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 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 (ALAadj), 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 ALAadj values of wheat, soybean, and maize crops based on ground-measured spectra, the LAI, and the leaf chlorophyll content (LCC). The results revealed ALAadj 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 ALAadj 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 ALAadj. Regarding the estimation results of mixed crops, the machine learning algorithms using ALAadj 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 ALAadj (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.

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 (ALAadj) 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 ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj 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 ALAadj 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 ALAadj, 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.

作物群体生物量是形成产量的物质基础,遥感技术是高效、客观监测作物地上生物量的重要手段,对农业生产管理具有重要意义.以安徽省龙亢农场为研究区,通过PROS AIL模拟光谱分析了 4个LAI相关的可见光-近红外植被指数、2个叶片干物质相关的短波红外植被指数和8个融合植被指数与冬小麦地上生物量的关系,并建立反演模型.模拟结果显示,干物质植被指数与作物生物量的相关性高于LAI相关的植被指数,两者融合的植被指数增强了常用植被指数冬小麦生物量的探测能力.利用实测冬小麦数据对生物量反演模型进行验证,结果显示:融合植被指数普遍提高了单一植被指数的地上生物量反演精度,其中MTVI2×NDMI精度最高(RMSE= 606.8 kg/hm2),并为作物地上生物量的高精度反演提供新的技术途径.

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.

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.

遥感技术是高效、客观监测农作物生长状态的重要手段,对农业生产管理具有重要意义.以安徽龙亢农场为研究区,收集了中高分辨率多源卫星遥感数据并进行了定量化处理,构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型,生产了长时序冬小麦植被参数卫星遥感产品.通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律,分析了不同品种冬小麦的长势情况,发现高产量小麦在越冬期长势显著优于低产量小麦.在此基础上,构建了基于归一化植被指数(NDVI)的冬小麦估产模型,结果表明:利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算,据此绘制了龙亢农场2017年冬小麦产量遥感估算地图,产量分布与实际种植情况吻合良好.实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测,为区域范围内农作物长势监测提供了一种有效途径.

Chlorophyll is an important pigment in green plants for photosynthesis and obtaining the energy for growth and development. The rapid, nondestructive and accurate estimation of chlorophyll content is significant for understanding the crops growth, monitoring the disease and insect, and assessing the yield of crops. Sentinel-2 equipped with the Multi-Spectral Instrument (MSI), which will provide images with high spatial, spectral and temporal resolution. It covers the VNIR/SWIR spectral region in 13 bands and incorporates two new spectral bands in the red-edge region and a spatial resolution of 20nm, which can be used to derive vegetation indices using red-edge bands. In this paper, we will focus on assessing the potential of vegetation spectral indices for retrieving chlorophyll content from Sentinel-2 at different angles. Subsequently, we used in-situ spectral data and Sentinel-2 data to test the relationship between VIs and chlorophyll content. The REP, MTCI, CI red-edge , CI green , Macc01, TCARI/OSAVI [705,750], NDRE1 and NDRE2 were calculated. NDRE2 index displays a strongly similar result for hyperspectral and simulated Sentinel-2 spectral bands (R 2 =0.53, R 2 =0.51, for hyperspectral and Sentinel-2, respectively). At different observation angles, NDRE2 has the smallest difference in performance (R 2 = 0.51, R 2 =0.64, at 0° and 15° , respectively).