Red Band Reflectance Research Articles

Machine learning approach to predict the turbidity of Saki Lake, Telangana, India, using remote sensing data

Water quality is crucial for all life forms, yet water pollution is escalating. Monitoring water quality is essential to combat this challenge. This study introduces a precise and efficient approach to predict water turbidity levels using linear regression models and machine learning algorithms such as k-NN regression and decision trees. The model is trained using independent features like red band reflectance and NDTI. Hyperparameter tuning, utilizing grid search CV and repeated k-fold cross-validation, is applied to enhance the model's accuracy. The machine learning method was assessed with turbidity data measured from Saki Lake in Hyderabad, India, over four years (2014–2017) by the Telangana State Groundwater Department. Concurrently, Landsat-8 imagery from the USGS was employed for comprehensive analysis. The decision tree regression, optimized with hyperparameter tuning, outperformed the others, yielding an MAE of 3.246, an RMSE of 3.802, and a correlation coefficient (R2) of 0.776. This study validates the decision tree method's precision in forecasting water turbidity and its strong agreement with on-site measured values.

Comparison of Total Suspended Solids (TSS) in the estuary of the Bengawan Solo Gresik River using satellite image data

Comparison of Total Suspended Solids (TSS) in Muara Bengawan Solo Gresik River Using Satellite Image Data. The estuary of the Bengawan Solo River, which is in Ujung Pangkah District, has undergone many changes every year. This is influenced by several factors, one of which is the process of settling (sedimentation) from the transport of material carried by river water along the watershed (DAS). Based on this background, efforts are needed to monitor the distribution of Total Suspended Solid (TSS) in the Bengawan Solo Gresik river estuary. The aim of this study is to determine the distribution of Total Suspended Solid (TSS) over 5 years to find the right formula to model the impact of this change, which can be addressed in the future. The method developed below is remote sensing technology with Landsat 8 satellite image data extracting digital number values that are converted into reflectance values to then find the optimal mathematical model algorithm with parameters from Total Suspended Solid (TSS). From the research that has been done, the following results are obtained: the red band reflectance has the best relationship value with TSS In Situ with an R square value of 0.0450. and the appropriate algorithm model for estimating TSS concentrations is the Linear model with the equation Y = 1311.3x + 447.93.

Modulation of the Marine Environment in the Natal Bight

Modulation of the marine environment in the Natal Bight (~29.1°S, 31.6°E) was studied using daily high-resolution climate reanalysis products and monthly satellite green- and red-band reflectance in the period 2002–2022. The KwaZulu-Natal shelf edge is characterized by a narrow band of upwelling next to the warm Agulhas Current. Strong, reversing longshore winds ~7 m/s and meandering poleward flow ~1 m/s pulse the system, but along the leeward coast that forms the Natal Bight, environmental conditions are buffered by a weak cyclonic gyre. Wind and current shear create a shadow zone that aggregates plankton, recycles nutrients, and sustains marine resources. The seasonal cycle is of high amplitude: the surface heat balance reaches +70 W/m2 in December, followed by river discharges ~3 M m3/yr of fresh nutrient-rich water that peak in February. This induces a buoyant surface layer that inhibits wind wave turbulence during summer. By contrast, winter (June–August) cooling −95 W/m2 and frequent cyclonic storminess deepen the mixed layer from 25 to 65 m, enabling wind wave turbulence to reach the seafloor (Tugela Bank). Red-band reflectance increases 3-fold from summer to winter and is significantly correlated with net heat balance −0.54, daily wave heights > 2.5 m +0.51, mixed layer depth +0.47, sea surface temp −0.41, and wind vorticity −0.39. Daily longshore winds from the northeast and southwest were, unexpectedly, most amplified in spring (August–October). The seasonality exhibits sequential effects that supports year-round marine nutrification in the Natal Bight. Intra-seasonal fluctuations were related to meandering of the Agulhas Current and changes in longshore winds and shelf waves that impart significant pulsing of near-shore currents at 4–9-day periods. Although the cyclonic gyre in the Natal Bight spins up and down, SST variance was found to be relatively low in its center, where external influences are buffered. Considering linear trends for winds and runoff and surface temperature over the period 1950–2021, we found that northeasterlies increased, runoff decreased, and inshore sea surface temperatures have warmed slowly relative to the adjacent land surface temperature. New insights derive from the use of monthly satellite red-band reflectance and daily 10 km climate reanalysis fields to understand how air–land–sea fluxes modulate the marine environment in the Natal Bight.

