Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake,China

Based on in situ water sampling and field spectral measurements in Dianshan Lake, a semi-analytical three-band algorithm was used to estimate Chlorophylla (Chla) content in case II waters. The three bands selected to estimate Chla for high concentrations included 653, 691 and 748 nm. An equation, based on the difference in reciprocal reflectance between 653 and 691 nm, multiplied by reflectance at 748 nm as [R_rs⁻¹(653) − R_rs⁻¹ (691)] R_rs(748), explained 85.57% of variance in Chla concentration with a root mean square error (RMSE) of <6.56 mg/m³. In order to test the utility of this model with satellite data, HJ-1A Hyperspectral Imager (HSI) data were analyzed using comparable wavelengths selected from the in situ data [B₆₇⁻¹(656) − B₈₀⁻¹(716)] B₈₇(753). This model accounted for 84.3% of Chla variation, estimating Chla concentrations with an RMSE of <4.23 mg/m³. The results illustrate that, based on the determined wavelengths, the spectrum-based model can achieve a high estimation accuracy and can be applied to hyperspectral satellite imagery especially for higher Chla concentration waters.     

Remote quantification of phycocyanin in potable water sources through an adaptive model

Cyanobacterial blooms in water supply sources in both central Indiana USA (CIN) and South Australia (SA) are a cause of great concerns for toxin production and water quality deterioration. Remote sensing provides an effective approach for quick assessment of cyanobacteria through quantification of phycocyanin (PC) concentration. In total, 363 samples spanning a large variation of optically active constituents (OACs) in CIN and SA waters were collected during 24 field surveys. Concurrently, remote sensing reflectance spectra (R_rs) were measured. A partial least squares–artificial neural network (PLS–ANN) model, artificial neural network (ANN) and three-band model (TBM) were developed or tuned by relating the R_rs with PC concentration. Our results indicate that the PLS–ANN model outperformed the ANN and TBM with both the original spectra and simulated ESA/Sentinel-3/Ocean and Land Color Instrument (OLCI) and EO-1/Hyperion spectra. The PLS–ANN model resulted in a high coefficient of determination (R²) for CIN dataset (R² = 0.92, R: 0.3–220.7 μg/L) and SA (R² = 0.98, R: 0.2–13.2 μg/L). In comparison, the TBM model yielded an R² = 0.77 and 0.94 for the CIN and SA datasets, respectively; while the ANN obtained an intermediate modeling accuracy (CIN: R² = 0.86; SA: R² = 0.95). Applying the simulated OLCI and Hyperion aggregated datasets, the PLS–ANN model still achieved good performance (OLCI: R² = 0.84; Hyperion: R² = 0.90); the TBM also presented acceptable performance for PC estimations (OLCI: R² = 0.65, Hyperion: R² = 0.70). Based on the results, the PLS–ANN is an effective modeling approach for the quantification of PC in productive water supplies based on its effectiveness in solving the non-linearity of PC with other OACs. Furthermore, our investigation indicates that the ratio of inorganic suspended matter (ISM) to PC concentration has close relationship to modeling relative errors (CIN: R² = 0.81; SA: R² = 0.92), indicating that ISM concentration exert significant impact on PC estimation accuracy.     

Assessment of satellite ocean color products of MERIS,MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea)

The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance R_rs(λ) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APD_m) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr⁻¹, whereas the R_rs(λ) uncertainty at 412 nm is the highest (APD_m 47–62%, RMS 0.0027–0.0041 sr⁻¹). The uncertainties of the aerosol optical thickness (AOT) τ_a, diffuse attenuation coefficient for downward irradiance at 490 nm K_d(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of K_d(490), SPM, and Chl-a may decrease significantly to the level of 20–30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task.     

