Vegetation greenness and photosynthetic phenology in response to climatic determinants

Vegetation phenology is a key indicator of vegetation-climate interactions and carbon sink changes in ecosystems. Therefore, it is very important to understand the temporal and spatial variability of vegetation phenology and the driving climatic determinants [e.g., temperature (Ts) and soil moisture (SM)]. Vegetation greenness and photosynthetic phenology were derived using the double logistic (DL) method to enhance vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF) spring and autumn phenology, respectively. The growing season length (GSL) of greenness phenology (about 100 days) derived EVI was longer than GSL of photosynthetic phenology (about 80 days) derived SIF. Although their overall spatiotemporal pattern trends were consistent, photosynthetic phenology varied 1.4 to 3.1 times more than greenness phenology over time. In addition, SIF-based photosynthetic phenology and EVI-based greenness phenology showed consistent factors of drivers but differed to some extent in spatial patterns and the most relevant preseason dates. Spring photosynthetic phenology was mainly influenced by pre-season mean cumulative Ts (about 90 days). However, greenness phenology was controlled by both pre-seasons mean cumulative Ts [(about 55 days) and mean cumulative SM (about 40 days)]. Autumn photosynthetic phenology was controlled by both periods’ mean cumulative Ts [(about 20 days) and SM (about 20 days)], but autumn greenness phenology was mainly influenced by pre-season mean cumulative Ts (85 days). The comparison analysis of SIF and EVI phenology helps to understand the difference between photosynthetic phenology and greenness phenology at a regional scale

Autumn Crop Yield Prediction using Data-Driven Approaches:- Support Vector Machines, Random Forest, and Deep Neural Network Methods

Accurate prediction of crop yield before harvest is critical to food security and importation. The calculated ten explanatory factors and autumn crop yield data were used as data sources in this research. Firstly, a Redundancy Analysis (RDA) was employed to carry out explanatory factors and feature selection. The simple effects of RDA were used to evaluate the interpretation rates of the explanatory factors. The conditional effects of RDA were adopted to select the features of the explanatory factors. Then, the autumn crop yield was divided into the training set and testing set with an 80/20 ratio, using Support Vector Regression (SVR), Random Forest Regression (RFR), and deep neural network (DNN) for the model, respectively. Finally, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) were used to evaluate the performance of the model comprehensively. The results showed that the interpretation rates of the explanatory factors ranged from 54.3% to 85.0% (p = 0.002), which could reflect the autumn crop yields well. When a small number of sample training data (e.g., 80 samples) was used, the DNN model performed better than both SVR and RF models

Climate warming-induced phenology changes dominate vegetation productivity in Northern Hemisphere ecosystems

The climate change is expected to trigger changes in vegetation phenology, temperature, and soil moisture (SM), altering the productivity of ecosystems. Despite numerous existing efforts, however, their contradicting conclusions suggest that how vegetation productivity is impacted by these factors still remains unclear in the Northern Hemisphere ecosystems (≥25°N). This study used the optimal fingerprint (OFP) method and redundancy analysis (RDA) to attribute the importance of key drivers of vegetation productivity from 2001 to 2019 based on long-term remote sensing and FLUXNET observation data. The results showed that solar-induced chlorophyll fluorescence (SIF), gross primary productivity (GPP), and net primary productivity (NPP) were increased in 72.01% to 88.04% of the vegetation areas. We observed that the correlation between vegetation productivity and spring phenology, autumn phenology, growing season length (GSL), SM, temperature reached 99% significance level, where early spring phenology, delayed autumn phenology, extended GSL, increased SM, and elevated temperature all enhanced ecosystem productivity, with GSL being the most important factor driving vegetation productivity. In addition, the pixel-wise attribution analysis indicated that GSL, as the dominant driver, accounted for 30.24% of the vegetation productivity, followed by temperature (23.79%), spring phenology (19.56%), autumn phenology (14.09%), and SM (12.31%), all of which were dominated by positive effects (54.19% to 73.14%). The results from this study serve as important references that benefit our understanding of driving mechanisms of temperature-phenology-SM interactions on ecosystem productivity

Data_Sheet_1_Vegetation greenness and photosynthetic phenology in response to climatic determinants.pdf

Vegetation phenology is a key indicator of vegetation-climate interactions and carbon sink changes in ecosystems. Therefore, it is very important to understand the temporal and spatial variability of vegetation phenology and the driving climatic determinants [e.g., temperature (Ts) and soil moisture (SM)]. Vegetation greenness and photosynthetic phenology were derived using the double logistic (DL) method to enhance vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF) spring and autumn phenology, respectively. The growing season length (GSL) of greenness phenology (about 100 days) derived EVI was longer than GSL of photosynthetic phenology (about 80 days) derived SIF. Although their overall spatiotemporal pattern trends were consistent, photosynthetic phenology varied 1.4 to 3.1 times more than greenness phenology over time. In addition, SIF-based photosynthetic phenology and EVI-based greenness phenology showed consistent factors of drivers but differed to some extent in spatial patterns and the most relevant preseason dates. Spring photosynthetic phenology was mainly influenced by pre-season mean cumulative Ts (about 90 days). However, greenness phenology was controlled by both pre-seasons mean cumulative Ts [(about 55 days) and mean cumulative SM (about 40 days)]. Autumn photosynthetic phenology was controlled by both periods’ mean cumulative Ts [(about 20 days) and SM (about 20 days)], but autumn greenness phenology was mainly influenced by pre-season mean cumulative Ts (85 days). The comparison analysis of SIF and EVI phenology helps to understand the difference between photosynthetic phenology and greenness phenology at a regional scale.</p

Global vegetation productivity increased in response to COVID-19 restrictions

The novel coronavirus 2019 (COVID-19)-imposed restrictions in 2020 and 2021 led to a notable reduction in human activity, providing an opportunity to study the impact of human activity on global vegetation productivity. The impacts on vegetation productivity are of particular interest, as vegetation carbon sinks serve as one of the main pathways for carbon neutrality. This study investigated the impacts of the COVID-19 pandemic restrictions on global vegetation productivity in 2020 and 2021 by leveraging remotely sensed big data and model data. This study revealed reduced atmospheric emissions and increased radiation reaching the surface in these two years. Compared to the time period from 2017 to 2019, global vegetation productivity increased by 1.95% and 1.15% in 2020 and 2021, respectively, with a majority of countries hit by the COVID-19 pandemic showing enhanced vegetation productivity. This study conclude that a sudden reduction in human activities due to COVID-19 restrictions plays a positive role in global vegetation productivity and carbon neutrality. The widely implemented COVID-19 control measures at the global scale allow scholars to observe the responding mechanism of vegetation productivity, greatly benefiting the rethinking of existing sustainable development strategies.</p