Retrieval of Aerosol Single-Scattering Albedo from MODIS Data Using an Artificial Neural Network

Aerosol single-scattering albedo (SSA) is one of the largest sources of uncertainty in the evaluation of the aerosol radiative forcing effect. The SSA signal, coupled with aerosol optical depth (AOD) and surface reflectance in satellite images, is difficult to retrieve by the look-up table approach. In this study, we proposed an artificial neural network- (ANN) based approach that retrieves SSA over land based on MODIS (moderate resolution imaging spectroradiometer) visible (red band) reflectance variations among nearby pixels that have different surface reflectivities. Using the training dataset generated by the radiative transfer model, the ANN model was trained to establish the relationship among SSA, surface reflectance, and top of atmosphere (TOA) reflectance. Then, based on the trained ANN model, SSA can be retrieved using the surface and apparent reflectance of several heterogeneous pixels. According to sensitivity analysis, this method works well on nonuniform land surfaces with high AODs. The root mean square error (RMSE) of retrieved and measured SSA (from 28 sites of AErosol RObotic NETwork, AERONET) was 0.042, of which the results with an error less than 0.03 accounted for 51%. In addition, the SSA retrieval method was applied to several thick aerosol layer events over different areas (South Asia, South America, and North China Plain) and compared with the ozone monitoring instrument near-UV aerosol data product (OMAERUV). The comparison results of the images show that the retrieval method of visible wavelength proposed in this study has similar outcomes to those from the ultraviolet wavelengths in these regions. The retrieval algorithm we propose provides an effective way to produce an SSA product in visible wavelength and might help to better estimate the aerosol radiative and optical properties over high heterogeneous areas, which is important for the aerosol radiative impact estimate at a regional scale.

Solar illumination effects on the dry-season variability of spectral and spatial attributes calculated from PlanetScope data over tropical forests of the Amazon

ABSTRACT The spectral variability of tropical forests during the Amazonian dry season is not entirely understood because of the divergent responses in Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VIs) measured under-increased water deficit and high insolation. Here, we used a dataset composed of 493 cloud-free PlanetScope (PS) images to investigate possible effects of solar illumination on the dry-season variability of spectral and spatial attributes. The attributes were calculated from June to September over dense tropical forests of the Amazon. The dry-season images were obtained at nadir viewing between 2017 and 2019 over 12 selected sites representing different climatic and environmental conditions. To detect dry-season patterns of vegetation brightness with changes in the geometry of image acquisition, we applied principal component analysis (PCA) over the PS surface reflectance. We plotted the average surface reflectance (2017–2019) for each of the four PS bands and inspected the variability of two VIs with distinct levels of anisotropy to bidirectional effects: the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). We also investigated the signal of textural metrics from Grey Level Co-occurrence Matrix (GLCM) obtained from the near-infrared (NIR) band of PS. Finally, we generated shade fractions from Spectral Mixture Analysis (SMA), correlated the spectral and spatial attributes of vegetation with solar angles, and observed the dry-season variability in reflectance and VIs over pseudo-invariant soil surfaces. The results showed the existence of solar illumination effects on PS image acquisition during the dry season of the Amazon, which affected differently the NDVI and EVI. From the beginning (June) to the end (September) of the dry season, the solar zenith angle (SZA) decreased and the solar azimuth angle (SAA) increased during the period of acquisition of the PS images. The amplitude of SZA between June and September increased towards south of the Amazon, while the amplitude of SAA increased towards north of this region. Changes in vegetation brightness from June to September were captured by PCA over some sites. Because of the overall increase in both red and NIR band reflectance, solar illumination effects were compensated during the NDVI calculation. In contrast, because the EVI is largely driven by changes in NIR reflectance, these effects contributed to increase the EVI signal at the end of the dry season. For most sites, GLCM texture mean increased towards the end of the dry season, while texture variance decreased in the opposite direction. Shade fractions decreased towards September when reduced amounts of canopy shadows were sensed by PS. EVI was more anisotropic than NDVI and presented higher negative correlations with SZA and shade fractions and higher positive correlations with SAA and texture mean. The dry-season increase in EVI with solar illumination effects was also observed over pseudo-invariant soil surfaces. From this unprecedent scale of observations at high spatial and temporal resolutions, we recommend caution when using anisotropic VIs for large-scale phenological studies over the Amazon because biophysical and non-biophysical signals may be coupled together.

