Retrieval of leaf biochemical parameters from reflectance measurements using model inversion generally faces "ill-posed" problems, which dramatically decreases the estimation accuracy of an inverse ...model. While the standard approach for model inversion retrieves various parameters simultaneously, usually only based on one merit function, the new approach proposed in this paper assigns a specific merit function for each retrieved parameter. Each merit function is specified in terms of the wavelength domains that the given parameter was found to be specifically sensitive to in an earlier sensitivity analysis. The approach has been validated with both in situ measured data sets and an artificial data set of 10 000 spectra simulated by the PROSPECT model. Results indicate that the new approach greatly improves the performance of inversion models, with root-mean-square error (rmse) values for chlorophyll content (Chl), equivalent water thickness (EWT), and leaf mass per area (LMA), based on the simulated data, of 7.12 μg/cm 2 , 0.0012 g/cm 2 , and 0.0019 g/cm 2 , respectively, compared with 11.36 μg/cm 2 , 0.0032 g/cm 2 , and 0.0040 g/cm 2 when using the standard approach. As for field-measured data sets, the proposed approach also greatly outperformed the standard approach, with respective rmse values of 8.11 μg/cm 2 , 0.0012 g/cm 2 , and 0.0008 g/cm 2 for Chl, EWT, and LMA when all data are pooled, compared with 11.84 μg/cm 2 , 0.0020 g/cm 2 , and 0.0027 g/cm 2 when using the standard approach. Hence, the proposed approach for model inversion can largely alleviate the "ill-posed" problem, and it could be widely applied for retrieving leaf biochemical parameters.
► A multi-layer canopy radiative transfer model (MRTM) has been developed. ► Canopy vertical heterogeneity significantly affects canopy scale reflectance. ► Biophysical and biochemical properties ...vary tremendously within canopy. ► Vertical change information is critical for accurate retrieval from reflectance.
An explicit analytical model for calculating vegetation canopy reflectance, the multiple-layer canopy radiative transfer model (MRTM), has been developed in this paper. The model is based on radiative transfer theory by separating the roles of incident direct and diffuse radiation and radiation singly and multiply scattered by foliage. Specifically, the vertical heterogeneity of biophysical and biochemical parameters within the canopy was carefully treated in the model. This model was validated with field measurements from a deciduous forest canopy. The results proved that the model could reproduce the measured reflectance quite well. In addition, the performance of MRTM was found to be superior in comparison to other canopy models such as PROSAIL, ACRM and FRT. The significant effect of vertical heterogeneity on the canopy reflectance was clearly identified by different scenarios, which indicates that the influence of vertical variation in leaf area density and leaf chlorophyll, water, and dry matter contents cannot be neglected, especially when the total LAI is large. If such influences are ignored, significant biases in the estimated canopy reflectance can be expected. Since this multiple-layer model is a hybrid one that offers efficient calculation, it could serve as a primary model to develop more accurate reflectance models for inhomogeneous forests at plot and regional scales in future studies.
Soil salinization is an important desertification process that threatens the stability of ecosystems, especially in arid lands. Quantifying and mapping soil salinity to monitor soil salinization is ...difficult because of its large spatial and temporal variability. There has been a growing interest in the use of hyperspectral reflectance as a rapid and inexpensive tool for soil salinity characterization in the recent past. However, as soil moisture often jointly affects soil reflectance, a moisture-insensitive reflectance model is needed to provide the base for soil salinity monitoring from soil reflectance. In this paper, we developed an exponent reflectance model to estimate soil salt contents inversely under various soil moisture conditions, based on a control laboratory experiment on the two factors (soil salinity and soil moisture) to soil reflectance. Main soil salt types (Na2SO4, NaCl, Na2CO3) with wide soil salinity (0% to 20%) and soil moisture (1.75% to 20%) levels (in weight base) from Western China were examined for their effects on soil reflectance through a model based approach. Moisture resistant but salt sensitive bands of reflected spectra have been identified for the model before being applied to inversely estimate soil salt content. Sensitive bands for Na2SO4 type of salt affected soils were identified as from 1920 to 2230nm, and 1970 to 2450nm for NaCl, 350 to 400nm for Na2CO3 type of salt affected soils, respectively. The sensitive bands focused on ranged from 1950 to 2450nm when all data were considered when ignoring salt types. The model was then applied to inversely estimate soil salt contents. High R2 of 0.87, 0.79, and 0.66, and low mean relative error (MRE) of 16.42%, 21.17%, and 27.16%; have been obtained for NaCl, Na2SO4 and Na2CO3, respectively. Performance of the inverse model dropped but remained significant when ignoring salt types with an R2 of 0.56 and a MRE of 33.25%. The approach proposed in this study should thus provide a new direction for estimating salinity from reflectance under various soil moisture conditions and should have wide applications in future monitoring of soil salinization.
