Trends and Climate Response in the Phenology of Crops in Northeast China

Crop phenology is the process of crop growth and yield formation, which is largely driven by climatic conditions. It is vital to investigate the shifts in crop phenological processes in response to climate variability. Previous studies often only explored the response of a single crop phenology to climate change, and lacked comparative studies on the climate response in different crop phenology. We intend to investigate the trends in phenological change of three typical crops (i.e., maize, rice and soybean) in Northeast China (NEC) and their response to climate change during 1981–2010. Its main purpose is to reveal the differences in the sensitivity of different crop phenology to key climate factors [e.g., mean temperature (T), accumulated precipitation (AP) and accumulated sunshine hours (AS) during the crop growth period]. We found that the three crops have different phenological changes and varying ranges, and significant spatial heterogeneity in phenological changes. The results indicated that the lengths of different crop growth stages [e.g., the vegetative growth period (VGP), the reproductive growth period (RGP) and the whole growth period (WGP)] were negatively correlated with T, especially in VGP and WGP. However, the lengths of growth period of the three crops were positively correlated with AP and AS. For each 1°C increase in T, the number of days shortened in WGP (about 5 days) was the largest, and that in RGP (less than 2 days) was the smallest. Therefore, the increases in T during past 3 decades have significantly shortened VGP and WGP of three crops, but had slight and inconsistent effects on RGP. Moreover, changes in AP has slight impact on the growth periods of maize and rice, and significantly shortened RGP and WGP of soybean. Changes in AS exerted important and inconsistent effects on the phenology of three crops. This study indicated that there are significant differences in the sensitivity and response of different crop phenology to climate factors. Therefore, in evaluating the response and adaptation of crops to climate change, comparison and comprehensive analysis of multiple crops are helpful to deeply understand the impact of climate change on crop production.

A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion

Dense time-series remote sensing data with detailed spatial information are highly desired for the monitoring of dynamic earth systems. Due to the sensor tradeoff, most remote sensing systems cannot provide images with both high spatial and temporal resolutions. Spatiotemporal image fusion models provide a feasible solution to generate such a type of satellite imagery, yet existing fusion methods are limited in predicting rapid and/or transient phenological changes. Additionally, a systematic approach to assessing and understanding how varying levels of temporal phenological changes affect fusion results is lacking in spatiotemporal fusion research. The objective of this study is to develop an innovative hybrid deep learning model that can effectively and robustly fuse the satellite imagery of various spatial and temporal resolutions. The proposed model integrates two types of network models: super-resolution convolutional neural network (SRCNN) and long short-term memory (LSTM). SRCNN can enhance the coarse images by restoring degraded spatial details, while LSTM can learn and extract the temporal changing patterns from the time-series images. To systematically assess the effects of varying levels of phenological changes, we identify image phenological transition dates and design three temporal phenological change scenarios representing rapid, moderate, and minimal phenological changes. The hybrid deep learning model, alongside three benchmark fusion models, is assessed in different scenarios of phenological changes. Results indicate the hybrid deep learning model yields significantly better results when rapid or moderate phenological changes are present. It holds great potential in generating high-quality time-series datasets of both high spatial and temporal resolutions, which can further benefit terrestrial system dynamic studies. The innovative approach to understanding phenological changes’ effect will help us better comprehend the strengths and weaknesses of current and future fusion models.

Exploring the Applicability and Scaling Effects of Satellite-Observed Spring and Autumn Phenology in Complex Terrain Regions Using Four Different Spatial Resolution Products

The information on land surface phenology (LSP) was extracted from remote sensing data in many studies. However, few studies have evaluated the impacts of satellite products with different spatial resolutions on LSP extraction over regions with a heterogeneous topography. To bridge this knowledge gap, this study took the Loess Plateau as an example region and employed four types of satellite data with different spatial resolutions (250, 500, and 1000 m MODIS NDVI during the period 2001–2020 and ~10 km GIMMS3g during the period 1982–2015) to investigate the LSP changes that took place. We used the correlation coefficient (r) and root mean square error (RMSE) to evaluate the performances of various satellite products and further analyzed the applicability of the four satellite products. Our results showed that the MODIS-based start of the growing season (SOS) and end of the growing season (EOS) were highly correlated with the ground-observed data with r values of 0.82 and 0.79, respectively (p < 0.01), while the GIMMS3g-based phenology signal performed badly (r < 0.50 and p > 0.05). Spatially, the LSP that was derived from the MODIS products produced more reasonable spatial distributions. The inter-annual averaged MODIS SOS and EOS presented overall advanced and delayed trends during the period 2001–2020, respectively. More than two-thirds of the SOS advances and EOS delays occurred in grasslands, which determined the overall phenological changes across the entire Loess Plateau. However, both inter-annual trends of SOS and EOS derived from the GIMMS3g data were opposite to those seen in the MODIS results. There were no significant differences among the three MODIS datasets (250, 500, and 1000 m) with regard to a bias lower than 2 days, RMSE lower than 1 day, and correlation coefficient greater than 0.95 (p < 0.01). Furthermore, it was found that the phenology that was derived from the data with a 1000 m spatial resolution in the heterogeneous topography regions was feasible. Yet, in forest ecosystems and areas with an accumulated temperature ≥10 °C, the differences in phenological phase between the MODIS products could be amplified.