Total suspended solids characterization and management implications for lakes in East China

In this study, we empirically developed a robust model (the Root Mean Square Error (RMSE), bias, NSE and RE were 26.63 mg/L, −4.86 mg/L, 0.47 and 16.47%, respectively) for estimating the total suspended solids (TSS) concentrations in lakes and reservoirs (Hereinafter referred to as lakes) across the Eastern Plain Lake (EPL) Zone. The model was based on 700 in-situ TSS samples collected during 2007–2020 and logarithmic transformed red band reflectance of Landsat data. Based on the Google Earth Engine (GEE), the TSS concentrations in 16,804 lakes were mapped from 1984 to 2019. The results demonstrated a decreasing tendency of TSS in 82.2% of the examined lakes (72.5% of the basins) indicating that the pollutants carried by TSS flowing into the lakes were decreasing. Statistically significant variation (p < 0.05) was found in half of these lakes (28.6% of the basins). High TSS level (>100 mg/L) was observed in 0.31% of lakes (1.1% of the basins). The changing rates of TSS in 47.8% of the lakes (52.7% of the basins) ranged between −50 mg/L/yr and 0. We found high and significantly increased relative spatial heterogeneity of TSS in 4.6% and 6.5% of lakes, respectively. Likewise, the environmental factors, i.e., fertilizer usage, domestic wastewater, industrial wastewater, precipitation, wind speed and Normalized Difference Vegetation Index (NDVI) exhibited a significant correlation with interannual TSS in 38, 21, 20, 11, 17 and 15 of the 91 basins, respectively. This analysis indicated that only precipitation and fertilizer usage were significantly (p < 0.05) related to the spatial distribution of TSS. The relative contributions of the six factors to the interannual TSS changes were varied in different basins. Overall, the NDVI (the representation of vegetation cover) had a high mean contribution to the interannual TSS changes with an average contribution of 7.2%, and contributions of fertilizer were varied greatly among the basins (0.01%–68%). Human activities (fertilizer usage, domestic wastewater, industrial wastewater) and natural factors (precipitation, wind speed and NDVI) played relatively important roles to TSS changes in 14 and 15 of the 91 basins, respectively. Beyond the six factors in this study, other unanalyzed factors (such as lake depth and soil texture) also had some impacts on the distribution of TSS in the study area.

Identifying river ice on the Tibetan Plateau based on the relative difference in spectral bands

Remote sensing can potentially be used to monitor river ice, which is of significance for water resource utilization and hydrological research, especially on the Tibetan Plateau, over which field observations are severely limited. However, methods to monitor the river ice distribution over a large scale while overcoming snow interference have not been reported yet. To monitor river ice on the Tibetan Plateau on a large scale, we present a relative difference river ice (RDRI) identification method based on the differential spectral characteristics of river ice. The difference between the red and near-infrared (NIR) band reflectance of river ice is divided by the sum of the NIR reflectance and infrared band reflectance. The resulting values are notably different for river ice and other neighboring similar landscapes, such as snow cover. This method can overcome the interference caused by snow cover and other surrounding landscapes and enable the monitoring of river ice in different forms at different elevations on the Tibetan Plateau. The RDRI method is applied to Landsat and Sentinel-2 images. The validation results show that the mean overall accuracy and Kappa coefficient were 99.76% and 0.94, respectively. The comparison of the RDRI and normalized difference snow index (NDSI) methods indicates that the accuracies of both methods for river ice monitoring are similar in the absence of any snow disturbance. The RDRI can enable more accurate remote sensing of river ice on a large scale, which can support river ice distribution research over the Tibetan Plateau.

