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.     

A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm

This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (H_R, and N_Rp, 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 H_R = 0.1, whereas values of ω = 0.06 − 0.08 and H_R = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, H_R and N_Rp, 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, H_R = 0.4 and N_Rp = −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 H_R 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.     

Upscaling plot-scale soil respiration in winter wheat and summer maize rotation croplands in Julu County,North China

Soil respiration (R_s) data from 45 plots were used to estimate the spatial patterns of R_s 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 R_s 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 R_s during the peak growing seasons of winter wheat and summer maize rotation croplands in Julu County. The SVR model captured the spatial variations of R_s at the county scale. The satellite-derived enhanced vegetation index was found to be the most important input used to predict R_s. 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 R_s 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 R_s are better captured using the SVR model than performing multiple regression, particularly for the relatively high and relatively low R_s values at the center and northeast study areas. Therefore, SVR shows promise for predicting spatial variations of R_s values on the basis of remotely sensed data and gridded soil property data at the county scale.     

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.     

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

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 (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F 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.     

Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants

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, R_v² = 0.54; root mean square error in validation, RMSE_v = 12.99 mg kg⁻¹; and residual prediction deviation in validation, RPD_v = 1.35) was slightly better than using the soil spectra (R_v² = 0.42, RMSE_v = 13.35 mg kg⁻¹, and RPD_v = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (R_v² = 0.63, RMSE_v = 11.94 mg kg⁻¹, RPD_v = 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.     

Detecting forest damage after a low-severity fire using remote sensing at multiple scales

Remote sensing technologies are an ideal platform to examine the extent and impact of fire on the landscape. In this study we assess that capacity of the RapidEye constellation and Landsat (Thematic Mapper and Operational Land Imager to map fine-scale burn attributes for a small, low severity prescribed fire in a dry Western Canadian forest. Estimates of burn severity from field data were collated into a simple burn index and correlated with a selected suite of common spectral vegetation indices. Burn severity classes were then derived to map fire impacts and estimate consumed woody surface fuels (diameter ≥2.6 cm). All correlations between the simple burn index and vegetation indices produced significant results (p < 0.01), but varied substantially in their overall accuracy. Although the Landsat Soil Adjusted Vegetation Index provided the best regression fit (R² = 0.56), results suggested that RapidEye provided much more spatially detailed estimates of tree damage (Soil Adjusted Vegetation Index, R² = 0.51). Consumption estimates of woody surface fuels ranged from 3.38 ± 1.03 Mg ha⁻¹ to 11.73 ± 1.84 Mg ha⁻¹, across four derived severity classes with uncertainties likely a result of changing foliage moisture between the before and after fire images. While not containing spectral information in the short wave infrared, the spatial variability provided by the RapidEye imagery has potential for mapping and monitoring fine scale forest attributes, as well as the potential to resolve fire damage at the individual tree level.     

Estimating crop net primary production using national inventory data and MODIS-derived parameters

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.     

A visible band index for remote sensing leaf chlorophyll content at the canopy scale

Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. The triangular greenness index (TGI) was developed based on the area of a triangle surrounding the spectral features of chlorophyll with points at (670 nm, R₆₇₀), (550 nm, R₅₅₀), and (480 nm, R₄₈₀), where R_λ is the spectral reflectance at wavelengths of 670, 550 and 480, respectively. The equation is TGI = −0.5[(670 − 480)(R₆₇₀ − R₅₅₀) − (670 − 550)(R₆₇₀ − R₄₈₀)]. In 1999, investigators funded by NASA's Earth Observations Commercialization and Applications Program collaborated on a nitrogen fertilization experiment with irrigated maize in Nebraska. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and Landsat 5 Thematic Mapper (TM) data were acquired along with leaf chlorophyll meter and other data on three dates in July during late vegetative growth and early reproductive growth. TGI was consistently correlated with plot-averaged chlorophyll-meter values at the spectral resolutions of AVIRIS, Landsat TM, and digital cameras. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high LAI and canopy closure, TGI was only affected by leaf chlorophyll content. Therefore, TGI may be the best spectral index to detect crop nitrogen requirements with low-cost digital cameras mounted on low-altitude airborne platforms.     

A radiative transfer model-based method for the estimation of grassland aboveground biomass

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.     

Modeling biophysical properties of broad-leaved stands in the hyrcanian forests of Iran using fused airborne laser scanner data and ultraCam-D images