Uncertainty analysis of remote sensing of colored dissolved organic matter: Evaluations and comparisons for three rivers in North America

The uncertainties involved in remote sensing inversion of CDOM (Colored Dissolved Organic Matter) were analyzed in estuarine and coastal regions of three North American rivers: Mississippi, Hudson, and Neponset. Water optical and biogeochemical properties, including CDOM absorption and above-surface spectra, were collected in very high resolution. CDOM’s concentrations (a_g(440), absorption coefficient at 440 nm) were inverted from EO-1 Hyperion images, using a quasi-analytical algorithm for CDOM (QAA-CDOM). Uncertainties are classified to five levels, in which the underwater measurement uncertainty (level 1), image preprocessing uncertainty (level 4) and inverse model uncertainty (level 5) were evaluated. Results indicate that at level 1, in situ CDOM measurement is significant with 0.1 in the unit of QSU and 0.01 in the unit of a_g(440) (m⁻¹). At level 4, surface wave is a potential uncertainty source for high-resolution images in estuarine and coastal regions. The remote sensing reflectance of wavy water is about 10 times of the truth. At level 5, the overall uncertainty of QAA-CDOM inversion is 0.006 m⁻¹, with accuracy R² = 0.77, k = 1.1 and RMSE_log = 0.33 m⁻¹. The correlations between uncertainties and other water properties indicate that the large uncertainty in some rivers, such as the Neponset and Atchafalaya, might be caused by high-concentration chlorophyll or sediments. The relationships among the three level uncertainties show that the level 1 uncertainty generally does not propagate into level 4 and 5, but the large uncertainty at level 4 usually introduce large uncertainty at level 5.     

内陆水体叶绿素a浓度反演的区域化三波段模型研究

以太湖和石头口门水库为例,建立了一种可直接计算水体叶绿素a浓度的区域化三波段模型。该模型通过对研究水体的光学特性分析,明确三波段组合与叶绿素a浓度间的函数关系参数,不仅可较好指示研究水体叶绿素a浓度水平,而且大大减小了回归算法所带来的模型不确定性。利用高精度、长时间序列的水体光学特性观测数据,建立针对不同研究水体的区域化三波段模型可为实现内陆水体叶绿素a浓度遥感监测业务化运行提供方法借鉴。     

Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression

This paper examines the use of canopy reflectance for different units of measurements of carotenoids estimation. Field spectral measurements were collected over cotton in different intensive field campaigns organized during the growing seasons of 2010 and 2011. Three units of measurement were evaluated carotenoids expressed as a mass per unit soil surface area (g/m²), a mass per unit leaf area (μg/cm²), and a mass per unit fresh leaf weight (mg/g), respectively. Four methods were compared to retrieve amount of carotenoids: stepwise multiple linear regression (SMLR), published spectral indices, band combination indices, and partial least square regression (PLSR). Results show that maximum sensitivity of reflectance to variation in different units of measurement of carotenoids was found in the green region at 515–550 nm, and at 715 nm and 750 nm regions in the far-red wavelengths. The predictive accuracies of Car (g/m²), Car (μg/cm²) and Car (mg/g) were tested on a validation data set and the results show that the highest R² values between estimations and observations were 0.468 for Car (g/m²), 0.563 for Car (μg/cm²), and 0.456 for Car (mg/g), with relative root mean square error (RMSE%, RMSE/mean) of 48.72%, 22.07% and 21.07%, respectively. Compared to Car (g/m²) and Car (mg/g), the model performance indices for Car (μg/cm²) show a high degree of consistency among the R² values and RMSE% and MAE% values. Further comparison were performed among the estimation accuracies of different unit carotenoids and among the different approaches used in the study by a paired-t-test. The results indicate that although the best estimation results for Car (μg/cm²) and Car (mg/g) were both obtained based on PLSR, they can be estimated by all four adopted methods without significant differences (P > 0.1). Whereas for Car (g/m²), the best estimation results were obtained based on published vegetation indices CIred-edge, which were significantly better than the estimation results based on SMLR (P < 0.000). In summary, the results of this study show that even the carotenoids expressed on concentration (mg/g) or content (μg/cm²) basis at leaf level can be estimated with the same prediction accuracies to the carotenoids expressed as a mass per unit surface area (g/m²) at canopy level using reflectance measurement at canopy level.     

