Search Results (132)

Understanding future changes in crop phenology and climate suitability is essential for sustaining winter wheat production in the Huang-Huai-Hai (3H) Plain under climate change. This study integrates bias-corrected CMIP6 climate projections, the DSSAT CERES-Wheat crop model, and Random Forest analysis to assess spatiotemporal shifts in winter wheat phenology and climate suitability. The assessment focuses on the mid- (2041–2060) and late 21st century (2081–2100) under the SSP2-4.5 and SSP5-8.5 scenarios. The results indicate that the vegetative and whole growing periods (VGP and WGP) will be extended in the mid-century but shorten by the late century. In contrast, the reproductive growing period (RGP) will be slightly reduced in the mid-century and extended under high emissions in the late century. Temperature suitability is projected to increase during the VGP and WGP but decline during the RGP. Precipitation suitability generally improves, except for a decrease during the reproductive period south of 32° N. Solar radiation suitability is expected to decline across all stages. Temperature is identified as the primary driver of phenological changes, with solar radiation and precipitation playing increasingly important roles in the mid- and late 21st century, respectively. Adaptive strategies, including the adoption of heat-tolerant varieties, longer reproductive periods, and earlier sowing, are recommended to enhance yield stability under future climate conditions. Full article

Climate change has contributed to a decline in winter chilling accumulation, a critical requirement for budbreak in temperate fruit crops. Its consequence has been a reduction in fruit production. To compensate for insufficient chilling, hydrogen cyanamide (HC) is widely applied, though its effectiveness remains limited. This study investigated the effect of HC application on budbreak in low-chill kiwifruit under warm conditions by correlating phenological responses with changes in carbohydrate and nitrogen concentrations in bark tissues across bud positions. Phenological observations revealed the highest budbreak percentage and total flower buds at the apical position. HC significantly increased budbreak by 58.82% at the apical position and by 375% at the middle position, with corresponding increases in total flower buds by 148.78% and 1066.67%, respectively. Additionally, shoot lengths were uniform among bud positions in HC-treated canes, whereas non-treated canes showed shoot length heterogeneity. Moreover, HC treatment triggered an earlier and more pronounced reduction in soluble sugars (sucrose and hexoses) concentrations along the gradient from apical to basal bud positions, where the response was strongest at the apical position, which was strongly associated with enhanced budbreak percentages and total flower bud formation. While total nitrogen content was highest in the apical position, it was unaffected by HC application. These findings indicate that HC may promote budbreak by enhancing the mobilization and consumption of soluble sugars for bud growth, thereby improving budbreak performance, flower bud production, and uniform shoot development in low-chill kiwifruit under warm conditions. Full article

Early-season crop mapping plays a critical role in yield estimation, agricultural management, and policy-making. However, most existing methods assign a uniform earliest identification time across provincial or broader extents, overlooking spatial heterogeneity in crop phenology and environmental conditions. This often results in delayed detection or reduced mapping accuracy. To address this issue, we proposed a zonal-based early-season mapping framework for winter wheat by integrating phenological and environmental factors. Aggregation zones across Shandong Province were delineated using Principal Component Analysis (PCA) based on factors such as start of season, end of season, temperature, slope, and others. On this basis, early-season winter wheat identification was conducted for each zone individually. Training samples were generated using the Time-Weighted Dynamic Time Warping (TWDTW) method. Time-series datasets derived from Sentinel-1/2 imagery (2021–2022) were processed on the Google Earth Engine (GEE) platform, followed by feature selection and classification using the Random Forest (RF) algorithm. Results indicated that Shandong Province was divided into four zones (A–D), with Zone D (southwestern Shandong) achieving the earliest mapping by early December with an overall accuracy (OA) of 97.0%. Other zones reached optimal timing between late December and late January, all with OA above 95%. The zonal strategy improved OA by 3.6% compared to the non-zonal approach, demonstrated a high correlation with official municipal-level statistics (R² = 0.97), and surpassed the ChinaWheat10 and ChinaWheatMap10 datasets in terms of crop differentiation and boundary delineation. Historical validation using 2017–2018 data from Liaocheng City, a prefecture-level city in Shandong Province, achieved an OA of 0.98 and an F₁ score of 0.96, further confirming the temporal robustness of the proposed approach. This zonal strategy significantly enhances the accuracy and timeliness of early-season winter wheat mapping at a large scale. Full article

