and OLI/TIRS sensors. This evaluation showed that the lowest Root Mean Square Error (RMSE) values for OLI/TIRS bands 10 and 11 are 0.0070 and 0.0075 obtained, respectively, by bagging and RF regression methods. For ASTER bands 13 and 14, the lowest RMSE values of 0.0078 and 0.0077 are both obtained by RF regression. For MODIS bands 31 and 32, the lowest RMSE values are 0.0053 and 0.0049 and obtained by boosting method. A comparison between the proposed method and other semi-empirical methods provided for these sensors demonstrated the ability of the method to improve the RMSE by up to 0.5%. Regarding the higher accuracy and applicability of the proposed method, it can serve as an effective and efficient means of estimating LSE using remote sensing data.

MODIS remote sensing approaches to monitoring soil moisture in Tibet, China

ABSTRACTSatellite remote sensing from space offers a unique way to monitor surface soil moisture on a large geographical scale. In this work, the general approaches to monitoring surface soil moisture for Tibet area were developed using Moderate Resolution Imaging Spectroradiometer (MODIS) data and ground observations. The result shows that MODIS Vegetation Supply Water Index (VSWI) and band-7 reflectance are significantly correlated with ground measured soil moisture and MODIS band 7 is a simple and more effective method to monitor soil moisture in Tibet with higher spatial resolution at 500 m in comparison with VSWI at 1000 m resolution.

On the continuity of quantifying floating algae of the Central West Atlantic between MODIS and VIIRS

ABSTRACTStudying abundance and distributions of floating macroalgae such as pelagic Sargassum calls for long-term continuous and consistent observations from multiple satellite sensors. Previous studies mainly relied on observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Medium Resolution Imaging Spectrometer (MERIS). As a follow-on sensor, the Visible Infrared Imager Radiometer Suite (VIIRS) also has the appropriate spectral bands to detect and quantify floating macroalgae. Based on previous works on MODIS, this study presents an improved procedure to extract floating algae pixels from VIIRS Alternative Floating Algae Index (AFAI) imagery, with image filtering used to suppress noise and adjusted thresholds used to mask sun glint, clouds, and cloud shadows. The overall extraction accuracy is about 85%. Simultaneous daily observations from MODIS and VIIRS over the Central West Atlantic (CWA) show consistent spatial patterns, but VIIRS estimations of the algae coverage (in km2) are consistently lower than MODIS (around – 19% mean relative difference or MRD), possibly due to lower sensitivity of the VIIRS near-infrared (NIR) bands than the corresponding MODIS bands. Similarly, at monthly scale VIIRS also shows lower coverage than MODIS, and their difference (around – 29% MRD) is larger than the difference between MODIS-Aqua and MODIS-Terra estimates (around – 14% MRD). Despite these differences, the spatial and temporal patterns between VIIRS and MODIS observed algae distributions match very well at all spatial and temporal scales. These results suggest that VIIRS can provide continuous and consistent observations of floating algae distributions and abundance from MODIS as long as their differences are accounted for, thus assuring continuity in the future. Furthermore, once Sargassum biomass per unit Sargassum area is determined from field measurements, conversion of these area estimates to Sargassum biomass is straightforward.

Drought Impact on Phenology and Green Biomass Production of Alpine Mountain Forest—Case Study of South Tyrol 2001–2012 Inspected with MODIS Time Series

Ecological balance and biodiversity of the alpine forest is endangered by global and local climatic extremes. It spurs a need for comprehensive forest monitoring, including in depth analyses of drought impact on the alpine woodland ecosystems. Addressing an arising knowledge gap, we identified and analyzed 2002–2012 aridity related responses within the alpine mountain forest of South Tyrol. The study exploited a S-mode PCA (Principal Component Analysis) based synergy between meteorological conditions rendered by the scPDSI (self-calibrated Palmer Drought Severity Index) and forest status approximated through MODIS (Moderate Resolution Imaging Spectroradiometer) derived NDVI (Normalized Difference Vegetation Index) and NDII7 (Normalized Difference Infrared Index based on MODIS band 7) time series. Besides characterizing predominant forest temporal response to drought, we identified corresponding spatial footprints of drought impact, as well as examined aridity-related changes in forest phenology and biomass production. The latter was further evaluated in relation to forest type, elevation, aspect and slope. Recognized meteorological conditions highlighted: prolonged 2003–2007 mild to extreme drought, and overall regional drying tendencies. Arising remotely sensed forest responses accounted on localized decline in foliage water content and/or photosynthetic activity, but also indicated regions where forest condition improved despite the meteorological stress. Perceived variability in the forest response to drought conditions was governed by geographic location, species structure, elevation and exposition, and featured complexity of the alpine forest ecosystem. Among the inspected biophysical factors elevation had the strongest influence on forest phenology and green biomass production under meteorological stress conditions. Stands growing above 1400 m a.s.l. demonstrated initial increase in annual biomass growth at the beginning of the dry spell in 2003. Conversely, woodlands at lower altitudes comprising considerable share of hardwood species were more prone to biomass decline in 2003, but experienced an overall upturn in biomass production during the following years of the dry spell. Aspect showed moderate effect on drought-related phenology and green biomass production responses. Diverse forest ecosystem responses identified in this study were in line with known local and regional analyses, but also shed some new light on drought induced alternation of forest status.