Estimating dissolved organic carbon concentration in turbid coastal waters using optical remote sensing observations

Dissolved Organic Carbon (DOC) is an important component in the global carbon cycle. It also plays an important role in influencing the coastal ocean biogeochemical (BGC) cycles and light environment. Studies focussing on DOC dynamics in coastal waters are data constrained due to the high costs associated with in situ water sampling campaigns. Satellite optical remote sensing has the potential to provide continuous, cost-effective DOC estimates. In this study we used a bio-optics dataset collected in turbid coastal waters of Moreton Bay (MB), Australia, during 2011 to develop a remote sensing algorithm to estimate DOC. This dataset includes data from flood and non-flood conditions. In MB, DOC concentration varied over a wide range (20–520 μM C) and had a good correlation (R² = 0.78) with absorption due to coloured dissolved organic matter (CDOM) and remote sensing reflectance. Using this data set we developed an empirical algorithm to derive DOC concentrations from the ratio of R_rs(412)/R_rs(488) and tested it with independent datasets. In this study, we demonstrate the ability to estimate DOC using remotely sensed optical observations in turbid coastal waters.     

An Accuracy Accessing Approach for Chlorophyll-a Concentration Retrieval Results

In this study, a MDC3A algorithm (Multi-Data Crossing Algorithm for Accuracy Accessing) was developed for accessing the accuracy of chl-a (chlorophyll-a) retrieval model in case of no sufficient available in situ measurements. Three simple estimation algorithms of chl-a concentration, i.e., two-band algorithm, three-band algorithm and four-band algorithm, were used as input dataset of MDC3A algorithm to illuminate its performance. These three simple algorithms were calibrated and validated by calibration and validation dataset collected on October 27–28, 2003. According to model calibration and validation results, it was found that the four-band algorithm (R ²?=?0.8676) had a superior performance to the two-band (R ²?=?0.5061) and three-band (R ²?=?0.5142) algorithm. The uncertainties in modeling prediction of these three simple algorithms were underestimated as 0.07 %, 0.04 % and 8.07 % for calibration dataset and 8.38 %, 9.33 % and 9.37 % for validation dataset by MDC3A algorithm through comparison with in situ measurements. Because the MDC3A algorithm was able to detect the random errors from measured values, but had an inadequate ability to detect systematical errors and gross errors from measured values. The uncertainty estimated by MDC3A algorithm was usually lower than that estimated by in situ measurements.     

A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake

Soft-classification-based methods for estimating chlorophyll-a concentration (C_chla) by satellite remote sensing have shown great potential in turbid coastal and inland waters. However, one of the most important water color sensors, the MEdium Resolution Imaging Spectrometer (MERIS), has not been applied to the study of turbid or eutrophic lakes. In this study, we developed a new soft-classification-based C_chla estimation method using MERIS data for the highly turbid and eutrophic Taihu Lake. We first developed a decision tree to classify Taihu Lake into three optical water types (OWTs) using MERIS reflectance data, which were quasi-synchronous (±3 h) with in situ measured C_chla data from 91 sample stations. Secondly, we used MERIS reflectance and in situ measured C_chla data in each OWT to calibrate the optimal C_chla estimation model for each OWT. We then developed a soft-classification-based C_chla estimation method, which blends the C_chla estimation results in each OWT by a weighted average, where the weight for each MERIS spectra in each OWT is the reciprocal value of the spectral angle distance between the MERIS spectra and the centroid spectra of the OWT. Finally, the soft-classification based C_chla estimation algorithm was validated and compared with no-classification and hard-classification-based methods by the leave-one-out cross-validation (LOOCV) method. The soft-classification-based method exhibited the best performance, with a correlation coefficient (R²), average relative error (ARE), and root-mean-square error (RMSE) of 0.81, 33.8%, and 7.0 μg/L, respectively. Furthermore, the soft-classification-based method displayed smooth values at the edges of OWT boundaries, which resolved the main problem with the hard-classification-based method. The seasonal and annual variations of C_chla were computed in Taihu Lake from 2003 to 2011, and agreed with the results of previous studies, further indicating the stability of the algorithm. We therefore propose that the soft-classification-based method can be effectively used in Taihu Lake, and that it has the potential for use in other optically-similar turbid and eutrophic lakes, and using spectrally-similar satellite sensors.     