Inventories of mixed broad-leaved forests of Iran mainly rely on terrestrial measurements. Due to rapid changes and disturbances and great complexity of the silvicultural systems of these multilayer forests, frequent repetition of conventional ground-based plot surveys is often cost prohibitive. Airborne laser scanning (ALS) and multispectral data offer an alternative or supplement to conventional inventories in the Hyrcanian forests of Iran. In this study, the capability of a combination of ALS and UltraCam-D data to model stand volume, tree density, and basal area using random forest (RF) algorithm was evaluated. Systematic sampling was applied to collect field plot data on a 150 m × 200 m sampling grid within a 1100 ha study area located at 36°38′- 36°42′N and 54°24′–54°25′E. A total of 308 circular plots (0.1 ha) were measured for calculation of stand volume, tree density, and basal area per hectare. For each plot, a set of variables was extracted from both ALS and multispectral data. The RF algorithm was used for modeling of the biophysical properties using ALS and UltraCam-D data separately and combined. The results showed that combining the ALS data and UltraCam-D images provided a slight increase in prediction accuracy compared to separate modeling. The RMSE as percentage of the mean, the mean difference between observed and predicted values, and standard deviation of the differences using a combination of ALS data and UltraCam-D images in an independent validation at 0.1-ha plot level were 31.7%, 1.1%, and 84 m³ ha⁻¹ for stand volume; 27.2%, 0.86%, and 6.5 m² ha⁻¹ for basal area, and 35.8%, −4.6%, and 77.9 n ha⁻¹ for tree density, respectively. Based on the results, we conclude that fusion of ALS and UltraCam-D data may be useful for modeling of stand volume, basal area, and tree density and thus gain insights into structural characteristics in the complex Hyrcanian forests.     

Vegetation chlorophyll estimates in the Amazon from multi-angle MODIS observations and canopy reflectance model

As a preparatory study for future hyperspectral missions that can measure canopy chemistry, we introduce a novel approach to investigate whether multi-angle Moderate Resolution Imaging Spectroradiometer (MODIS) data can be used to generate a preliminary database with long-term estimates of chlorophyll. MODIS monthly chlorophyll estimates between 2000 and 2015, derived from a fully coupled canopy reflectance model (ProSAIL), were inspected for consistency with eddy covariance fluxes, tower-based hyperspectral images and chlorophyll measurements. MODIS chlorophyll estimates from the inverse model showed strong seasonal variations across two flux-tower sites in central and eastern Amazon. Marked increases in chlorophyll concentrations were observed during the early dry season. Remotely sensed chlorophyll concentrations were correlated to field measurements (r² = 0.73 and r² = 0.98) but the data deviated from the 1:1 line with root mean square errors (RMSE) ranging from 0.355 μg cm⁻² (Tapajós tower) to 0.470 μg cm⁻² (Manaus tower). The chlorophyll estimates were consistent with flux tower measurements of photosynthetically active radiation (PAR) and net ecosystem productivity (NEP). We also applied ProSAIL to mono-angle hyperspectral observations from a camera installed on a tower to scale modeled chlorophyll pigments to MODIS observations (r² = 0.73). Chlorophyll pigment concentrations (Chl_A+B) were correlated to changes in the amount of young and mature leaf area per month (0.59 ≤  r² ≤  0.64). Increases in MODIS observed Chl_A+B were preceded by increased PAR during the dry season (0.61 ≤ r² ≤  0.62) and followed by changes in net carbon uptake. We conclude that, at these two sites, changes in LAI, coupled with changes in leaf chlorophyll, are comparable with seasonality of plant productivity. Our results allowed the preliminary development of a 15-year time series of chlorophyll estimates over the Amazon to support canopy chemistry studies using future hyperspectral sensors.     

Forecast of wheat yield throughout the agricultural season using optical and radar satellite images

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)): C_VH, C_HV, or the combined used of X_HH and C_HH, C_HH and C_HV, or green reflectance and C_HH. 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 (C_VV and C_VH). 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.     

A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data

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.     

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.     

Timber production assessment of a plantation forest: An integrated framework with field-based inventory,multi-source remote sensing data and forest management history

Timber production is the purpose for managing plantation forests, and its spatial and quantitative information is critical for advising management strategies. Previous studies have focused on growing stock volume (GSV), which represents the current potential of timber production, yet few studies have investigated historical process-harvested timber. This resulted in a gap in a synthetical ecosystem service assessment of timber production. In this paper, we established a Management Process–based Timber production (MPT) framework to integrate the current GSV and the harvested timber derived from historical logging regimes, trying to synthetically assess timber production for a historical period. In the MPT framework, age-class and current GSV determine the times of historical thinning and the corresponding harvested timber, by using a “space-for-time” substitution. The total timber production can be estimated by the historical harvested timber in each thinning and the current GSV. To test this MPT framework, an empirical study on a larch plantation (LP) with area of 43,946 ha was conducted in North China for a period from 1962 to 2010. Field-based inventory data was integrated with ALOS PALSAR (Advanced Land-Observing Satellite Phased Array L-band Synthetic Aperture Radar) and Landsat-8 OLI (Operational Land Imager) data for estimating the age-class and current GSV of LP. The random forest model with PALSAR backscatter intensity channels and OLI bands as input predictive variables yielded an accuracy of 67.9% with a Kappa coefficient of 0.59 for age-class classification. The regression model using PALSAR data produced a root mean square error (RMSE) of 36.5 m³ ha⁻¹. The total timber production of LP was estimated to be 7.27 × 10⁶ m³, with 4.87 × 10⁶ m³ in current GSV and 2.40 × 10⁶ m³ in harvested timber through historical thinning. The historical process-harvested timber accounts to 33.0% of the total timber production, which component has been neglected in the assessments for current status of plantation forests. Synthetically considering the RMSE for predictive GSV and misclassification of age-class, the error in timber production were supposed to range from −55.2 to 56.3 m³ ha⁻¹. The MPT framework can be used to assess timber production of other tree species at a larger spatial scale, providing crucial information for a better understanding of forest ecosystem service.     

