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 canopy chlorophyll content (CCC) and leaf area index (LAI) are both essential indicators for crop growth monitoring and yield estimation. The PROSAIL model, which couples the properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAIL) radiative transfer models, is commonly used for the quantitative retrieval of crop parameters; however, its homogeneous canopy assumption limits its accuracy, especially in the case of multiple crop categories. The adjusted average leaf angle (ALA_adj), which can be parameterized for a specific crop type, increases the applicability of the PROSAIL model for specific crop types with a non-uniform canopy and has the potential to enhance the performance of PROSAIL-coupled hybrid methods. In this study, the PROSAIL-D model was used to generate the ALA_adj values of wheat, soybean, and maize crops based on ground-measured spectra, the LAI, and the leaf chlorophyll content (LCC). The results revealed ALA_adj values of 62 degrees for wheat, 45 degrees for soybean, and 60 degrees for maize. Support vector regression (SVR), random forest regression (RFR), extremely randomized trees regression (ETR), the gradient boosting regression tree (GBRT), and stacking learning (STL) were applied to simulated data of the ALA_adj in 50-band data to retrieve the CCC and LAI of the crops. The results demonstrated that the estimation accuracy of singular crop parameters, particularly the crop LAI, was greatly enhanced by the five machine learning methods on the basis of data simulated with the ALA_adj. Regarding the estimation results of mixed crops, the machine learning algorithms using ALA_adj datasets resulted in estimations of CCC (RMSE: RFR = 51.1 μg cm⁻², ETR = 54.7 μg cm⁻², GBRT = 54.9 μg cm⁻², STL = 48.3 μg cm⁻²) and LAI (RMSE: SVR = 0.91, RFR = 1.03, ETR = 1.05, GBRT = 1.05, STL = 0.97), that outperformed the estimations without using the ALA_adj (namely CCC RMSE: RFR = 93.0 μg cm⁻², ETR = 60.1 μg cm⁻², GBRT = 60.0 μg cm⁻², STL = 68.5 μg cm⁻² and LAI RMSE: SVR = 2.10, RFR = 2.28, ETR = 1.67, GBRT = 1.66, STL = 1.51). Similar findings were obtained using the suggested method in conjunction with 19-band data, demonstrating the promising potential of this method to estimate the CCC and LAI of crops at the satellite scale.

Leaf chlorophyll content (LCC) is a prominent plant physiological trait and a proxy for leaf photosynthetic capacity. The acquisition of LCC data over large spatial and temporal scales facilitates vegetation growth monitoring and terrestrial carbon cycle modeling. In this study, a global 500 m LCC weekly dataset (GLCC) was produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physical radiative transfer modeling approach that considers the influence of canopy structure and soil background. Firstly, five look-up-tables (LUTs) were generated using PROSPECT-D+4-Scale and PROSAIL-D models for woody and non-woody plants. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. The one LUT applicable to non-woody vegetation type includes 25 sub-LUTs corresponding to five kinds of canopy structures and five kinds of soil backgrounds. The final retrieval was considered the aggregation of the LCC inversion results of all sub-LUTs for each plant function type (PFT). Then, the GLCC dataset was generated and validated using field measurements, yielding an overall accuracy of R² = 0.41 and RMSE = 8.94 μg cm⁻². Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset. OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003–2012 to 2018–2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity to analyze variations in vegetation dynamics.

The accurate retrieval of canopy chlorophyll content (CCC) is essential to the effective monitoring of forest productivity, and environmental stress. However, the clumping index (CI), a vital canopy structural parameter, affects inaccurate remote sensing of forest CCC. In this article, we proposed a concept of effective CCC (CCC _e ) and an integrated CI approach to retrieving forest CCC using empirical regression and random forest regression. First, the PROSPECT-D and four-scale models were used to simulate forest canopy spectra, and the forest CCC _e including CI was found more feasible to be remotely sensed than the CCC. Then, an empirical regression model and random forest model trained using different combinations of the medium resolution imaging spectrometer (MERIS) terrestrial chlorophyll index (MTCI), reflectance, and CI values were used to estimate the CCC. Finally, the proposed approach was tested using satellite-based CI and MERIS product. Using the empirical regression model, the results showed that the retrieval of forest CCC using the MTCI was greatly improved by the inclusion of the CI (RMSE from 63.64 to 36.51 μg cm ^-2 for broadleaf; RMSE from 96.02 to 58.49 μg cm ^-2 for coniferous). Using the random forest approach and the model trained using the reflectance in red and red-edge bands, MTCI, and CI performed best, with RMSE = 27.95 μg cm ^-2 for broadleaf and RMSE = 34.83 μg cm ^-2 for coniferous. Overall, it is concluded that include the CI, particularly the approach using forest random regression, have the potential for satellite-based forest CCC mapping at regional and global scales.

Rapid urbanization facilitates socioeconomic development but also exacerbates land use/cover change (LUCC), significantly impacting ecological environments. Timely, objective, and quantitative assessments of ecological quality changes resulting from LUCC are essential for safeguarding the natural environment and managing land resources. However, limited research has explored the potential interrelationships between the spatio-temporal heterogeneity of LUCC and ecological quality during urbanization. This study focuses on the Xiong’an New Area, a region experiencing rapid urbanization, utilizing the remote sensing-based ecological index (RSEI) to monitor ecological quality dynamics from 2017 to 2023. To address the computational challenges associated with large-scale regions, a streamlined RSEI construction method was developed using Landsat imagery and implemented via Google Earth Engine (GEE). A geographically weighted regression (GWR) analysis, integrated with Sentinel-2 land use data, was employed to examine the influence of LUCC on ecological quality. The findings reveal the following: (1) Ecological quality in the Xiong’an New Area has exhibited an overall positive trajectory, with improvements elevating the ecological status to above moderate levels. (2) Urban expansion resulted in a 17% reduction in farmland, primarily converted into construction land, which expanded by approximately 12%. (3) Ecological protection policies have facilitated the conversion of farmland into wetlands and urban green areas, which emerged as the principal contributors to ecological quality enhancement. (4) A positive correlation was observed between changes in ecological land and ecological quality, while a negative correlation was identified between shifts in the construction land and farmland and ecological quality. This research provides valuable scientific insights into ecological conservation and land use management, thereby establishing a foundation for the development of rational land resource planning and sustainable ecological development strategies in the Xiong’an New Area.