Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this paper, the key parameters of the Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (ε_max), was optimized by using vegetation classification data. Then, the NEP was estimated by coupling the optimized CASA model, geostatistical model of soil respiration (GSMSR) and the soil respiration–soil heterotrophic respiration (R_s-R_h) relationship model. The ground observations from ChinaFLUX were used to verify the NEP estimation accuracy. The results showed that the R² of the optimized CASA model increased from 0.411 to 0.774, and RMSE decreased from 21.425 gC·m⁻²·month⁻¹ to 12.045 gC·m⁻²·month⁻¹, indicating that optimizing CASA model by vegetation classification data was an effective method to improve the estimation accuracy of NEP. On this basis, the spatial and temporal distribution of NEP in China was analyzed. The research indicated that the monthly variation of NEP in China was a single peak curve with summer as the peak, which generally presented the pattern of southern region > northern region > Qinghai–Tibet region > northwest region. Furthermore, from 2001 to 2016, most regions of China showed a non-significant level upward trend, but main cropland (e.g., North China Plain and Northeast Plain) and some grassland (e.g., Ngari in Qinghai–Tibet Plateau and Xilin Gol League in Inner Mongolia) showed a non-significant-level downward trend. The study can deepen the understanding of the distribution of carbon sources/sinks in China, and provide a reference for regional carbon cycle research.

The leaf area index (LAI) is an essential indicator used in crop growth monitoring. In the study, a hybrid inversion method, which combined a physical model with a statistical method, was proposed to estimate the crop LAI. The simulated compact high-resolution imaging spectrometer (CHRIS) canopy spectral crop reflectance datasets were generated using the PROSAIL model (the coupling of PROSPECT leaf optical properties model and Scattering by Arbitrarily Inclined Leaves model) and the CHRIS band response function. Partial least squares (PLS) was then used to reduce the dimension of the simulated spectral data. Using the principal components (PCs) of PLS as the model inputs, the hybrid inversion models were built using various modeling algorithms, including the backpropagation artificial neural network (BP-ANN), least squares support vector regression (LS-SVR), and random forest regression (RFR). Finally, remote sensing mapping of the CHRIS data was achieved with the hybrid model to test the inversion accuracy of LAI estimates. The validation result yielded an accuracy of R² = 0.939 and normalized root-mean-square error (NRMSE) = 6.474% for the PLS_RFR model, which indicated that the crops LAI could be estimated accurately by using spectral feature extraction and a hybrid inversion strategy. The results showed that the model based on principal components extracted by PLS had a good estimation accuracy and noise immunity and was the preferred method for LAI estimation. Furthermore, the comparative analysis results of various datasets showed that prior knowledge could improve the precision of the retrieved LAI, and using this information to constrain parameters (e.g., chlorophyll content or LAI), which make important contributions to the spectra, is the key to this improvement. In addition, among the PLS, BP-ANN, LS-SVR, and RFR methods, RFR was the optimal modeling algorithm in the paper, as indicated by the high R² and low NRMSE in various datasets.

Net ecosystem productivity (NEP) is a key indicator to describe terrestrial ecosystem functions and carbon sinks. The CASA model was improved by optimizing the parameters optimum temperature and maximum light use efficiency ( $\varepsilon_{max}$ ), and the NEP value of the European terrestrial ecosystem was calculated by combining the soil respiration model. The results showed that when using vegetation classification data to optimize parameter $\varepsilon_{max}$ , the R2 of NEP between estimates and observations increased from 0.252 to 0.403, and the RMSE decreased from 84.557 gC·m ^-2 ·month ^-1 to 64.466 gC·m ^-2 ·month ^-1 . After further optimizing the optimum temperature, R2 increased to 0.428, and the RMSE decreased to 63.720 gC·m ^-2 ·month ^-1 . It indicated that the CASA model could be improved by optimizing $\varepsilon_{max}$ as well as optimum temperature, which was a good approach to improve the NEP estimations. Based on this, the NEP spatiotemporal changes in various regions of Europe were analyzed using the optimization results. The NEP values of European terrestrial ecosystem has regional differences, showing a pattern of western region > southern region > central region > eastern region > northern region. The monthly change of NEP in each region is a single peak curve with high in summer and low in winter, and the annual overall value is positive (i.e., it shows a carbon sink). The research can enable us to obtain the carbon source/sink distribution information in Europe more accurately and provide a scientific reference for carbon balance policy formulation in the region.