Raised seasonal temperatures reinforce autumn Varroa destructor infestation in honey bee colonies

Varroa destructor is the main pest of the honey bee Apis mellifera, causing colony losses. We investigated the effect of temperature on the autumn abundance of V. destructor in bee colonies over 1991–2020 in Central Europe. We tested the hypothesis that temperature can affect autumn mite populations with different time-lags modulating the bee abundance and brood availability. We showed that raised spring (March–May) and autumn (October) temperatures reinforce autumn V. destructor infestation in the bee colonies. The critical temperature signals embrace periods of bee activity, i.e., just after the first cleansing flights and just before the last observed bee flights, but no direct effects of phenological changes on V. destructor abundance were found. These effects were potentially associated with increased bee reproduction in the specific periods of the year and not with the extended period of activity or accelerated spring onset. We found significant effects of autumn bee abundance, autumn capped brood abundance, and the number of colonies merged on autumn mite infestation. We also observed differences in V. destructor abundance between bees derived from different subspecies. We indicated that climatic effects, through influence on the bee abundance and brood availability, are one of the main drivers regulating V. destructor abundance.

The influence of vegetation water dynamics on the ASCAT backscatter-incidence angle relationship in the Amazon

Microwave observations are sensitive to plant water content and could therefore provide essential information on biomass and plant water status in ecological and agricultural applications. The combined data record of the C-band scatterometers on ERS 1/2, the Metop series and the planned Metop Second Generation satellites will span over 40 years, which would provide a long-term perspective on the role of vegetation in the climate system. Recent research has indicated that the unique viewing geometry of ASCAT could be exploited to observe vegetation water dynamics. The incidence angle dependence of backscatter can be described with a second order polynomial, the slope and curvature of which are related to vegetation. In a study limited to grasslands, seasonal cycles, spatial patterns and interannual variability in the slope and curvature were found to vary among grassland types and were attributed to differences in moisture availability, growing season length and phenological changes. To exploit ASCAT slope and curvature for global vegetation monitoring, their dynamics over a wider range of vegetation types needs to be quantified and explained in terms of vegetation water dynamics. Here, we compare ASCAT data with meteorological data and GRACE Equivalent Water Thickness (EWT) to explain the dynamics of ASCAT backscatter, slope and curvature in terms of moisture availability and demand. We consider differences in the seasonal cycle, diurnal differences, and the response to the 2010 and 2015 droughts across ecoregions in the Amazon basin and surroundings. Results show that spatial and temporal patterns in backscatter reflect moisture availability indicated by GRACE EWT. Slope and curvature dynamics vary considerably among the ecoregions. The evergreen forests, often used as a calibration target, exhibit very stable behaviour even under drought conditions. The limited seasonal variation follows changes in the radiation cycle, and may indicate phenological changes such as litterfall. In contrast, the diversity of land cover types within the Cerrado region results in considerable heterogeneity in terms of the seasonal cycle and the influence of drought on both slope and curvature. Seasonal flooding in forest and savanna areas also produced a distinctive signature in terms of the backscatter as a function of incidence angle. This improved understanding of the incidence angle behaviour of backscatter increases our ability to interpret and make optimal use of the ASCAT data record and VOD products for vegetation monitoring.

Phenological Changes of Soybean in Response to Climate Conditions in Frigid Region in China over the Past Decades

Plant phenology becoming a focus of current research worldwide is a sensitive indicator of global climate change. To understand observed soybean phenology and explore its climatic determinants in frigid region (Northeast China and northeast in Inner Mongolia), we studied the phenological changes of soybean [Glycine max (L.) Merr.] for the frigid region during 1981–2017, then analyzed the contribution of major causal climate factors to phenology based on multiple stepwise regression. Altogether, the average temperature from sowing to maturity (WGP) was significant increasing, accumulated precipitation and sunshine hours were decreasing. More than 50% of observations showed delays in sowing, emergence and maturity stage and short durations of sowing to flowering (VGP), flowering to maturity (RGP) and sowing to maturity (WGP). The late sowing was getting the following phenological timing backward, but the flowering and maturity delaying trends were much less than that of sowing timing due to the warming accelerated growth of soybean. Detailed analysis indicated mean temperature and accumulated precipitation of the 1–3 months immediately preceding the mean emergence, flowering and maturity dates influenced the phenological timing in higher latitude areas (HLJ and FL), while in JL and LN, accumulated precipitation and sunshine hours(replacing mean temperature) were the climatic determinants. These results brought light the importance of research and policy to support strategies for adaptation to local condition under the climate change.

Response of net primary productivity to grassland phenological changes in Xinjiang, China

Determining the relationship between net primary productivity (NPP) and grassland phenology is important for an in-depth understanding of the impact of climate change on ecosystems. In this study, the NPP of grassland in Xinjiang, China, was simulated using the Carnegie-Ames-Stanford approach (CASA) model with Moderate Resolution Imaging Spectroradiometer (MODIS) grassland phenological (MCD12Q2) data to study trends in phenological metrics, grassland NPP, and the relations between these factors from 2001–2014. The results revealed advancement of the start of the growing season (SOS) for grassland in most regions (55.2%) in Xinjiang. The percentage of grassland area in which the end of the growing season (EOS) was delayed (50.9%) was generally the same as that in which the EOS was advanced (49.1%). The percentage of grassland area with an increase in the length of the growing season (LOS) for the grassland area (54.6%) was greater than that with a decrease in the LOS (45.4%). The percentage of grassland area with an increase in NPP (61.6%) was greater than that with a decrease in NPP (38.4%). Warmer regions featured an earlier SOS and a later EOS and thus a longer LOS. Regions with higher precipitation exhibited a later SOS and an earlier EOS and thus a shorter LOS. In most regions, the SOS was earlier, and spring NPP was higher. A linear statistical analysis showed that at various humidity (K) levels, grassland NPP in all regions initially increased but then decreased with increasing LOS. At higher levels of K, when NPP gradually increased, the LOS gradually decreased.