Unmanned aerial vehicle (UAV) remote sensing has emerged as a powerful tool for precision agriculture, offering high-resolution crop monitoring capabilities. However, multi-flight UAV missions introduce radiometric inconsistencies that hinder the accuracy of vegetation indices and physiological trait estimation. This study investigates the efficacy of relative radiometric correction in enhancing canopy chlorophyll content (CCC) estimation for winter wheat. Dual UAV sensor configurations captured multi-flight imagery across three experimental sites and key wheat phenological stages (the green-up, heading, and grain filling stages). Sentinel-2 data served as an external radiometric reference. The results indicate that relative radiometric correction significantly improved spectral consistency, reducing RMSE values (in spectral bands by >86% and in vegetation indices by 38–96%) and enhancing correlations with Sentinel-2 reflectance. The predictive accuracy of CCC models improved after the relative radiometric correction, with validation errors decreasing by 17.1–45.6% across different growth stages and with full-season integration yielding a 44.3% reduction. These findings confirm the critical role of relative radiometric correction in optimizing multi-flight UAV-based chlorophyll estimation, reinforcing its applicability for dynamic agricultural monitoring. Full article

Wheat is the largest terrestrial agricultural crop globally. This study was conducted to determine the soil microbial biomass, soil CO₂ evolution, and physiological profile in the rhizosphere of the winter wheat rain-fed Triticum aestivum along the development stages in a rain-fed semi-arid agro-ecosystem. The data show that a significant, over 100-fold increase in the utilization of four substrate groups (benzoic acid, amino acid, carbohydrates, and carboxylic acid) occurred in the wheat soil rhizosphere along the wheat growth phenology. After the stubble field stage, there was a notable decrease in the utilization of all four substrates. The occurrence of each substrate in the soil aligns with the below-ground rhythm of wheat plant biomass growth. The abundance of fine roots, categorizing wheat plant roots, in the soil at maturity and the stubble field stage may explain the heightened activity and diversity of copiotroph bacteria. This association suggests a potential link between the richness of fine roots and the increased activity and diversity of copiotroph bacteria in the soil. The findings clarify the impact of constraining abiotic factors, coupled with the phenological influences of wheat plants, and their combined effects on substrate utilization by microbial communities in a rain-fed Triticum aestivum wheat field. Full article

Modeling the soil–plant–atmosphere continuum (SPAC) system requires multiple subprocesses and numerous parameters. Sensitivity analysis is effective to identify important model components and improve the modeling efficiency. However, most sensitivity analyses for SPAC models focus on parameter-level assessment, providing limited insights into process-level importance. To address this gap, this study proposes a process sensitivity analysis method that integrates the Bayesian network with variance-based sensitivity measures. Four subprocesses are demarcated based on the physical relationships between model components revealed by the network. Applied to a winter wheat SPAC system under different water conditions, the method effectively and reliably identifies critical processes. The results indicate that, under minimal water stress, the subprocesses of photosynthesis and dry matter partitioning primarily determine agricultural outputs. As the water supply decreases, the subprocesses of soil water movement and evapotranspiration gain increasing importance, becoming predominant under sever water stress. Throughout the crop season, the subprocess importance and its response to water stress are modulated by the crop phenology. Compared to conventional parameter sensitivity analysis, our method excels in synthesizing divergent parameter importance changes and identifying influential subprocesses, even without high-sensitivity parameters. This study provides new insights into adaptive SPAC modeling by dynamically simplifying unimportant subprocesses in response to environmental changes. Full article

Determining the peak flowering dates of winter rapeseed is crucial for both increasing yields and developing tourism resources. Currently, the Normalized Difference Yellow Index (NDYI), widely used for monitoring these dates, faces stability and accuracy issues due to atmospheric interference and limited optical data during the flowering period. This research examines changes in remote-sensing parameters caused by canopy variations during winter rapeseed’s flowering period from crop canopy morphological characteristics and canopy optical properties. By integrating Sentinel-1 and Sentinel-2 data, a new spectral index, the Normalized Backscatter Yellow Vegetation Index (NBYVI), is introduced. The study uses phenological characteristics and the random forest classification algorithm to create a map of winter rapeseed in parts of the middle and lower reaches of the Yangtze River Basin, achieving a Kappa coefficient of 90.57%. It evaluates the effectiveness of crop morphological indices in monitoring growth stages and explores the impacts of elevation and latitude on the peak flowering dates of winter rapeseed. The error ranges for predicting the peak flowering dates with the NDYI (traditional optical index) and the VV (crop morphological index) are generally 2–7 days and 2–6 days, respectively, while the error range for the NBYVI index is generally 0–4 days, demonstrating superior stability and accuracy compared to the NDYI and VV indices. Full article