Predicting Thaumastocoris peregrinus damage using narrow band normalized indices and hyperspectral indices using field spectra resampled to the Hyperion sensor

Thaumastocoris peregrinus (T. peregrinus) is a sap sucking insect that feeds on Eucalyptus leaves. It poses a threat to the forest industry by reducing the photosynthetic ability of the tree, resulting in stunted growth and even death of severely infested trees. Remote sensing techniques offer the potential to detect and map T. peregrinus infestations in plantation forests using current operational hyperspectral scanners. This study resampled field spectral data measured from a field spectrometer to the band settings of the Hyperion sensor in order to assess its potential in predicting T. peregrinus damage. Normalized indices based on NDVI ratios were calculated using the resampled visible and near-infrared bands of the Hyperion sensor to assess its utility in predicting T. peregrinus damage using Partial Least Squares (PLS) regression. The top 20 normalized indices were based on specific biochemical absorption features that predicted T. peregrinus damage with a mean bootstrapped R² value of 0.63 on an independent test dataset. The top 20 indices were located in the near-infrared region between 803.3 nm and 894.9 nm. Twenty three previously published hyperspectral indices which have been used to assess stress in vegetation were also used to predict T. peregrinus damage and resulted in a mean bootstrapped R² value of 0.59 on an independent test dataset. The datasets were combined to assess its collective strength in predicting T. peregrinus damage and significant indices were chosen based on variable importance scores (VIP) and were then entered into a PLS model. The indices chosen by VIP predicted T. peregrinus damage with a mean bootstrapped R² value of 0.71 on an independent test dataset. A greedy backward variable selection model was further tested on the VIP selected indices in order to find the best subset of indices with the best predictive accuracy. The greedy backward variable selection model identified 3 indices and performed the best by predicting damage with an R² value of 0.74 with the lowest RMSE of 1.30% on an independent test dataset. The best three indices identified include the anthocyanin reflectance index, carotenoid reflectance index and the normalized index calculated at 864.4 and 884.7 nm. Individual relationships between these indices and T. peregrinus damage indicate that high correlations are obtained with the inclusion of a few severely infested trees in the sample size. When the severely infested trees were removed from the study, the normalized index (864.4 and 884.7 nm) and the anthocyanin reflectance index still yielded significant correlations at the 99% confidence interval. This study indicates the significance of normalized indices and spectral indices calculated from the visible and near-infrared bands in hyperspectral data for the prediction of T. peregrinus damage.     

基于高光谱遥感数据提取森林结构参数的研究

以位于三峡库区的龙门河森林自然保护区为研究区,综合利用线性光谱混合模型和几何光学模型,基于高光谱遥感数据提取森林结构参数是本文研究的重点。在研究区地面调查数据的基础上,通过高光谱数据和混合光谱分解法,获得反演几何光学模型所需的四分量参数,根据背景光照分量与森林植被冠层各参数间的关系,反演得到森林冠层郁闭度及平均冠幅的定量分布图,并利用37个野外实测样本进行结果验证。     

Estimating aboveground and organ biomass of plant canopies across the entire season of rice growth with terrestrial laser scanning

