Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R² and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.

The fractional cover of vegetation (PV) and exposed bedrock are key ecological indicators of the extent and degree of land degradation in karst regions. In this study, we suggested and compared new methodology for direct and objective estimation of key indicators of karst rocky desertification with hyperspectral and multispectral imagery. The results showed that the Hyperion estimated fractional covers of PV had good correlation with the field surveyed fractional covers and the R2 (coefficient of determination) and RMSE (root mean square error) for PV was 0.91 and 0.05, respectively; while for exposed bedrock was not so good, 0.53 and 0.11, respectively. It demonstrated that hyperspectral imagery was able to directly estimate the key ecological indicators of karst rocky desertification, which was in a heterogeneous landscape. As for the ASTER imagery, the results were not so accurate. It showed that multispectral imagery could not be used to effectively estimate the fractional cover of PV and exposed bedrock. Our study indicates that it could use hyperspectral imagery to directly and effectively estimate the fractional cover of PV and exposed bedrock for karst rocky desertification assessment in a heterogeneous landscape of karst ecosystem

Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALA_adj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALA_adj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALA_adj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALA_adj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALA_adj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm⁻²; R² from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm⁻²; R² from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm⁻²; R² from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALA_adj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.

Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat: R² = 0.62 and RMSE = 77.10 μg cm⁻²; MERIS satellite data for soybeans: R² = 0.24 and RMSE = 136.54 μg cm⁻²). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVI2 and random forest regression, with R² = 0.65 and RMSE = 37.76 μg cm⁻² (field spectra data) and R² = 0.78 and RMSE = 47.96 μg cm⁻² (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data.

The Landsat satellite series represents the longest record of global-scale medium spatial resolution earth observations, and the utility of Landsat data for long-term and/or large-area monitoring depends on accurate and quantitative atmospheric correction to produce a consistently corrected surface reflectance (SR) dataset. In this study, we developed a rapid, automated atmospheric correction procedure based on Moderate Resolution Imaging Spectrometer (MODIS) atmospheric characterization products and the 6S (Second Simulation of a Satellite Signal in the Solar Spectrum) radiative transfer code for Landsat-5 Thematic Mapper (TM) imagery. Three MODIS atmosphere products at the resolution of 0.05°, MOD04, MOD05, and MOD07 were used as input to the 6S radiative transfer model in order to compute the parameters required for atmospheric correction, which were then used to correct TM imagery per-pixel automatically. This method was tested using five multi-date Landsat TM images in Beijing, China, and the atmospheric correction precision was assessed using ground-measured reflectance. The result showed that the SR retrieved from Landsat TM is consistent with the ground measurements, with a mean R ² of 0.773 and a mean root mean square error value of 0.045.