共查询到20条相似文献，搜索用时 31 毫秒
1.
A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAIL) alone (R2 = 0.77 and RMSE = 231 g m−2). Assimilation of GLAI derived from the fusion dataset (GLAIF) resulted in a R2 of 0.71 and RMSE of 193 g m−2 while assimilating the combination of GLAIL and GLAIF led to further improvements (R2 = 0.76 and RMSE = 176 g m−2). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited. 相似文献
2.
3.
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 RMSECV = 6.17%) followed by VNIR–SWIR reflectance spectra (R2 = 0.91, RMSE = 6.90% and RMSECV = 7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R2 = 0.67, RMSE = 13.27% and RMSECV = 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. 相似文献
4.
This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT + SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m2m−2, defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm−2, defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI × DMC was regarded as the estimated grassland AGB according to their definitions. The well-known ill-posed inversion problem when inverting PROSAILH was alleviated using ecological criteria to constrain the simulation scenario and therefore the number of simulated spectra. A case study of the presented method was applied to a plateau grassland in China to estimate its AGB. The results were compared to those obtained using an exponential regression, a partial least squares regression (PLSR) and an artificial neural networks (ANN). The RTM-based method offered higher accuracy (R2 = 0.64 and RMSE = 42.67 gm−2) than the exponential regression (R2 = 0.48 and RMSE = 41.65 gm−2) and the ANN (R2 = 0.43 and RMSE = 46.26 gm−2). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R2 = 0.55) but higher RMSE (RMSE = 37.79 gm−2). Although it is still necessary to test these methodologies in other areas, the RTM-based method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology. 相似文献
5.
Surface soil moisture (SSM) is a critical variable for understanding the energy and water exchange between the land and atmosphere. A multi-linear model was recently developed to determine SSM using ellipse variables, namely, the center horizontal coordinate (x0), center vertical coordinate (y0), semi-major axis (a) and rotation angle (θ), derived from the elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). However, the multi-linear model has a major disadvantage. The model coefficients are calculated based on simulated data produced by a land surface model simulation that requires sufficient meteorological measurements. This study aims to determine the model coefficients directly using limited meteorological parameters rather than via the complicated simulation process, decreasing the dependence of the model coefficients on meteorological measurements. With the simulated data, a practical algorithm was developed to estimate SSM based on combined optical and thermal infrared data. The results suggest that the proposed approach can be used to determine the coefficients associated with all ellipse variables based on historical meteorological records, whereas the constant term varies daily and can only be determined using the daily maximum solar radiation in a prediction model. Simulated results from three FLUXNET sites over 30 cloud-free days revealed an average root mean square error (RMSE) of 0.042 m3/m3 when historical meteorological records were used to synchronously determine the model coefficients. In addition, estimated SSM values exhibited generally moderate accuracies (coefficient of determination R2 = 0.395, RMSE = 0.061 m3/m3) compared to SSM measurements at the Yucheng Comprehensive Experimental Station. 相似文献
6.
Leaf carotenoids content (LCar) is an important indicator of plant physiological status. Accurate estimation of LCar provides valuable insight into early detection of stress in vegetation. With spectroscopy techniques, a semi-empirical approach based on spectral indices was extensively used for carotenoids content estimation. However, established spectral indices for carotenoids that generally rely on limited measured data, might lack predictive accuracy for carotenoids estimation in various species and at different growth stages. In this study, we propose a new carotenoid index (CARI) for LCar assessment based on a large synthetic dataset simulated from the leaf radiative transfer model PROSPECT-5, and evaluate its capability with both simulated data from PROSPECT-5 and 4SAIL and extensive experimental datasets: the ANGERS dataset and experimental data acquired in field experiments in China in 2004. Results show that CARI was the index most linearly correlated with carotenoids content at the leaf level using a synthetic dataset (R2 = 0.943, RMSE = 1.196 μg/cm2), compared with published spectral indices. Cross-validation results with CARI using ANGERS data achieved quite an accurate estimation (R2 = 0.545, RMSE = 3.413 μg/cm2), though the RBRI performed as the best index (R2 = 0.727, RMSE = 2.640 μg/cm2). CARI also showed good accuracy (R2 = 0.639, RMSE = 1.520 μg/cm2) for LCar assessment with leaf level field survey data, though PRI performed better (R2 = 0.710, RMSE = 1.369 μg/cm2). Whereas RBRI, PRI and other assessed spectral indices showed a good performance for a given dataset, overall their estimation accuracy was not consistent across all datasets used in this study. Conversely CARI was more robust showing good results in all datasets. Further assessment of LCar with simulated and measured canopy reflectance data indicated that CARI might not be very sensitive to LCar changes at low leaf area index (LAI) value, and in these conditions soil moisture influenced the LCar retrieval accuracy. 相似文献
7.