Retrieving Total Suspended Matter in Tropical Reservoirs Within a Cascade System With Widely Differing Optical Properties

Total suspended matter (TSM) is an important indicator of water quality and has an adverse effect on light propagation in the water affecting the primary production. Moderate resolution imaging spectroradiometer (MODIS) data have been widely used to develop numerous semiempirical models all over the world. In this paper, many of these models perform satisfactorily either in a single water body or in many similar turbid water environments, however, their performance drops when data from oligo-to-eutrophic environments are used to monitor all reservoirs simultaneously. Most of them tend to overestimate TSM at low concentration. Two separate model calibrations were used to monitor the spatio-temporal variability of TSM in two oligo-to-eutrophic reservoirs, Nova Avanhandava (Nav) and Barra Bonita (BB), situated in the cascade system of Sao Paulo State, Brazil. The exponential model using MODIS band 1 yielded the best accuracy for BB (RMSE = 3.66 mg/L) and MODIS band 1 based linear model showed the best performance for Nav (RMSE = 0.37 mg/L). The distribution of TSM in Nav showed comparatively low and homogenous concentrations for the entire reservoir but the magnitude doubled for a period between May and October. The same seasonal variability occurred in BB showing increased TSM trend in October compared to May, but the spatial variability was not homogeneous because of the runoff from Piracicaba River, which is the main contributor of TSM. Rainfall associated runoff and land use and land cover were the principal factors for TSM variability.

A novel strategy for wetland area extraction using multispectral MODIS data

MODIS (Moderate Resolution Imaging Spectro-radiometer) is an imaging sensor onboard Terra/Aqua platform which provides information in 36 spectral bands. Spectral mixing of features due to high data dimensionality and high inter-band correlations constitutes the biggest challenge in the analysis of MODIS data. In view of this, present study attempts to develop a novel strategy involving Principal Component Analysis (PCA), Band to Band Correlation (BTBC) analysis, Stepwise Discriminant Analysis (SDA), and separability analysis to reduce the data dimensionality for extracting reliable and maximum information for wetland areal extent using coarse resolution MODIS (1km) data. The PCA explains variability in data and removes data redundant information; BTBC analysis eradicates the high correlated bands providing best bands suitable for wetland mapping; SDA evaluates the discriminatory power of different MODIS bands to discriminate the wetlands from other class types. Further, Normalized Difference Vegetation Index (NDVI) and Wetland Model Index (WMI) were also incorporated into the study to improve the classification accuracy. Finally, separability analysis was conducted to optimize the selected MODIS bands and indices.Results of rigorous data mining reveal that out of 24 input layers of MODIS (1km) data (22 optical MODIS bands, NDVI and WMI), only 4 input layers (WMI, NDVI, MODIS bands – NIR band 2 and SWIR band 6) are best suited for delineation and mapping of wetlands. This study also corroborates the usage of WMI, a newly developed index with combination of visible and short wavelength infra red (SWIR) MODIS bands, as the most optimal input layer to separate wetlands from the other land use class types (barren land, agricultural land, rivers/canals/streams, forest, wasteland/gullied or riverine land). It was observed that the magnitude of mapped wetland areal extent using MODIS (1km) data varied from 105,053ha in 2010–2011 to 111,479ha in 2011–2012 accounting for 0.44% (2010–2011)–0.46% (2011–2012) of the total geographical area of Uttar Pradesh, India. Seasonally, monsoon season displayed maximum wetland area with overall accuracy of 92% whereas summer season exhibited minimum wetland area with overall accuracies varying from 85 to 87% during both the sampling years. The output of the present research work will not only facilitate to improve the wetland area estimates but also provide an important input for climate change predictions in wetlands over large areas.

Unmanned aerial system nadir reflectance and MODIS nadir BRDF-adjusted surface reflectances intercompared over Greenland

Abstract. Albedo is a fundamental parameter in earth sciences, and many analyses utilize the Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo (MCD43) algorithms. While derivative albedo products have been evaluated over Greenland, we present a novel, direct comparison with nadir surface reflectance collected from an unmanned aerial system (UAS). The UAS was flown from Summit, Greenland, on 210 km transects coincident with the MODIS sensor overpass on board the Aqua and Terra satellites on 5 and 6 August 2010. Clear-sky acquisitions were available from the overpasses within 2 h of the UAS flights. The UAS was equipped with upward- and downward-looking spectrometers (300–920 nm) with a spectral resolution of 10 nm, allowing for direct integration into the MODIS bands 1, 3, and 4. The data provide a unique opportunity to directly compare UAS nadir reflectance with the MODIS nadir BRDF-adjusted surface reflectance (NBAR) products. The data show UAS measurements are slightly higher than the MODIS NBARs for all bands but agree within their stated uncertainties. Differences in variability are observed as expected due to different footprints of the platforms. The UAS data demonstrate potentially large sub-pixel variability of MODIS reflectance products and the potential to explore this variability using the UAS as a platform. It is also found that, even at the low elevations flown typically by a UAS, reflectance measurements may be influenced by haze if present at and/or below the flight altitude of the UAS. This impact could explain some differences between data from the two platforms and should be considered in any use of airborne platforms.