Original

Highly polluted lake water turbidity was analyzed to detect the changes during COVID-19 lockdown. Total eight Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imageries are used to observe the qualitative change of lake water turbidity for pre, during and post lockdown period. The imageries from January to June, and September and October were analyzed for change detections. Cloud free imageries have been chosen for analysis. The water pixels and turbidity are assessed by the Normalized Difference Water Index (NDWI) and Normalized Difference Turbidity Index (NDTI), respectively. The blue, red and NIR band reflectance indicates that most of the lake water becomes clearer than before the lockdown period except May. The turbidity index shows low pollution after the lockdown period. High turbidity was observed evenly distributed from January to June. In September and October, high turbidity only found the upper part of the lake. This probably indicates the floating pollutants in the lake. The qualitative study of lake water turbidity using a remote sensing approach indicates that the COVID-19 lockdown improves the water quality in city lake water.

Evaluation of soybean condition under various fertilizer application by the relationship of the red and near-infrared bands reflectance in scatter plot

This study aims to determine the dependence of the red band reflectance and near-infrared band reflectance in the evaluation of crops. The experiment using various fertilizers on soybean crops was analyzed to show the difference in reflections of red and near-infrared wavelengths. We used a multispectral camera attached to a UAV to obtain reflectance data of the surface. In this study, we used the normalized difference vegetation index, calculated based on red and near-infrared bands. The lower the value of red reflectance and the higher of near-infrared reflectance, the more chlorophyll, healthy vegetation, and the higher the crop condition. The decreasing photosynthesis rates and changes in leaf mesophyll are usually associated with decreasing reflectance of wavelengths within the near-infrared spectral range. We processed the obtained images and data from the camera and analyzed the reflectance separately for each plot of the surveyed field and each pixel of the image. The results showed in this paper that, according to a scatter plot analysis, different pixel concentrations were noted, which indicated by different information on the test field plot.

Damage Analysis of Three Long-Track Tornadoes Using High-Resolution Satellite Imagery

Remote sensing of tornado damage can provide valuable observations for post-event surveys and reconstructions. The tornadoes of 3 March 2019 in the southeastern United States are an ideal opportunity to relate high-resolution satellite imagery of damage with estimated wind speeds from post-event surveys, as well as with the Rankine vortex tornado wind field model. Of the spectral metrics tested, the strongest correlations with survey-estimated wind speeds are found using a Normalized Difference Vegetation Index (NDVI, used as a proxy for vegetation health) difference image and a principal components analysis emphasizing differences in red and blue band reflectance. NDVI-differenced values across the width of the EF-4 Beauregard-Smiths Station, Alabama, tornado path resemble the pattern of maximum ground-relative wind speeds across the width of the Rankine vortex model. Maximum damage sampled using these techniques occurred within 130 m of the tornado vortex center. The findings presented herein establish the utility of widely accessible Sentinel imagery, which is shown to have sufficient spatial resolution to make inferences about the intensity and dynamics of violent tornadoes occurring in vegetated areas.