Soil respiration (Rs) is of great importance to the global carbon balance. Remote sensing of Rs is challenging because of (1) the lack of long-term Rs data for model development and (2) limited knowledge of using satellite-based products to estimate Rs. Using 8-years (2002–2009) of continuous Rs measurements with nonsteady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that Rs was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between Rs and NDVI × LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled Rs closely tracked the seasonal patterns of measured Rs and explained 74–92% of the variance in Rs with a root mean square error (RMSE) less than 1.0 g C/m2/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of Rs with an overall R2 of 0.78 (p < 0.001) for data of these two sites. Consequently, we mapped Rs of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of Rs were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in Rs. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of Rs. 相似文献
8.
The aim of this study is to estimate the capabilities of forecasting the yield of wheat using an artificial neural network combined with multi-temporal satellite data acquired at high spatial resolution throughout the agricultural season in the optical and/or microwave domains. Reflectance (acquired by Formosat-2, and Spot 4–5 in the green, red, and near infrared wavelength) and multi-configuration backscattering coefficients (acquired by TerraSAR-X and Radarsat-2 in the X- and C-bands, at co- (abbreviated HH and VV) and cross-polarization states (abbreviated HV and VH)) constitute the input variable of the artificial neural networks, which are trained and validated on the successively acquired images, providing yield forecast in near real-time conditions. The study is based on data collected over 32 fields of wheat distributed over a study area located in southwestern France, near Toulouse. Among the tested sensor configurations, several satellite data appear useful for the yield forecasting throughout the agricultural season (showing coefficient of determination (R2) larger than 0.60 and a root mean square error (RMSE) lower than 9.1 quintals by hectare (q ha−1)): CVH, CHV, or the combined used of XHH and CHH, CHH and CHV, or green reflectance and CHH. Nevertheless, the best accurate forecast (R2 = 0.76 and RMSE = 7.0 q ha−1) is obtained longtime before the harvest (on day 98, during the elongation of stems) using the combination of co- and cross-polarized backscattering coefficients acquired in the C-band (CVV and CVH). These results highlight the high interest of using synthetic aperture radar (SAR) data instead of optical ones to early forecast the yield before the harvest of wheat. 相似文献
9.
This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (HR, and NRp, p = H,V) in the Soil Moisture (SM) retrieval from L-band passive microwave observations using the L-band Microwave Emission of the Biosphere (L-MEB) model. In the current Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2), v620, and Level 3 (L3), v300, SM retrieval algorithms, low vegetated areas are parameterized by ω = 0 and HR = 0.1, whereas values of ω = 0.06 − 0.08 and HR = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, HR and NRp, p = H,V) were tested in this study, treating SMOS SM retrievals as homogeneous over each pixel instead of retrieving SM over a representative fraction of the pixel, as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω = 0.10, HR = 0.4 and NRp = −1 (p = H,V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and HR varied, respectively, in the range 0.08–0.12 and 0.1–0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics (R = 0.61, bias = −0.019 m3 m−3, ubRMSE = 0.062 m3 m−3 for the IGBP-based calibration; against R = 0.54, bias = −0.034 m3 m−3 and ubRMSE = 0.070 m3 m−3 for the SMOS L3 SM product). This result is a key step in the calibration of the roughness and vegetation parameters in the operational SMOS retrieval algorithm. The approach presented here is the core of a new forthcoming SMOS optimized SM product. 相似文献
10.
Soil respiration (Rs) data from 45 plots were used to estimate the spatial patterns of Rs during the peak growing seasons of winter wheat and summer maize in Julu County, North China, by combining satellite remote sensing data, field-measured data, and a support vector regression (SVR) model. The observed Rs values were well reproduced by the model at the plot scale, with a root-mean-square error (RMSE) of 0.31 μmol CO2 m−2 s−1 and a coefficient of determination (R2) of 0.73. No significant difference was detected between the prediction accuracy of the SVR model for winter wheat and summer maize. With forcing from satellite remote sensing data and gridded soil property data, we used the SVR model to predict the spatial distributions of Rs during the peak growing seasons of winter wheat and summer maize rotation croplands in Julu County. The SVR model captured the spatial variations of Rs at the county scale. The satellite-derived enhanced vegetation index was found to be the most important input used to predict Rs. Removal of this variable caused an RMSE increase from 0.31 μmol CO2 m−2 s−1 to 0.42 μmol CO2 m−2 s−1. Soil properties such as soil organic carbon (SOC) content and soil bulk density (SBD) were the second most important factors. Their removal led to an RMSE increase from 0.31 μmol CO2 m−2 s−1 to 0.37 μmol CO2 m−2 s−1. The SVR model performed better than multiple regression in predicting spatial variations of Rs in winter wheat and summer maize rotation croplands, as shown by the comparison of the R2 and RMSE values of the two algorithms. The spatial patterns of Rs are better captured using the SVR model than performing multiple regression, particularly for the relatively high and relatively low Rs values at the center and northeast study areas. Therefore, SVR shows promise for predicting spatial variations of Rs values on the basis of remotely sensed data and gridded soil property data at the county scale. 相似文献
11.