Non-destructive and accurate estimation of crop biomass is crucial for the quantitative diagnosis of growth status and timely prediction of grain yield. As an active remote sensing technique, terrestrial laser scanning (TLS) has become increasingly available in crop monitoring for its advantages in recording structural properties. Some researchers have attempted to use TLS data in the estimation of crop aboveground biomass, but only for part of the growing season. Previous studies rarely investigated the estimation of biomass for individual organs, such as the panicles in rice canopies, which led to the poor understanding of TLS technology in monitoring biomass partitioning among organs. The objective of this study was to investigate the potential of TLS in estimating the biomass for individual organs and aboveground biomass of rice and to examine the feasibility of developing universal models for the entire growing season. The field plots experiments were conducted in 2017 and 2018 and involved different nitrogen (N) rates, planting techniques and rice varieties. Three regression approaches, stepwise multiple linear regression (SMLR), random forest regression (RF) and linear mixed-effects (LME) modeling, were evaluated in estimating biomass with extensive TLS and biomass data collected at multiple phenological stages of rice growth across the entire season. The models were calibrated with the 2017 dataset and validated independently with the 2018 dataset.The results demonstrated that growth stage in LME modeling was selected as the most significant random effect on rice growth among the three candidates, which were rice variety, growth stage and planting technique. The LME models grouped by growth stage exhibited higher validation accuracies for all biomass variables over the entire season to varying degrees than SMLR models and RF models. The most pronounced improvement with a LME model was obtained for panicle biomass, with an increase of 0.74 in R² (LME: R² = 0.90, SMLR: R² = 0.16) and a decrease of 1.15 t/ha in RMSE (LME: RMSE =0.79 t/ha, SMLR: RMSE =2.94 t/ha). Compared to SMLR and RF, LME modeling yielded similar estimation accuracies of aboveground biomass for pre-heading stages, but significantly higher accuracies for post-heading stages (LME: R² = 0.63, RMSE =2.27 t/ha; SMLR: R² = 0.42, RMSE =2.42 t/ha; RF: R² = 0.57, RMSE =2.80 t/ha). These findings implied that SMLR was only suitable for the estimation of biomass at pre-heading stages and LME modeling performed remarkably well across all growth stages, especially for post-heading. The results suggest coupling TLS with LME modeling is a promising approach to monitoring rice biomass at post-heading stages at high accuracy and to overcoming the saturation of canopy reflectance signals encountered in optical remote sensing. It also has great potential in the monitoring of other crops in cloud-cover conditions and the instantaneous prediction of grain yield any time before harvest.     

A three-band semi-analytical model for deriving total suspended sediment concentration from HJ-1A/CCD data in turbid coastal waters

The accurate assessment of total suspended sediment (TSM) concentration in coastal waters by means of remote sensing is quite challenging, due to the optical complexity and significant variability of these waters. In this study, three-band semi-analytical TSM retrieval (TSTM) model with HJ-1A/CCD spectral bands was developed for the retrieval of TSM concentration from turbid coastal waters. This model was calibrated and validated by means of one calibration dataset and three independent validation datasets obtained from three different turbid waters. It was found that the TSTM model may be used to retrieve accurate TSM concentration data from highly turbid waters without the spectral slope of the model requiring further optimization. Finally, the TSM concentration data were quantified from the HJ-1A/CCD images after atmospheric correction using the dark-object subtraction technique. Upon comparing the model-derived and field-measured TSM concentration data, it was observed that the TSTM model produced <29% uncertainty in deriving TSM concentration from the HJ-1A/CCD data. These findings imply that the TSTM model may be used for the quantitative monitoring of TSM concentration in coastal waters, provided that the atmospheric correction scheme for the HJ-1A/CCD imagery is available.     

Comparing methods for mapping canopy chlorophyll content in a mixed mountain forest using Sentinel-2 data

The Sentinel-2 Multi-Spectral Imager (MSI) has three spectral bands centered at 705, 740, and 783 nm wavelengths that exploit the red-edge information useful for quantifying plant biochemical traits. This sensor configuration is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving canopy chlorophyll content (CCC) from Sentinel-2 in a heterogeneous mixed mountain forest. Amongst the algorithms presented in the literature, 13 different vegetation indices (VIs), a non-parametric statistical approach, and two radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field campaign was conducted in July 2017 to collect in situ measurements of CCC in Bavarian forest national park, and the cloud-free Sentinel-2 image was acquired on 13 July 2017. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches, such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. As such the modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m² (R² = 0.75). The PLSR resulted in the highest R² of 0.78, and slightly higher RMSE =0.22 g/m² than mSR3. The physical-based approach-INFORM inversion using look-up table resulted in an RMSE =0.31 g/m², and R² = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for long-term terrestrial ecosystems monitoring across the globe.     