Using Small Unmanned Aircraft Systems for Early Detection of Drought Stress in Turfgrass

ABSTRACTRecent advances in small unmanned aircraft systems (sUAS) and sensors may improve accuracy and efficiency in turfgrass research and management compared with conventional methods. We evaluated the ability of sUAS combined with ultra‐high spatial resolution remote sensing to detect early drought stress. Results were compared with ground‐based techniques in creeping bentgrass (Agrostis stolonifera L.) irrigated at different levels from well‐watered to severe deficit (100 to 15% evapotranspiration [ET] replacement). Small UAS‐based measurements with a modified digital camera included three reflectance bands (near infrared [NIR, 680–780 nm] and overlapping green [G] and blue [B] bands [400–580 nm]) and eight derived vegetation indices (VIs). Ground‐based measurements included soil volumetric water content (VWC), turfgrass quality (TQ), green cover (GC), soil temperature (Tsoil), and reflectance with handheld optical sensors. Declines in VWC in deficit‐irrigation treatments were detected with NIR and six of eight VIs from sUAS, and the normalized difference vegetation index (NDVI) and red band reflectance from a handheld sensor, before symptoms appeared in TQ and GC. The most consistently sensitive parameters of sUAS throughout the 3‐yr study were NIR and GreenBlue VI [(G − B)/(G + B)], which detected drought stress &gt;5 d before decreases in TQ. Results indicate that ultra‐high spatial resolution remote sensing with sUAS detected drought stress before it was visible to a human observer and could be valuable for improving irrigation management in turfgrass.

Developing an Automatic Phenology-Based Algorithm for Rice Detection Using Sentinel-2 Time-Series Data

Phenology-based classification methods have been developed with the goal of removing the need for ground sampling. In contrast, machine-learning-based classification methods are costly since they need extensive ground truth data to be collected, a process which is time-consuming and must be repeated on a seasonal or annual basis. Hence, in this research, we present a new automatic rule-based method, based on crop phenology, to detect rice using the time series of Sentinel-2 imagery. To do the research, the 10-m spatial resolution Sentinel-2 data acquired during the rice growing season in the red and near-infrared spectral bands for three regions in Iran were used. To develop the rules, the near-infrared band reflectance at the rice cultivation time, the red band reflectance close to the rice harvest time, and the temporal Normalized Difference Vegetation Index data were used to detect rice and discriminate it from other crops. Furthermore, the dates of data associated with the phenological stages used to develop the rice classification rules were extracted from the rice crop calendar. Although the rice fields had extensive intra-class temporal phenology variability, the algorithm performed with excellence in detecting them. The kappa coefficients obtained were 0.73, 0.94, and 0.70 for Marvdasht, Dargaz, and Qazvin, respectively.

Mapping Canopy Heights of Poplar Plantations in Plain Areas Using ZY3-02 Stereo and Multispectral Data

Forest canopy height plays an important role in forest management and ecosystem modeling. There are a variety of techniques employed to map forest height using remote sensing data but it is still necessary to explore the use of new data and methods. In this study, we demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data from China’s latest civilian stereo mapping satellite ZY3-02. First, a digital surface model (DSM) was extracted using photogrammetry methods. Then, canopy samples and ground samples were selected through manual interpretation. Canopy height samples were obtained by calculating the DSM elevation differences between the canopy samples and ground samples. A regression model was used to correlate the reflectance of a ZY3-02 multispectral image with the canopy height samples, in which the red band and green band reflectance were selected as predictors. Finally, the model was extrapolated to the entire study area and a wall-to-wall forest canopy height map was obtained. The validation of the predicted canopy height map reported a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 1.58 m. This study demonstrates the capacity of ZY3-02 data for mapping the canopy height of pure plantations in plain areas.

Satellite-based monitoring of contrasting characteristics of suspended sediment discharged from the Red and the Ma river systems along the northern coast of Vietnam

The northern coast of Vietnam has accumulated a significant amount of sediment discharged from the multiple distributaries of rivers such as the Red River and Ma River. While recent decreases of the sediment supply appears to have a significant impact on coastal erosion, the complex network of these distributaries makes it difficult to determine the overall spatiotemporal characteristics of sediment discharges and related topographic changes. The goal of the current study is to develop a satellite-based monitoring system for observation of turbidity discharged from the multiple rivers and to investigate the applicability of the developed monitoring system through a case study on the northern coast of Vietnam. Based on the in-situ observed data, a formula was determined for estimation of the surface water turbidity as a function of the red band reflectance of Moderate Resolution Imaging Spectro-radiometer (MODIS) images. The formula was then combined with a newly determined threshold for cloud-masking to obtain maps of the nearshore turbidity patterns. These maps capture the spatiotemporal water surface turbidity along the entire coast of the Red River Delta and the coast around the Ma River mouth over the past sixteen years with frequency of twice a day. Finally, long-term trends of the turbidity patterns from multiple rivers were compared with the in-situ observation data and it was found that the Red River and the Ma River showed clearly contrasting characteristics, which reasonably explain the recent coastal shoreline changes and characteristics of sediment sampled along the coast.