Wetland biomass is essential for monitoring the stability and productivity of wetland ecosystems. Conventional field methods to measure or estimate wetland biomass are accurate and reliable, but expensive, time consuming and labor intensive. This research explored the potential for estimating wetland reed biomass using a combination of airborne discrete-return Light Detection and Ranging (LiDAR) and hyperspectral data. To derive the optimal predictor variables of reed biomass, a range of LiDAR and hyperspectral metrics at different spatial scales were regressed against the field-observed biomasses. The results showed that the LiDAR-derived H_p99 (99th percentile of the LiDAR height) and hyperspectral-calculated modified soil-adjusted vegetation index (MSAVI) were the best metrics for estimating reed biomass using the single regression model. Although the LiDAR data yielded a higher estimation accuracy compared to the hyperspectral data, the combination of LiDAR and hyperspectral data produced a more accurate prediction model for reed biomass (R2 = 0.648, RMSE = 167.546 g/m2, RMSEr = 20.71%) than LiDAR data alone. Thus, combining LiDAR data with hyperspectral data has a great potential for improving the accuracy of aboveground biomass estimation. 相似文献
12.
Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA).Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R2: 0.51–0.82, p < 0.0001). The best robustness (R2 = 0.74, RMSE = 18.97 g/m2 and Bias = 0.12 g/m2) was obtained using a combination of two low-scale features (1639 nm, scale 4) and (2133 nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the NDLMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368 nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340 nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or spaceborne imaging spectroscopy data for mapping canopy foliar dry biomass. 相似文献
13.
There are increasing societal and plant industry demands for more accurate, objective and near real-time crop production information to meet both economic and food security concerns. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to monitor large agricultural areas at acceptable pixel scale, cost and accuracy. Fitting parametric profiles to growing season vegetation index time series reduces the volume of data and provides simple quantitative parameters that relates to crop phenology (sowing date, flowering). In this study, we modelled various Gaussian profiles to time sequential MODIS enhanced vegetation index (EVI) images over winter crops in Queensland, Australia. Three simple Gaussian models were evaluated in their effectiveness to identify and classify various winter crop types and coverage at both pixel and regional scales across Queensland's main agricultural areas. Equal to or greater than 93% classification accuracies were obtained in determining crop acreage estimates at pixel scale for each of the Gaussian modelled approaches. Significant high to moderate correlations (log-linear transformation) were also obtained for determining total winter crop (R2 = 0.93) areas as well as specific crop acreage for wheat (R2 = 0.86) and barley (R2 = 0.83). Conversely, it was much more difficult to predict chickpea acreage (R2 ≤ 0.26), mainly due to very large uncertainties in survey data. The quantitative approach utilised here further had additional benefits of characterising crop phenology in terms of length of growing season and providing regression diagnostics of how well the fitted profiles matched the EVI time series. The Gaussian curve models utilised here are novel in application and therefore will enhance the use and adoption of remote sensing technologies in targeted agricultural application. With innate simplicity and accuracies comparable to other more convoluted multi-temporal approaches it is a good candidate in determining total and specific crop acreage estimates in future national and global food security frameworks. 相似文献
14.
Tracking water level fluctuations in small lakes and reservoirs is important in order to better understand and manage these ecosystems. A geographic object-based image analysis (GEOBIA) method using very high spatial and temporal resolution optical (Pléiades) and radar (COSMO-SkyMed and TerraSAR-X) remote sensing imagery is presented here which (1) tracks water level fluctuations via variations in water surface area and (2) avoids common difficulties found in using single-band radar images for water-land image classification. Results are robust, with over 98% of image surface area correctly classified into land or water, R2 = 0.963 and RMSE = 0.42 m for a total water level fluctuation range of 5.94 m. Multispectral optical imagery is found to be more straightforward in producing results than single-band radar imagery, but the latter crucially increase temporal resolution to the point where fluctuations can be satisfactorily tracked in time. Moreover, an analysis suggests that high and medium spatial resolution imagery is sufficient, in at least some cases, in tracking the water level fluctuations of small inland reservoirs. Finally, limitations of the methodology presented here are briefly discussed along with potential solutions to overcome them. 相似文献
15.
