Temporal Pattern Analysis of Cropland Phenology in Shandong Province of China Based on Two Long-Sequence Remote Sensing Data

Ren, Shuang; An, Shuai

doi:10.3390/rs13204071

Cited by 4 publications

(4 citation statements)

References 47 publications

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“…RH influences seed germination and seedling growth in plants. Higher RH can facilitate rapid seed water absorption and germination under appropriate moisture conditions, favoring earlier crop growth 2 . However, excessive moisture may lead to overly wet soil, affecting seed oxygen supply root system growth, thereby delaying the onset of growth.…”

Section: Discussionmentioning

confidence: 99%

“…As an exceptionally responsive bio-indicator, vegetation phenology is a particularly important asset to climate change, making it an invaluable tool for monitoring ecological responses to shifting climate conditions 3 , 6 – 8 . Over recent decades, the dynamics of vegetation phenology have garnered global attention, highlighting the intricate interplay between phenology and climatic factors 2 , 9 , 10 . Consequently, comprehensive investigations into ecosystem responses to climate variables are essential for deepening our comprehension of vegetation dynamics within the context of climate change 11 .…”

Section: Introductionmentioning

confidence: 99%

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Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes

Wang,

Chen,

et al. 2024

Sci Rep

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Previous studies have primarily focused on the influence of temperature and precipitation on phenology. It is unclear if the easily ignored climate factors with drivers of vegetation growth can effect on vegetation phenology. In this research, we conducted an analysis of the start (SOS) and end (EOS) of the growing seasons in the northern region of China above 30°N from 1982 to 2014, focusing on two-season vegetation phenology. We examined the response of vegetation phenology of different vegetation types to preseason climatic factors, including relative humidity (RH), shortwave radiation (SR), maximum temperature (Tmax), and minimum temperature (Tmin). Our findings reveal that the optimal preseason influencing vegetation phenology length fell within the range of 0–60 days in most areas. Specifically, SOS exhibited a significant negative correlation with Tmax and Tmin in 44.15% and 42.25% of the areas, respectively, while EOS displayed a significant negative correlation with SR in 49.03% of the areas. Additionally, we identified that RH emerged as the dominant climatic factor influencing the phenology of savanna (SA), whereas temperature strongly controlled the SOS of deciduous needleleaf forest (DNF) and deciduous broadleaf forest (DBF). Meanwhile, the EOS of DNF was primarily influenced by Tmax. In conclusion, this study provides valuable insights into how various vegetation types adapt to climate change, offering a scientific basis for implementing effective vegetation adaptation measures.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes

Wang,

Chen,

et al. 2024

Sci Rep

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“…Accordingly, Piao et al [30] recommended continuous monitoring and analysis using satellite observations to verify whether and how the trends in phenological parameters will continue in the coming decades. Several studies [31][32][33] have noted that, despite the interest in detecting the phenological dynamics of cultivated plants at a regional scale, most researchers did not consider it due to the perception that it was influenced by anthropogenic activities. Thus, instead of focusing on cultivated plants, attention has been mostly focused towards phenological changes in natural vegetation.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, instead of focusing on cultivated plants, attention has been mostly focused towards phenological changes in natural vegetation. Moreover, in the few cases where cultivated plants have been considered, they were small-scale studies based on in situ field data [32]. Therefore, assessments of plant phenology covering both the periods before and after the global warming hiatus are needed, especially for cultivated plants.…”

Section: Introductionmentioning

confidence: 99%

Recent Cereal Phenological Variations under Mediterranean Conditions

Benito-Verdugo,

González-Zamora,

Martínez-Fernández

2024

Remote Sensing

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This study analyzes the temporal patterns of rainfed cereal phenology extracted from the GIMMS NDVI3g dataset in the main cereal-growing regions under a Mediterranean climate in Spain, Portugal, France and Italy during the period 1982–2022. The series before and after the beginning of the 21st century were analyzed separately. Phenological parameters were extracted using the modified dynamic threshold method, and their trends were analyzed. Correlation analyses were performed to study the relationships among these parameters and to analyze the influence of hydroclimatic variables on the start (SOS) and end (EOS) of the growing season. Results showed a temporal reversal in phenological trends between both study periods, coinciding with the global warming hiatus. In the first period (1982–2002), SOS and EOS advanced (−7.5 and −3.1 days, respectively), and the length of growing season (LOS) increased. However, during the second stage (2003–2022), SOS and EOS were delayed (7.5 and 1.7 days, respectively), and LOS decreased. Similar dynamics were observed for the influence of the hydroclimatic variables on SOS and EOS, stronger in the first period and weaker in the second. This study provides valuable information on the phenological dynamics of rainfed cereals that may be useful for their management and planning in climate change scenarios.

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A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery

Seydi

Amani²,

Ghorbanian

2022

Remote Sensing

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Accurate and timely mapping of crop types and having reliable information about the cultivation pattern/area play a key role in various applications, including food security and sustainable agriculture management. Remote sensing (RS) has extensively been employed for crop type classification. However, accurate mapping of crop types and extents is still a challenge, especially using traditional machine learning methods. Therefore, in this study, a novel framework based on a deep convolutional neural network (CNN) and a dual attention module (DAM) and using Sentinel-2 time-series datasets was proposed to classify crops. A new DAM was implemented to extract informative deep features by taking advantage of both spectral and spatial characteristics of Sentinel-2 datasets. The spectral and spatial attention modules (AMs) were respectively applied to investigate the behavior of crops during the growing season and their neighborhood properties (e.g., textural characteristics and spatial relation to surrounding crops). The proposed network contained two streams: (1) convolution blocks for deep feature extraction and (2) several DAMs, which were employed after each convolution block. The first stream included three multi-scale residual convolution blocks, where the spectral attention blocks were mainly applied to extract deep spectral features. The second stream was built using four multi-scale convolution blocks with a spatial AM. In this study, over 200,000 samples from six different crop types (i.e., alfalfa, broad bean, wheat, barley, canola, and garden) and three non-crop classes (i.e., built-up, barren, and water) were collected to train and validate the proposed framework. The results demonstrated that the proposed method achieved high overall accuracy and a Kappa coefficient of 98.54% and 0.981, respectively. It also outperformed other state-of-the-art classification methods, including RF, XGBOOST, R-CNN, 2D-CNN, 3D-CNN, and CBAM, indicating its high potential to discriminate different crop types.

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Temporal Pattern Analysis of Cropland Phenology in Shandong Province of China Based on Two Long-Sequence Remote Sensing Data

Cited by 4 publications

References 47 publications

Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes

Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes

Recent Cereal Phenological Variations under Mediterranean Conditions

A Dual Attention Convolutional Neural Network for Crop Classification Using Time-Series Sentinel-2 Imagery

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