Barley is an ecologically adaptable crop widely used in agriculture and well suited for organic farming. Satellite imagery from Sentinel-2 can support crop monitoring and yield prediction, optimising production processes. This study compares two types of Sentinel-2 data—standard (S2) data with 10 m and 20 m resolution and Sentinel-2 Deep Resolution 3 (S2DR3) data with 1 m resolution—to assess their (i) relationship with yield in organically grown barley and (ii) utility for winter crop mapping. Vegetation indices were generated and analysed across different phenological phases to determine the most suitable predictors of yield. The results indicate that using 10 × 10 m data, the BBCH-41 phase is optimal for yield prediction, with the Green Chlorophyll Vegetation Index (GCVI; r = 0.80) showing the strongest correlation with yield. In contrast, S2DR3 data with a 1 × 1 m resolution demonstrated that Transformed the Chlorophyll Absorption in Reflectance Index (TCARI), TO, and Normalised Difference Red Edge Index (NDRE1) were consistently reliable across all phenological stages, except for BBCH-51, which showed weak correlations. These findings highlight the potential of remote sensing in organic barley farming and emphasise the importance of selecting appropriate data resolutions and vegetation indices for accurate yield prediction. With the use of three-date spectral band stacks, the Random Forest (RF) and Support Vector Classification (SVC) methods were used to differentiate between wheat, barley, and rapeseed. A five-fold cross-validation approach was applied, training data were stratified with 200 points per crop, and classification accuracy was assessed using the User’s and Producer’s accuracy metrics through pixel-by-pixel comparison with a reference raster. The results for S2 and S2DR3 were very similar to each other, confirming the significant potential of S2DR3 for high-resolution crop mapping. Full article

Agricultural productions are deeply affected by the phenological changes, especially in Shanxi Province, where Southern Shanxi is the main production area of winter wheat. Studying the phenological changes of this region and clarifying the effects of varieties and sowing dates on the phenological characteristics of southern Shanxi can be used for efficient introduction and scientific sowing. We have analyzed the meteorological datasets, phenological period data, and crop management data of seven observation points in the main winter wheat producing areas of Shanxi Province from 1992 to 2021. Trend analysis was used to analyze the time variation trend of various meteorological factors from 1992 to 2021. These results showed that the growth period was mainly advanced, especially in Changzhi and Yuncheng. The sensitivity analysis showed that the growth period of most sites were positively correlated with the sensitivity of various climate factors. Except for jointing to heading stage, the sensitivity of the duration of other growth stages to average temperature was positive, indicating that high temperature had an effect on effective vernalization and early reproductive growth of winter wheat. The modeling results showed that the growth period of winter wheat in Shanxi showed a trend of delay from sowing to ripening, and the sensitivity to temperature showed an increasing trend from sowing to ripening, while the sensitivity to precipitation was the opposite. Meanwhile, an earlier sowing date will make winter wheat develop earlier in warm climate conditions, requiring attention to cold prevention after winter. It is recommended to plant YH-20410 or YH-805 as suitable varieties in the Yuncheng area. In the future, this area can also moderately introduce new varieties with high heat requirements, which can, to some extent, offset the negative impacts of climate change. Full article

Extreme climate events can threaten food production and disrupt supply chains. For instance, the 2023 drought in Catalonia caused large areas of winter cereals to wilt and die early, yielding no grain. This study examined whether Sentinel-2 can detect total crop losses of winter cereals using ground truth data on crop failure. The methodology explored which Sentinel-2 phenological and greenness variables could best predict three drought impact classes: normal growth, moderate impact, and high impact, where the crop failed to produce grain. The results demonstrate that winter cereals affected by drought exhibit a premature decline in several vegetation indices. As a result, the best predictors for detecting total crop losses were metrics associated with the later stages of crop development. Specifically, the mean Normalized Difference Vegetation Index (NDVI) for the first half of May showed the highest correlation with drought impact classes (R² = 0.66). This study is the first to detect total crop losses at the plantation level using field data combined with Sentinel-2 imagery. It also offers insights into rapid monitoring methods for crop failure, an event likely to become more frequent as the climate warms. Full article

Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. This study presents methodological innovations through a multi-level feature enhancement framework that transcends traditional time series analysis. Using Shandong Province, northern China’s agricultural heartland, as a case study, we first established a foundation with time series red-edge vegetation indices (REVI) from Sentinel-2 imagery, uniquely combining the normalized difference red edge index (NDRE₇₀₅) and plant senescence reflectance index (PSRI). Moving beyond conventional time series analysis, we innovatively amplified key temporal characteristics through newly designed spatial feature parameters (SFPs) and phenological feature parameters (PFPs). This strategic enhancement of critical temporal points significantly improved classification performance by capturing subtle spatial patterns and phenological transitions that are often overlooked in traditional approaches. The study yielded three significant findings: (1) The synergistic application of NDRE₇₀₅ and PSRI significantly outperformed single-index approaches, demonstrating the effectiveness of our dual-index strategy; (2) The integration of SFPs and PFPs with time series REVI markedly enhanced feature discrimination at crucial growth stages, with PFPs showing superior capability in distinguishing agricultural land types through amplified phenological signatures; (3) Our optimal classification scheme (FC6), leveraging both enhanced spatial and phenological features, achieved remarkable accuracy (93.21%) with a Kappa coefficient of 0.9159, representing improvements of 4.83% and 0.0538, respectively, over the baseline approach. This comprehensive framework successfully mapped 120,996 km² of agricultural land, differentiating winter wheat–summer maize rotation areas (39.44%), single-season crop fields (36.16%), orchards (14.49%), and facility vegetable fields (9.91%). Our approach advances the field by introducing a robust, scalable methodology that not only utilizes the full potential of time series data but also strategically enhances critical temporal features for improved classification accuracy, particularly valuable for regions with complex farming systems and diverse crop patterns. Full article