National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux between land and atmosphere. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale as well as national and continental scales. Existing satellite-based NPP products tend to underestimate NPP on croplands. An Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP over large multi-state regions. The method is documented here and evaluated for corn (Zea mays L.) and soybean (Glycine max L. Merr.) in Iowa and Illinois in 2006 and 2007. The method includes a crop-specific Enhanced Vegetation Index (EVI), shortwave radiation data estimated using the Mountain Climate Simulator (MTCLIM) algorithm, and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that corresponds to the Cropland Data Layer (CDL) land cover product. Results from the modeling framework captured the spatial NPP gradient across croplands of Iowa and Illinois, and also represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 917 g C m−2 yr−1 and 409 g C m−2 yr−1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Site comparisons with flux tower data show AgI-LUE NPP in close agreement with tower-derived NPP, lower than inventory-based NPP, and higher than MOD17A3 NPP. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents. 相似文献
16.
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 (Rrs) 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 Rrs 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. 相似文献
17.
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 (ag(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 ag(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 RMSElog = 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. 相似文献
18.
A statistical relationship between canopy mass-based foliar nitrogen concentration (%N) and canopy bidirectional reflectance factor (BRF) has been repeatedly demonstrated. However, the interaction between leaf properties and canopy structure confounds the estimation of foliar nitrogen. The canopy scattering coefficient (the ratio of BRF and the directional area scattering factor, DASF) has recently been suggested for estimating %N as it suppresses the canopy structural effects on BRF. However, estimation of %N using the scattering coefficient has not yet been investigated for longer spectral wavelengths (>855 nm). We retrieved the canopy scattering coefficient for wavelengths between 400 and 2500 nm from airborne hyperspectral imagery, and then applied a continuous wavelet analysis (CWA) to the scattering coefficient in order to estimate %N. Predictions of %N were also made using partial least squares regression (PLSR). We found that %N can be accurately retrieved using CWA (R2 = 0.65, RMSE = 0.33) when four wavelet features are combined, with CWA yielding a more accurate estimation than PLSR (R2 = 0.47, RMSE = 0.41). We also found that the wavelet features most sensitive to %N variation in the visible region relate to chlorophyll absorption, while wavelet features in the shortwave infrared regions relate to protein and dry matter absorption. Our results confirm that %N can be retrieved using the scattering coefficient after correcting for canopy structural effect. With the aid of high-fidelity airborne or upcoming space-borne hyperspectral imagery, large-scale foliar nitrogen maps can be generated to improve the modeling of ecosystem processes as well as ecosystem-climate feedbacks. 相似文献
19.
Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350–2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, Rv2 = 0.54; root mean square error in validation, RMSEv = 12.99 mg kg−1; and residual prediction deviation in validation, RPDv = 1.35) was slightly better than using the soil spectra (Rv2 = 0.42, RMSEv = 13.35 mg kg−1, and RPDv = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (Rv2 = 0.63, RMSEv = 11.94 mg kg−1, RPDv = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and 2290 nm, leaf spectral bands near 700, 890 and 900 nm in PLSR models were important wavelengths for soil arsenic prediction. Moreover, soil arsenic showed significantly positive correlations with soil organic matter (r = 0.62, p < 0.01) and leaf arsenic (r = 0.77, p < 0.01), and a significantly negative correlation with leaf chlorophyll (r = −0.67, p < 0.01). The results showed that the prediction of arsenic contents using soil and leaf spectra may be based on their relationships with soil organic matter and leaf chlorophyll contents, respectively. Although RPD of 1.47 was below the recommended RPD of >2 for soil analysis, arsenic prediction in agricultural soils can be improved by combining the leaf and soil spectra. 相似文献
20.
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 Rrs(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 (APDm) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr−1, whereas the Rrs(λ) uncertainty at 412 nm is the highest (APDm 47–62%, RMS 0.0027–0.0041 sr−1). The uncertainties of the aerosol optical thickness (AOT) τa, diffuse attenuation coefficient for downward irradiance at 490 nm Kd(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 Kd(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. 相似文献