Terrestrial CDOM in Lakes of Yamal Peninsula: Connection to Lake and Lake Catchment Properties

In this study, we analyze interactions in lake and lake catchment systems of a continuous permafrost area. We assessed colored dissolved organic matter (CDOM) absorption at 440 nm (a(440)CDOM) and absorption slope (S300–500) in lakes using field sampling and optical remote sensing data for an area of 350 km2 in Central Yamal, Siberia. Applying a CDOM algorithm (ratio of green and red band reflectance) for two high spatial resolution multispectral GeoEye-1 and Worldview-2 satellite images, we were able to extrapolate the a(λ)CDOM data from 18 lakes sampled in the field to 356 lakes in the study area (model R2 = 0.79). Values of a(440)CDOM in 356 lakes varied from 0.48 to 8.35 m−1 with a median of 1.43 m−1. This a(λ)CDOM dataset was used to relate lake CDOM to 17 lake and lake catchment parameters derived from optical and radar remote sensing data and from digital elevation model analysis in order to establish the parameters controlling CDOM in lakes on the Yamal Peninsula. Regression tree model and boosted regression tree analysis showed that the activity of cryogenic processes (thermocirques) in the lake shores and lake water level were the two most important controls, explaining 48.4% and 28.4% of lake CDOM, respectively (R2 = 0.61). Activation of thermocirques led to a large input of terrestrial organic matter and sediments from catchments and thawed permafrost to lakes (n = 15, mean a(440)CDOM = 5.3 m−1). Large lakes on the floodplain with a connection to Mordy-Yakha River received more CDOM (n = 7, mean a(440)CDOM = 3.8 m−1) compared to lakes located on higher terraces.

A Landsat-based model for retrieving total suspended solids concentration of estuaries and coasts in China

Abstract. Retrieving total suspended solids (TSS) concentration accurately is essential for sustainable management of estuaries and coasts, which plays a key role in the interaction between hydrosphere, pedosphere and atmosphere. Although many TSS retrieval models have been published, the general inversion method that is applicable to different field conditions is still under research. In order to obtain a TSS remote sensing model that is suitable for estimating TSS concentrations with wide range in estuaries and coasts by Landsat imagery, after reviewing a number of Landsat-based TSS retrieval models and improving a comparatively better one among them, this study developed a quadratic model using the ratio of logarithmic transformation of red band and near-infrared band and logarithmic transformation of TSS concentration (QRLTSS) based on 119 in situ samples collected in 2006–2013 from five regions of China. It was found that the QRLTSS model works well and shows a satisfactory performance. The QRLTSS model based on Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus) and OLI (Operational Land Imager) sensors explained about 72 % of the TSS concentration variation (TSS: 4.3–577.2 mg L−1, N = 84, P value &lt; 0.001) and had an acceptable validation accuracy (TSS: 4.5–474 mg L−1, root mean squared error (RMSE) ≤ 25 mg L−1, N = 35). In addition, a threshold method of red-band reflectance (OLI: 0.032, ETM+ and TM: 0.031) was proposed to solve the two-valued issue of the QRLTSS model and to retrieve TSS concentration from Landsat imagery. After a 6S model-based atmospheric correction of Landsat OLI and ETM+ imagery, the TSS concentrations of three regions (Moyangjiang River estuary, Pearl River estuary and Hanjiang River estuary) in Guangdong Province in China were mapped by the QRLTSS model. The results indicated that TSS concentrations in the three estuaries showed large variation ranging from 0.295 to 370.4 mg L−1. Meanwhile we found that TSS concentrations retrieved from Landsat imagery showed good validation accuracies with the synchronous water samples (TSS: 7–160 mg L−1, RMSE: 11.06 mg L−1, N = 22). The further validation from EO-1 Hyperion imagery also showed good performance (in situ synchronous measurement of TSS: 106–220.7 mg L−1, RMSE: 26.66 mg L−1, N = 13) of the QRLTSS model for the area of high TSS concentrations in the Lingding Bay of the Pearl River estuary. Evidently, the QRLTSS model is potentially applied to simulate high-dynamic TSS concentrations of other estuaries and coasts by Landsat imagery, improving the understanding of the spatial and temporal variation of TSS concentrations on regional and global scales. Furthermore, the QRLTSS model can be optimized to establish a regional or unified TSS retrieval model of estuaries and coasts in the world for different satellite sensors with medium- and high-resolution similar to Landsat TM, ETM+ and OLI sensors or with similar red bands and near-infrared bands, such as ALI, HJ-1 A and B, LISS, CBERS, ASTER, ALOS, RapidEye, Kanopus-V, and GF.