► Salt contents under various moisture conditions have been inversely retrieved. ► Effects of soil moisture on soil spectra have been explicitly clarified. ► Moisture-resistant but salt-sensitive wavelengths have been identified. ► The model obtained comparable inversion results for both dry and wet soils.
Bamboo forests provide important ecosystem services and play an important role in terrestrial carbon cycling. Of the approximately 500 bamboo species in China, Moso bamboo (Phyllostachys pubescens) ...is the most important one in terms of distribution, timber value, and other economic values. In this study, we estimated current and potential carbon stocks in China's Moso bamboo forests and in their products. The results showed that Moso bamboo forests in China stored about 611.15 ± 142.31 Tg C, 75% of which was in the top 60 cm soil, 22% in the biomass of Moso bamboos, and 3% in the ground layer (i.e., bamboo litter, shrub, and herb layers). Moso bamboo products store 10.19 ± 2.54 Tg C per year. The potential carbon stocks reach 1331.4 ± 325.1 Tg C, while the potential C stored in products is 29.22 ± 7.31 Tg C a−1. Our results indicate that Moso bamboo forests and products play a critical role in C sequestration. The information gained in this study will facilitate policy decisions concerning carbon sequestration and management of Moso bamboo forests in China.
•Moso bamboo forest stored 611.15 ± 142.31 TgC, about 7.8% of that of China's forests.•C stocks in soil, vegetation and ground layers are 75%, 22% and 3%, respectively.•The potential carbon stocks of Moso bamboo forest can reach 1331.4 ± 325.1 TgC.•Current and potential Moso bamboo products store 10.19 ± 2.54 and 29.22 ± 7.31 TgC a−1.•Moso bamboo plantations play an essential role in carbon sink forestry in China.
This study aimed at finding efficient hyperspectral indices for estimating three leaf biochemical parameters: chlorophyll content (CHL, μg
cm
−2), leaf water thickness (EWT, g
cm
−2), and leaf mass ...per area (LMA, g
cm
−2) in typical temperate deciduous forests. These parameters are required by most biogeochemical models that describe ecosystem functions. We have identified the most efficient hyperspectral indices (both the index types and the wavelength domains) based on both a simulated data set (produced with the calibrated leaf reflectance model PROSPECT) and with data sets (I, II, and III) from measurement of field-collected leaves. Results indicated that CHL, EWT, and LMA can be estimated with high precision using a two-waveband vegetation index (Double Deference index, DDn) for all parameters, with an overall root mean square error (RMSE) of 6.87
μg
cm
−2 for CHL, 0.0011
g
cm
−2 for EWT, and 0.0015
g
cm
−2 for LMA. The best overall indices for temperate deciduous forests were DDn (715, 185) for CHL, DDn (1530, 525) for EWT, and DDn (1235, 25) for LMA, although these indices were not necessarily the best for every specific data set (especially for the simulated data set). Moreover, discrepancies were obvious when the identified indices were applied to different data sets. Even if the wavelengths of calibrated indices have been accurately determined through the simulated data set, the regressions between the indices and the biochemical parameters must be calibrated with field-based measurements. The indices identified in this study are applicable to various species (data set III), various phenological stages and locations (data set I), and various leaf anatomies (data set II) and may therefore be widely applicable for temperate deciduous forests and possibly for other plant communities.
Vertical heterogeneity of the canopy is being increasingly recognized in remote estimates of vegetative properties. Given the current limited knowledge of this issue, this paper investigated the ...effects of different vertical distributions of crop structure e.g., leaf angle (LA) and leaf area index (LAI) and biochemical parameters e.g., chlorophyll a and b content (Chl a+b ) and water content (W c ) on canopy reflectance and vegetation indices (VIs). A recently developed multiple-layer canopy reflectance model (MRTM) was tested for winter wheat and used to run a simulation analysis of different canopy scenarios. The results showed that the MRTM performed well to model winter wheat canopy reflectance with regard to spikes and vertical distributions of leaf properties. The vertical profiles of LA and LAI influenced canopy reflectance at almost all wavelengths, whereas the vertical profile of Chl a+b mainly affected reflectance in the visible region, and that of W c only affected reflectance in the near-infrared region. Changes in vertical distribution of the LA resulted in clear variations in VIs related to the LA, LAI, and Chl a+b estimates. The vertical LAI and Chl a+b profiles mainly influenced the VIs related to the LAI and Chl a+b estimates. The W c vertical profile primarily affected the VIs used to estimate crop water properties. The sensitivities of the VIs were mainly associated with the spectral responses and penetration characteristics of the bands they used. These findings suggest that the sensitivity of VIs to the vertical distributions of crop parameters should be considered when establishing models for remote crop monitoring.