The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences in crop growth stages between training and target areas. This study proposes the Adaptive Month Matching (AMM) method to align the phenological stages of crops between training and target areas for enhanced transfer learning in cropland segmentation. In the AMM method, an optimal Sentinel-2 monthly time series is identified in the training area based on deep learning model performance for major crops common to both areas. A month-matching process then selects the optimal Sentinel-2 time series for the target area by aligning the phenological stages between the training and target areas. In this study, the training area covered part of the Mississippi River Delta, while the target areas included diverse regions across the US and Canada. The evaluation focused on major crops, including corn, soybeans, rice, and double-cropped winter wheat/soybeans. The trained deep learning model was transferred to the target areas, and accuracy metrics were compared across different time series chosen by various phenological alignment methods. The AMM method consistently demonstrated strong performance, particularly in transferring to rice-growing regions, achieving an overall accuracy of 98%. It often matched or exceeded other phenological matching techniques in corn segmentation, with an average overall accuracy across all target areas exceeding 79% for cropland segmentation. Full article

Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long-term and sustainable prediction of agricultural yields. Here, we propose FastDTW-HC, a combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method that requires no ground truth input for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK. A series of variables is first derived from the EO products, and these include spectral indices from Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then, the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using the FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale. Full article

The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing is limited by the penetration of sunlight into the canopy depth, and thus directly estimating winter wheat stem biomass via canopy spectra remains a difficult task. There is a stable linear relationship between the stem dry biomass (SDB) and leaf dry biomass (LDB) of winter wheat during the entire growth stage. Therefore, this study comprehensively considered remote sensing and crop phenology, as well as biomass allocation laws, to establish a novel two-component (LDB, SDB) and two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) to estimate SDB across the growth stages of winter wheat. The core of the Tc/Tp-SDB model employed phenological variables (e.g., effective accumulative temperature, EAT) to correct the SDB estimations determined from the LDB. In particular, LDB was estimated using spectral vegetation indices (e.g., red-edge chlorophyll index, CI_{red edge}). The results revealed that the coefficient values (β₀ and β₁) of ordinary least squares regression (OLSR) of SDB with LDB had a strong relationship with phenological variables. These coefficient (β₀ and β₁) relationships were used to correct the OLSR model parameters based on the calculated phenological variables. The EAT and CI_{red edge} were determined as the optimal parameters for predicting SDB with the novel Tc/Tp-SDB model, with r, RMSE, MAE, and distance between indices of simulation and observation (DISO) values of 0.85, 1.28 t/ha, 0.95 t/ha, and 0.31, respectively. The estimation error of SDB showed an increasing trend from the jointing to flowering stages. Moreover, the proposed model showed good potential for estimating SDB from UAV hyperspectral imagery. This study demonstrates the ability of the Tc/Tp-SDB model to accurately estimate SDB across different growing seasons and growth stages of winter wheat. Full article

This study investigates the effects of climate change on the irrigation demand of vegetable crops caused by alteration of climate parameters affecting evapotranspiration (ET), plant development, and growing periods in Central Europe. Utilizing a model framework comprising two varying climate scenarios (RCP 2.6 and RCP 8.5) and two regional climate models (COSMO C-CLM and WETTREG 2013), we calculate the daily crop water balance (CWBc) as a measure for irrigation demand based on reference ET and the temperature-driven duration of crop coefficients until 2100. Our findings for onion show that rising temperatures may shorten cultivation periods by 5 to 17 days; however, the irrigation demand may increase by 5 to 71 mm due to higher ET. By reaching the base temperatures for onion growth earlier in the year, cultivation start can be advanced by up to 30 days. Greater utilization of winter soil moisture reduces the irrigation demand by up to 21 mm, though earlier cultivation is restricted by frost risks. The cultivation of thermophilic crops, however, cannot be advanced to the same extent, as shown for bush beans, and plants will transpire more strongly due to longer dry periods simulated for summer. The results underscore the need for adaptive crop and water management strategies to counteract the simulated changes in phenology and irrigation demand of vegetable crops. Therefore, special consideration must be given to the regional-specific and model- and scenario-dependent simulation results. Full article