TIME SERIES ANALYSIS OF TOTAL SUSPENDED SOLID (TSS) USING LANDSAT DATA IN BERAU COASTAL AREA, INDONESIA

Water quality information is usually used for the first examination of the pollution. One of the parameters of water quality is Total Suspended Solid (TSS), which describes the amount of matter of particles suspended in the water. TSS information is also used as initial information about waters condition of a region. TSS could be derive from Landsat data with several combinations of spectral channels to evaluate the condition of the observation area for both the waters and the surrounding land. The study aimed to evaluate Berau waters condition in Kalimantan, Indonesia, by utilizing TSS dynamics extracted from Landsat data. Validated TSS extraction algorithm was obtained by choosing the best correlation between field data and image data. Sixty pairs of points had been used to build validated TSS algorithms for the Berau Coastal area. The algorithm was TSS = 3.3238 * exp (34 099 * Red Band Reflectance). The data used for this study were Landsat 5 TM, Landsat 7 ETM and Landsat 8 data acquisition in 1994, 1996, 1998, 2002, 2004, 2006, 2008 and 2013. For detailed evaluation, 20 regions were created along the watershed up to the coast. The results showed the fluctuation of TSS values in each selected region. TSS value increased if there was a change of any kind of land cover/land used into bareland, ponds, settlements or shrubs. Conversely, TSS value decreased if there was a wide increase of mangrove area or its position was very closed to the ocean.

The accuracy of large-area forest canopy cover estimation using Landsat in boreal region

Large area prediction of continuous field of tree cover i.e., canopy cover (CC) using Earth observation data is of high interest in practical forestry, ecology, and climate change mitigation activities. We report the accuracy of using Landsat images for CC prediction in boreal forests validated with field reference plots (N=250) covering large variation in latitude, forest structure, species composition, and site type. We tested two statistical models suitable for estimating CC: the beta regression (BetaReg) and random forest (RanFor). Landsat-based predictors utilized include individual bands, spectral vegetation indices (SVI), and Tasseled cap (Tass) features. Additionally, we tested an alternative model based on spectral mixture analysis (SMA). Finally, we carried out a first validation in boreal forests of the recently published Landsat Tree Cover Continuous (TCC) global product.Results showed simple BetaReg with red band reflectance provided the highest prediction accuracy (leave-site-out RMSECV 13.7%; R2CV 0.59; biasCV 0.5%). Spectral transformations into SVI and Tass did not improve accuracy. Including additional predictors did not significantly improve accuracy either. Nonlinear model RanFor did not outperform BetaReg. The alternative SMA model did not outperform the empirical models. However, empirical models cannot resolve the underestimation of high cover and overestimation of low cover. SMA prediction errors appeared less dependent on forest structure, while there seemed to be a potential for improvement by accounting for endmember variability of different tree species. Finally, using temporally concurrent observations, we showed the reasonably good accuracy of Landsat TCC product in boreal forests (RMSE 13.0%; R2 0.53; bias −2.1%), however with a tendency to underestimate high cover.