Validation is mandatory to quantify the reliability of remote sensing products (RSPs). However, this process is not straightforward and usually presents formidable challenges in terms of both theory and real-world operations. In this context, a dedicated validation initiative was launched in China, and we identified a validation strategy (VS). This overall VS focuses on validating regional-scale RSPs with a systematic site-to-network concept, consisting of four main components: (1) general guidelines and technical specifications to guide users in validating various land RSPs, particularly aiming to further develop in situ sampling schemes and scaling approaches to acquire ground truth at the pixel scale over heterogeneous surfaces; (2) sound site-based validation activities, conducted through multi-scale, multi-platform, and multi-source observations to experimentally examine and improve the first component; (3) a national validation network to allow for comprehensive assessment of RSPs from site or regional scales to the national scale across various zones; and (4) an operational RSP evaluation system to implement operational validation applications. Research progress on the development of these four components is described in this paper. Some representative research results, with respect to the development of sampling methods and site-based validation activities, are also highlighted. The development of this VS improves our understanding of validation issues, especially to facilitate validating RSPs over heterogeneous land surfaces both at the pixel scale level and the product level.

Net ecosystem productivity (NEP) is a critical indicator of the CO2 capture capacity of vegetation ecosystems. Based on the land classification and clumping index (CI) datasets, the key parameters of the CASA model, including fraction of photosynthetic active radiation (FPAR) and maximum light use efficiency ( $\varepsilon_{max}$ ), were optimized. Then, the NEP of China's terrestrial ecosystems was estimated, using the improved CASA coupled with a soil respiration model. Finally, the accuracy of NEP estimation was evaluated by observation data from ChinaFLUX station. The research results indicated the RMSE of the improved NEP estimations decreased from 21.139 to 10.179 (unit: gC⋅m ⁻² ⋅month ⁻¹ , and the R ² value increased from 0.413 to 0.832, indicating that optimizing the parameters ( $\varepsilon_{max}$ ) and FPAR are both effective methods to improve the model. The spatiotemporal variation of China's NEP was analyzed using the optimized results. The NEP value of China shows a decreasing distribution pattern from southeast to northwest, and the values of different regions are in the order South > North > Qinghai–Tibet > Northwest. The monthly NEP variation in different regions of China is a unimodal curve, reaching the maximum in summer. This study optimized the NEP estimation, which can better characterize the distribution pattern of carbon sinks/sources in China's terrestrial ecosystems and lay a scientific foundation for developing regional carbon neutrality schemes.

Leaf area index (LAI) is a valuable indicator used in vegetation growth monitoring. To optimize the index selection according to the type of remote sensing data and to improve the inversion accuracy of LAI, this paper analyzes the influence of different bandwidths on the accuracy of the inversion model based on vegetation indices. First, the simulation dataset is generated by the PROSAIL model, and on this basis, 15 vegetation indices with high correlation coefficients with LAI, Then, by analyzing the sensitivity of these 15 indices to the variations in bandwidth, and to the coefficient of determination (R2) of the LAI inversion model with the variations of bandwidth, the influence of different bandwidths on the accuracy of LAI estimation by each index is determined. The results show that bandwidth is one of the most important factors in determining the accuracy of LAI inversion, and the influence on different vegetation indices can be divided into three categories: 1) narrowband vegetation index, the accuracy of inversion models built by vegetation indices decreases with the increase of bandwidth, including SR $_{[800,680]}$ , OSAVI, MTVI2, SR $_{[752,690]}$ , RDVI, NDCI, and NVI; 2) middleband vegetation index, the accuracy first increases and then decreases with the increase of bandwidth, including SR $_{[700,670]}$ , Carte5, and SR $_{[675,700]}$ ; And 3) broadband vegetation index, the accuracy increases with the increase of bandwidth, including SPVI, Carte2, OSAVI2, MTVI1, and NDVI705. The study provides a scientific basis for vegetation index optimization in the process of LAI inversion.