Estimation of vegetation chlorophyll content is crucial for understanding carbon balance and for assessing stress and vulnerability of desert ecosystems. This study evaluated LIBERTY and PROSPECT, ...both the radiative transfer models at leaf scale, for estimating the chlorophyll content of Haloxylon ammodendron assimilating branches inversely from measured reflectance spectra. The results showed that both original LIBERTY and PROSPECT exhibited tangible challenges for inversion using measured data. However, their calibrated versions were capable of accurate retrieval of chlorophyll content inversely from reflectance spectra. For calibrated LIBERTY, the inversed estimation recorded an R² of 0.55 with an RMSE of 34.33 mg m⁻² over the entire measured chlorophyll range from 47.03 to 291.83 mg m⁻². For comparison, the R² reached 0.53 with an RMSE of 34.76 mg m⁻² for the calibrated PROSPECT. Further validations with other independent data sets produced similar high chlorophyll estimation accuracies. Our results indicated that both LIBERTY and PROSPECT are applicable for estimating chlorophyll content inversely for assimilating branches of typical desert plants after careful calibration, which is a necessary prior when coupling with canopy models to make further stand level chlorophyll estimations.
The selective cutting method currently used in Moso bamboo forests has resulted in a reduction of stand productivity and carbon sequestration capacity. Given the time and labor expense involved in ...addressing this problem manually, simulation using an ecosystem model is the most suitable approach. The BIOME-BGC model was improved to suit managed Moso bamboo forests, which was adapted to include age structure, specific ecological processes and management measures of Moso bamboo forest. A field selective cutting experiment was done in nine plots with three cutting intensities (high-intensity, moderate-intensity and low-intensity) during 2010–2013, and biomass of these plots was measured for model validation. Then four selective cutting scenarios were simulated by the improved BIOME-BGC model to optimize the selective cutting timings, intervals, retained ages and intensities. The improved model matched the observed aboveground carbon density and yield of different plots, with a range of relative error from 9.83% to 15.74%. The results of different selective cutting scenarios suggested that the optimal selective cutting measure should be cutting 30% culms of age 6, 80% culms of age 7, and all culms thereafter (above age 8) in winter every other year. The vegetation carbon density and harvested carbon density of this selective cutting method can increase by 74.63% and 21.5%, respectively, compared with the current selective cutting measure. The optimized selective cutting measure developed in this study can significantly promote carbon density, yield, and carbon sink capacity in Moso bamboo forests.
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•We optimized the selective cutting measure using the improved BIOME-BGC model.•Selective cutting in winter has the greatest benefit to carbon storage and fluxes.•The optimal interval for selective cutting was identified as 2 years.•Best cutting ratio for aged 6, 7 and >8 years was 0.3, 0.8 and 1, respectively.•Plant carbon storage and yield of this method increased 75% and 22%, respectively.
Monitoring soil salinization has been a difficult process in arid lands due their large spatial and temporal variability. Hyperspectral remote sensing has offered a potential for faster detection of ...salinization process but mostly from empirical approaches. In this paper, an integrated approach combining model inversion and empirical regressions has been proposed for soil salt content (SSC) estimation from hyperspectral information obtained from controlled laboratory experiments. All soil samples were artificially salinized using Na 2 SO 4 , NaCl, and Na 2 CO 3 (99% purity) salts to different levels and to different soil-moisture conditions, since soil moisture often jointly affects reflectance spectra with SSC. Hapke model was calibrated and validated for its simulation on soil reflectance and showed good agreements with measured data. The optimal values of single scattering albedo that was inversely retrieved from the Hapke model had good relationships with SSC at 2000-2200 nm for each treatment even under various soil-moisture conditions. Taking usage of these findings, the integrated approach obtained high accuracies on SSC estimations with R 2 's of 0.90, 0.86, and 0.72 and slightly dropped R 2 's of 0.89, 0.81, and 0.67 for NaCl-, Na 2 SO 4 -, and Na 2 CO 3 -type saline soils under respective dry and wet conditions. The R 2 decreased to 0.55 and 0.53 for dry and wet soils when salt types were ignored. The integrated approach provides a novel as well as an efficient way for SSC estimation from reflected spectra, and hence, we foresee its potential applications for large-scale SSC mapping from reflectance measurements.