Sources of bias and variability in long-term Landsat time series over Canadian boreal forests

Abstract A variety of evidence suggests that the boreal forests of Canada are responding to climate change. Specifically, several studies have inferred that widespread browning trends detected in time series of the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) reflect the response of boreal forests to longer growing seasons, increased summer drought stress, and higher frequency of fires. Data from the Thematic Mapper (TM5) and Enhanced Thematic Mapper Plus (ETM+) sensors onboard Landsat 5 and 7, respectively, span essentially the same time period as the AVHRR record, but provide data with substantially higher radiometric and spatial fidelity, and by extension, a much improved basis for evaluating decadal-scale trends in spectral vegetation indices such as the NDVI. However, detection of trends, which are often subtle, requires careful attention to ensure that artifacts associated with the quality and stability of inter- and intra-sensor calibration do not lead to spurious conclusions in results from time series analyses. In this paper, we use time series of TM5 and ETM+ images for fifteen sites distributed across the Canadian boreal forest zone to explore if and how sensor geometry and inter- and intra-sensor calibration affect trends in spectral vegetation indices derived from multi-decadal Landsat time series. To do this, we created annual cloud-free composites for each Landsat spectral band based on peak summer NDVI at each site from 1984 to 2011 using all available TM5 and ETM+ data. To distinguish trends arising from long term climate change from those related to disturbance, we isolated areas within each site that were undisturbed during the Landsat record, and used these locations to analyze sources of variance in time series of red reflectance, near-infrared (NIR) reflectance, the NDVI, and the Enhanced Vegetation Index (EVI). Our results highlight the challenges involved in distinguishing trends in surface properties from data artifacts caused by undetected atmospheric effects, changes in sensor view angles, and subtle radiometric differences between the TM5 and ETM+ sensors. In particular, differences in sensor view geometry across adjacent overlapping Landsat scenes cause vegetated pixels in the eastern portion of Landsat scenes to have higher reflectances in the red and NIR bands (by 5 and 6 percent, respectively) than pixels in the western portion of scenes. While this effect does not significantly change NDVI values, it does affect EVI values. We also found modest, but potentially significant, differences between the red band reflectance of each sensor, with TM5 data having 14 percent higher red reflectance on average for vegetated pixels, which can introduce spurious trends in time series that combine TM5 and ETM+ data. More generally, the results from this work demonstrate that while the 30 + year Landsat archive provides unprecedented opportunities for studying changes to the Earth's terrestrial biosphere over the last three decades, care must be taken when inferring trends in these data without considering how sources of variance unrelated to surface processes affect the integrity of Landsat time series.

Simulation of GOES-R ABI aerosol radiances using WRF-CMAQ: a case study approach

Abstract. In anticipation of the upcoming GOES-R launch we simulate visible and near-infrared reflectances of the Advanced Baseline Imager (ABI) for cases of high aerosol loading containing regional haze and smoke over the eastern United States. The simulations are performed using the Weather Research and Forecasting (WRF), Sparse Matrix Operator Kernel Emissions (SMOKE), and Community Multiscale Air Quality (CMAQ) models. Geostationary, satellite-derived, biomass-burning emissions are also included as an input to CMAQ. Using the CMAQ aerosol concentrations and Mie calculations, radiance is computed from the discrete ordinate atmospheric radiative transfer model. We present detailed methods for deriving aerosol extinction from WRF and CMAQ outputs. Our results show that the model simulations create a realistic set of reflectances in various aerosol scenarios. The simulated reflectances provide distinct spectral features of aerosols which are then compared to data from the Moderate Resolution Imaging Spectroradiometer (MODIS). We also present a simple technique to synthesize green band reflectance (which will not be available on the ABI), using the model-simulated blue and red band reflectance. This study is an example of the use of air quality modeling in improving products and techniques for Earth-observing missions.