Modelling high arctic percent vegetation cover using field digital images and high resolution satellite data

In this study, digital images collected at a study site in the Canadian High Arctic were processed and classified to examine the spatial-temporal patterns of percent vegetation cover (PVC). To obtain the PVC of different plant functional groups (i.e., forbs, graminoids/sedges and mosses), field near infrared-green-blue (NGB) digital images were classified using an object-based image analysis (OBIA) approach. The PVC analyses comparing different vegetation types confirmed: (i) the polar semi-desert exhibited the lowest PVC with a large proportion of bare soil/rock cover; (ii) the mesic tundra cover consisted of approximately 60% mosses; and (iii) the wet sedge consisted almost exclusively of graminoids and sedges. As expected, the PVC and green normalized difference vegetation index (GNDVI; (R_NIR − R_Green)/(R_NIR + R_Green)), derived from field NGB digital images, increased during the summer growing season for each vegetation type: i.e., ∼5% (0.01) for polar semi-desert; ∼10% (0.04) for mesic tundra; and ∼12% (0.03) for wet sedge respectively. PVC derived from field images was found to be strongly correlated with WorldView-2 derived normalized difference spectral indices (NDSI; (R_x − R_y)/(R_x + R_y)), where R_x is the reflectance of the red edge (724.1 nm) or near infrared (832.9 nm and 949.3 nm) bands; R_y is the reflectance of the yellow (607.7 nm) or red (658.8 nm) bands with R²’s ranging from 0.74 to 0.81. NDSIs that incorporated the yellow band (607.7 nm) performed slightly better than the NDSIs without, indicating that this band may be more useful for investigating Arctic vegetation that often includes large proportions of senescent vegetation throughout the growing season.     

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.     

Towards decadal soil salinity mapping using Landsat time series data

Salinization is one of the major soil problems around the world. However, decadal variation in soil salinization has not yet been extensively reported. This study exploited thirty years (1985–2015) of Landsat sensor data, including Landsat-4/5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI (Operational Land Imager), for monitoring soil salinity of the Yellow River Delta, China. The data were initially corrected for atmospheric effects, and then matched the spectral bands of EO-1 (Earth Observing One) ALI (Advanced Land Imager). Subsequently, soil salinity maps were derived with a previously developed PLSR (Partial Least Square Regression) model. On intra-annual scale, the retrievals showed that soil salinity increased in February, stabilized in March, and decreased in April. On inter-annual scale, soil salinity decreased within 1985–2000 (−0.74 g kg⁻¹/10a, p < 0.001), and increased within 2000–2015 (0.79 g kg⁻¹/10a, p < 0.001). Our study presents a new perspective for use of multiple Landsat data in soil salinity retrieval, and further the understanding of soil salinization development over the Yellow River Delta.     

A GPP assimilation model for the southeastern Tibetan Plateau based on CO2 eddy covariance flux tower and remote sensing data

The gross primary production (GPP) at individual CO₂ eddy covariance flux tower sites (GPP_Tower) in Dali (DL), Wenjiang (WJ) and Linzhi (LZ) around the southeastern Tibetan Plateau were determined by the net ecosystem exchange of CO₂ (NEE) and ecosystem respiration (R_e). The satellite remote sensing-VPM model estimates of GPP values (GPP_MODIS) used the satellite-derived 8-day surface reflectance product (MOD09A1), including satellite-derived enhanced vegetation index (EVI) and land surface water index (LSWI). In this paper, we assembled a subset of flux tower data at these three sites to calibrate and test satellite-VPM model estimated GPP_MODIS, and introduced the satellite data and site-level environmental factors to develop four new assimilation models. The new assimilation models’ estimates of GPP values were compared with GPP_MODIS and GPP_Tower, and the final optimum model among the four assimilation models was determined and used to calibrate GPP_MODIS. The results showed that GPP_MODIS had similar temporal variations to the GPP_Tower, but GPP_MODlS were commonly higher in absolute magnitude than GPP_Tower with relative error (RE) about 58.85%. While, the assimilation models’ estimates of GPP values (GPP_MODEL) were much more closer to GPP_Tower with RE approximately 6.98%, indicating that the capacity of the simulation in the new assimilation model was greatly improved, the R² and root mean square error (RMSE) of the new assimilation model were 0.57–4.90% higher and 0.74–2.47 g C m⁻² s⁻¹ lower than those of the GPP_MODIS, respectively. The assimilation model was used to predicted GPP dynamics around the Tibetan Plateau and showed a reliable result compared with other researches. This study demonstrated the potential of the new assimilation model for estimating GPP around the Tibetan Plateau and the performances of site-level biophysical parameters in related to satellite-VPM model GPP.