Novel hyperspectral indices, which are the first derivative normalized difference nitrogen index (FD-NDNI) and the first derivative ratio nitrogen vegetation index (FD-SRNI), were developed to estimate the leaf nitrogen content (LNC) of wheat. The field stress experiments were conducted with different nitrogen and water application rates across the growing season of wheat and 190 measurements were collected on canopy spectra and LNC under various treatments. The inversion models were constructed based on the dataset to evaluate the ability of various spectral indices to estimate LNC. A comparative analysis showed that the model accuracies of FD-NDNI and FD-SRNI were higher than those of other commonly used hyperspectral indices including mNDVI₇₀₅, mSR, and NDVI₇₀₅, which was indicated by higher R² and lower root mean square error (RMSE) values. The least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms were then used to optimize the models constructed by FD-NDNI and FD-SRNI. The p-R² values of the FD-NDNI_RFR and FD-SRNI_RFR models reached 0.874 and 0.872, respectively, which were higher than those of the exponential and SVR model and indicated that the RFR model was accurate. Using the RFR inversion model, remote sensing mapping for the Operative Modular Imaging Spectrometer (OMIS) image was accomplished. The remote sensing mapping of the OMIS image yielded an accuracy of R² = 0.721 and RMSE = 0.540 for FD-NDNI and R² = 0.720 and RMSE = 0.495 for FD-SRNI, which indicates that the similarity between the inversion value and the measured value was high. The results show that the new hyperspectral indices, i.e., FD-NDNI and FD-SRNI, are the optimal hyperspectral indices for estimating LNC and that the RFR algorithm is the preferred modeling method.

A hybrid inversion method was developed to estimate the leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) of crops. Fifty hyperspectral vegetation indices (VIs), such as the photochemical reflectance index (PRI) and canopy chlorophyll index (CCI), were compared to identify the appropriate VIs for crop LCC and CCC inversion. The hybrid inversion models were then generated from different modelling methods, including the curve-fitting and least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms, by using simulated Compact High Resolution Imaging Spectrometer (CHRIS) datasets that were generated by a radiative transfer model. Finally, the remote-sensing mapping of a CHRIS image was completed to test the inversion accuracy. The results showed that the remote-sensing mapping of the CHRIS image yielded an accuracy of R² = 0.77 and normalized root mean squared error (NRMSE) = 17.34% for the CCC inversion, and an accuracy of only R² = 0.33 and NRMSE = 26.03% for LCC inversion, which indicates that the remote-sensing technique was more appropriate for obtaining chlorophyll content at the canopy scale (CCC) than at the leaf scale (LCC). The estimated results of various VIs and algorithms suggested that the PRI and CCI were the optimal VIs for LCC and CCC inversion, respectively, and RFR was the optimal method for modelling.

Information concerning the spatial and temporal characteristics of vegetative drought is essential for decision‐making and for many environmental and agricultural applications. This study presents a comprehensive spatio‐temporal analysis of vegetative drought conditions that used the vegetation condition index (VCI) data product obtained from NOAA/AVHRR (National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer) to reveal the vegetative drought patterns across China from 1981 to 2015. Considering the characteristics of VCI as drought indicators, multiple methods were designed for these analyses, including drought frequency analysis, trend analysis, anomaly index analysis, and Mann‐Kendall testing. The results show that China is a country with high vegetative drought frequency, but that most areas experienced only slight or moderate periods of drought. In addition, drought has obvious seasonal and geographical differences and occurs with greater frequencies in regions that are highly affected by monsoons such as the northern and southern regions—particularly in the spring and autumn. In contrast, vegetative drought frequencies in regions less affected by monsoons such as the Tibetan Plateau and the northwestern regions were much lower except in northern Sinkiang, eastern Inner Mongolia, and southern Tibet. During 1981–2015, the VCI had an increasing trend in most areas of China (except in the southern region, where it decreased), indicating a reduced frequency of vegetative drought throughout the country. Moreover, the trend was wavelike rather than a one‐way change and could be divided into four phases: (1) a slowly increasing phase from 1981 to 1990, (2) an intensively fluctuating phase from 1991 to 2000, (3) a steadily increasing phase from 2001 to 2010, and (4) a slowly decreasing phase after 2010. Mann‐Kendall analysis further suggested that upward trends of the VCI were more evident in spring and autumn than in summer and that both trends mutated in 1997. Specifically, the trend reached the 0.05 level of significance in the spring.

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.