Retrieval of leaf water content spanning the visible to thermal infrared spectra

The objective of this study was to investigate the entire spectra (from visible to the thermal infrared; 0.390–14.0 μm) to retrieve leaf water content in a consistent manner. Narrow-band spectral indices (calculated from all possible two band combinations) and a partial least square regression (PLSR) were used to assess the strength of each spectral region. The coefficient of determination (R²) and root mean square error (RMSE) were used to report the prediction accuracy of spectral indices and PLSR models. In the visible-near infrared and shortwave infrared (VNIR–SWIR), the most accurate spectral index yielded R² of 0.89 and RMSE of 7.60%, whereas in the mid infrared (MIR) the highest R² was 0.93 and RMSE of 5.97%. Leaf water content was poorly predicted using two-band indices developed from the thermal infrared (R² = 0.33). The most accurate PLSR model resulted from MIR reflectance spectra (R² = 0.96, RMSE = 4.74% and RMSE cross validation RMSE_CV = 6.17%) followed by VNIR–SWIR reflectance spectra (R² = 0.91, RMSE = 6.90% and RMSE_CV = 7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R² = 0.67, RMSE = 13.27% and RMSE_CV = 16.39%). This study demonstrated that the mid infrared (MIR) and shortwave infrared (SWIR) domains were the most sensitive spectral region for the retrieval of leaf water content.     

Evaluation of EO-1 hyperion data for agricultural applications

The present study was carried out to evaluate the satellite-based hyperspectral data available from Hyperion onboard EO-1 of NASA for agricultural applications. The study was carried out for Daurala block of Meerut district, using data of March 2005. The preliminary data analysis showed that there are 196 usable bands out of a total of 242 bands. Principal component (PC) analysis showed that about 99% of the information explained in 10 PCs. The atmospherically corrected reflectance, derived from satellite data had good agreement with the ground reflectance, observed using handheld spectroradiometer, with r² ranging from 0.85 to 0.98. A set of twenty most usable bands was selected by the criteria of maximum contribution to first five PCs and the band combinations with least inter-band correlations.     

Comparison of ocean color chlorophyll algorithms for IRS-P4 OCM sensor usingin-situ data

In-situ chlorophyll concentration data and remote sensing reflectance (R_rs) measurements collected in six different ship campaigns in the Arabian Sea were used to evaluate the accuracy, precision, and suitability of different ocean color chlorophyll algorithms for the Arabian Sea. The bio-optical data sets represent the typical range of biooptical conditions expected in this region and are composed of 47 stations encompassing chlorophyll concentration, between 0.072 and 5.90 mg m^-3, with 43 observations in case I water and 4 observations in case II water. Six empirical chlorophyll algorithms [i.e. Aiken-C, POLDER-C, OCTS-C, Morel-3, Ocean Chlorophyll-2 (OC2) and Ocean Chlorophyll-4 (OC4)] were selected for analysis on the Arabian Sea data set. Numerous statistical and graphical criterions were used to evaluate the performance of these algorithms. Among these six chlorophyll algorithms two chlorophyll algorithms (i.e. OC2 and OC4) performed well in the case I waters of the Arabian Sea. The OC2 algorithm, a modified cubic polynomial function which uses ratio of R_rs490 nm and R_rs555 nm (where, R_rs is remote sensing reflectance), performed well with r²=0.85; rms =0.15. The OC4 algorithm, a four-band (443, 490, 510, 555 nm), maximum band ratio formulation was found best on the basis of statistical analysis results with r²=0.85 and rms=0.14. Both OC2 and OC4 algorithms failed to estimate chlorophyll inTrichodesmium dominated waters. The OC2 algorithm was preferred over OC4 algorithm for routine processing of the OCM data to generate chlorophyll-a images, as it uses a band ratio of 490/555 nm and atmospheric correction is more accurate in 490 nm compared to 443 nm band, which is used by OC4 algorithm.     

A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation

ABSTRACT

We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004–2008) to the EO-1 drift period (2011–2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14° (2015) and 22° (2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen’s r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.