As the second largest rice producer, India contributes about 20% of the world’s rice production. Timely, accurate, and reliable rice yield prediction in India is crucial for global food security and health issues. Deep learning models have achieved excellent performances in predicting crop yield. However, the interpretation of deep learning models is still rare. In this study, we proposed a transformer-based model, Informer, to predict rice yield across the Indian Indo-Gangetic Plains by integrating time-series satellite data, environmental variables, and rice yield records from 2001 to 2016. The results showed that Informer had better performance (R² = 0.81, RMSE = 0.41 t/ha) than four other machine learning and deep learning models for end-of-season prediction. For within-season prediction, the Informer model could achieve stable performances (R² ≈ 0.78) after late September, which indicated that the optimal prediction could be achieved 2 months before rice maturity. In addition, we interpreted the prediction models by evaluating the input feature importance and analyzing hidden features. The evaluation of feature importance indicated that NIR_V was the most critical factor, while intervals 6 (mid-August) and 12 (mid-November) were the key periods for rice yield prediction. The hidden feature analysis demonstrated that the attention-based long short-term memory (AtLSTM) model accumulated the information of each growth period, while the Informer model focused on the information around intervals 5 to 6 (August) and 11 to 12 (November). Our findings provided a reliable and simple framework for crop yield prediction and a new perspective for explaining the internal mechanism of deep learning models.

Photosynthesis is arguably the most important biochemical process on Earth, which is dramatically influenced by environmental conditions. How environmental factors drive stomatal conductance and biochemical processes of leaf photosynthetic rate has not been sufficiently investigated in subtropical China. In this study, we analysed the effects of stomatal and biochemical parameters on the photosynthetic rate of native Masson’s pine (Pinus massoniana Lamb.) and exotic slash pine (Pinus elliottii Engelm.) in response to leaf temperature and vapour pressure deficit (VPD) in subtropical China, based on leaf gas exchange measurements in 2016. Our results showed that there was no significant difference in the light-saturated photosynthetic rate (A_sat) between native Masson’s pine and exotic slash pine. The seasonal patterns of maximum rate of the carboxylation (V_cmax25) were basically consistent with seasonal patterns of A_sat for both species. The positive effect of leaf temperature on A_sat was mainly produced through its positive effect on V_cmax25. Leaf temperature had no significant effect on stomatal conductance. V_cmax25 and g_s simultaneously affected A_sat in response to VPD. Our results highlighted the importance of biochemical processes in limiting leaf photosynthetic rate in response to environmental conditions in subtropical evergreen coniferous plantations.

Global warming has triggered a surge in severe drought worldwide, disrupting vegetation photosynthesis and profoundly altering the global carbon cycle. However, the immediate and time-lagged responses of vegetation photosynthesis to warming drought remain unclear. Most current research on regional-scale solar-induced chlorophyll fluorescence (SIF) drought stress relies on daily or even monthly datasets, limiting insights into the diurnal dynamics of photosynthesis under drought. To address these gaps, we developed a continuous hourly SIF dataset (HC-SIF _OCO ) based on observations from Orbiting Carbon Observatory-2 (OCO-2) and OCO-3. HC-SIF _OCO exhibited accuracy comparable to that of tower-based observations in terms of diurnal photosynthesis [SIF: R ² ≥ 0.89, GPP (gross primary productivity): R ² ≥ 0.94]. Then, we used this dataset to investigate the drought in the Yangtze River Basin in 2022. According to our analysis, the initial signs of drought-induced stress are reflected in a decrease in vegetation fluorescence efficiency, followed by anomalies in SIF and, finally, abnormalities in canopy structure. Drought has led to an approximately 3% increase in midday depression compared to previous years while also causing an advancement in the seasonal peak. Furthermore, immediate decreases in atmospheric moisture were responsible for more than 70% of the decline in vegetation photosynthesis, with soil dryness playing a 2-month-later role. Although temperature generally benefited photosynthesis, this effect diminished dramatically at midday. In summary, we present a new method for obtaining high-resolution temporal SIF data, providing new insights into the vegetation’s diurnal response to drought.

Light use efficiency (LUE) is a key indicator of vegetation photosynthesis, which provides important insights into how vegetation productivity responds to environmental conditions. The photochemical reflectance index (PRI) is based on reflectance at 531 and 570 nm, which reflects the xanthophyll cycle process of plants under different radiation conditions, and makes LUE related to plant optical characteristics. In this study, tower-based PRI and eddy covariance (EC) based LUEs were used to explore the ability of PRI to track LUE variations in a subtropical, evergreen mixed forest in South China. The results indicate that there is a stronger relationship between PRI and LUE, corrected by the bidirectional reflectance distribution function (BRDF), where R² = 0.46 before correction and R² = 0.60 after correction. Generally, PRI is able to capture diurnal and seasonal changes in LUE. Simultaneously, this study highlights a significant correlation between LUE and PRI, but there is also a large seasonal difference in its correlation. The correlation in winter was significantly stronger than summer. The strongest correlation is found in November (R² = 0.91) and the weakest is in July (R² = 0.34). Photosynthetically active radiation (PAR) had a strong influence on the LUE-PRI relationship, while vapor pressure deficit (VPD) and air temperature (Ta) had negative influences on the relationship between LUE and PRI. Terrestrial laser scanning is used to retrieve the vertical structure of forest crown. Our results show that the vegetation canopy structure (i.e., effective leaf area index, LAI_e), extracted from terrestrial laser scanning (TLS) point data in subtropical mixed forests, had a weak influence on LUE. Our research suggests that environmental factors and vegetation canopy structures should be considered when using PRI to accurately estimate LUE.

In terrestrial ecosystems, leaves are aggregated into different spatial structures and their spatial distribution is non-random. Clumping index (CI) is a key canopy structural parameter, characterizing the extent to which leaf deviates from the random distribution. To assess leaf clumping effects on global terrestrial ET, we used a global leaf area index (LAI) map and the latest version of global CI product derived from MODIS BRDF data as well as the Boreal Ecosystem Productivity Simulator (BEPS) to estimate global terrestrial ET. The results show that global terrestrial ET in 2015 was 511.9 ± 70.1 mm yr⁻¹ for Case I, where the true LAI and CI are used. Compared to this baseline case, (1) global terrestrial ET is overestimated by 4.7% for Case II where true LAI is used ignoring clumping; (2) global terrestrial ET is underestimated by 13.0% for Case III where effective LAI is used ignoring clumping. Among all plant functional types (PFTs), evergreen needleleaf forests were most affected by foliage clumping for ET estimation in Case II, because they are most clumped with the lowest CI. Deciduous broadleaf forests are affected by leaf clumping most in Case III because they have both high LAI and low CI compared to other PFTs. The leaf clumping effects on ET estimation in both Case II and Case III is robust to the errors in major input parameters. Thus, it is necessary to consider clumping effects in the simulation of global terrestrial ET, which has considerable implications for global water cycle research.