Phenological Maps Research Articles

Phenological Mapping of Invasive Insects: Decision Support for Surveillance and Management.

Readily accessible and easily understood forecasts of the phenology of invasive insects have the potential to support and improve strategic and tactical decisions for insect surveillance and management. However, most phenological modeling tools developed to date are site-based, meaning that they use data from a weather station to produce forecasts for that single site. Spatial forecasts of phenology, or phenological maps, are more useful for decision-making at area-wide scales, such as counties, states, or entire nations. In this review, we provide a brief history on the development of phenological mapping technologies with a focus on degree-day models and their use as decision support tools for invasive insect species. We compare three different types of phenological maps and provide examples using outputs of web-based platforms that are presently available for real-time mapping of invasive insects for the contiguous United States. Next, we summarize sources of climate data available for real-time mapping, applications of phenological maps, strategies for balancing model complexity and simplicity, data sources and methods for validating spatial phenology models, and potential sources of model error and uncertainty. Lastly, we make suggestions for future research that may improve the quality and utility of phenological maps for invasive insects.

A temporally and spatially explicit, data-driven estimation of airborne ragweed pollen concentrations across Europe

Ongoing and future climate change driven expansion of aeroallergen-producing plant species comprise a major human health problem across Europe and elsewhere. There is an urgent need to produce accurate, temporally dynamic maps at the continental level, especially in the context of climate uncertainty. This study aimed to restore missing daily ragweed pollen data sets for Europe, to produce phenological maps of ragweed pollen, resulting in the most complete and detailed high-resolution ragweed pollen concentration maps to date. To achieve this, we have developed two statistical procedures, a Gaussian method (GM) and deep learning (DL) for restoring missing daily ragweed pollen data sets, based on the plant's reproductive and growth (phenological, pollen production and frost-related) characteristics. DL model performances were consistently better for estimating seasonal pollen integrals than those of the GM approach. These are the first published modelled maps using altitude correction and flowering phenology to recover missing pollen information. We created a web page (http://euragweedpollen.gmf.u-szeged.hu/), including daily ragweed pollen concentration data sets of the stations examined and their restored daily data, allowing one to upload newly measured or recovered daily data. Generation of these maps provides a means to track pollen impacts in the context of climatic shifts, identify geographical regions with high pollen exposure, determine areas of future vulnerability, apply spatially-explicit mitigation measures and prioritize management interventions.

Paddy Rice Phenological Mapping throughout 30-Years Satellite Images in the Honghe Hani Rice Terraces

The Honghe Hani Rice Terraces represent the coexistence between natural and cultural systems. Despite being listed as a World Heritage Site in 2013, certain natural and anthropogenic factors have changed land use/land cover, which has led to a reduction in the size of the paddy rice area. It is difficult to accurately assess these changes due to the lack of historical maps of paddy rice croplands with fine spatial resolution. Therefore, we integrated a random forest classifier and phenological information to improve mapping accuracy and stability. We then mapped the historical distribution of land use/land cover in the Honghe Hani Rice Terraces from 1989–1991 to 2019–2021 using the Google Earth Engine. Finally, we analyzed the driving forces of land use types in the Honghe Hani Rice Terraces. We found that: (1) forests, shrubs or grasslands, and other croplands could be discriminated from paddy rice during the flooding and transplanting period, and water bodies and buildings could also be discriminated from paddy rice during the growing and harvesting period. (2) Inputting phenological feature data improved mapping accuracy and stability compared with single phenological periods. (3) In the past thirty years, 10.651%, 8.810%, and 5.711% of paddy rice were respectively converted to forests, shrubs or grasslands, and other croplands in the Honghe Hani Rice Terraces. (4) Lower agricultural profits and drought led to problems in identifying the driving mechanisms behind paddy rice distribution changes. This study demonstrates that phenological information can improve the mapping accuracy of rice terraces. It also provides evidence for the change in the size of the rice terrace area and associated driving forces in Southwest China.

Detecting crop phenology from vegetation index time-series data by improved shape model fitting in each phenological stage

Crop phenology provides important information for crop growth management and yield estimations. The popular shape model fitting (SMF) method detects crop phenology from vegetation index (VI) time-series data, but it has two limitations. First, SMF assumes the same “relative position” of phenological stages for the pixels of the same crop type. This assumption is valid only if all target pixels, relative to the shape model, display a synchronized increase (or decrease) in length between any two phenological stages, which is uncommon in practice. Second, the variance in the resulting phenology estimates for a particular phenological stage is related to the stage itself; this makes it challenging to simulate spatial and temporal variations in crop phenology using SMF. Here, we address both limitations by developing the shape model fitting by the Separate phenological stage method (“SMF-S"). SMF-S uses a modified fitting function and an iterative procedure to match the shape model with the VI time series for each phenological stage in an adaptive local window. Comparisons between SMF-S and SMF in simulation experiments show the superior performance of SMF-S in different scenarios, regardless of noise. Comparisons involving winter wheat field observations from the North China Plain showed that the RMSE values averaged over nine phenological stages were smaller for SMF-S (RMSE = 9.5 d) than for SMF (RMSE = 13.4 d) and one variant of SMF (the shape model with accumulated growing degree days (SM-AGDD); RMSE=33.6 d). Moreover, SMF-S better described the spatial variations (i.e., variance) in the results and captured the temporal shifts in multiple phenological stages. In the derived regional phenology maps of winter wheat on the North China Plain, SMF-S generated more reasonable spatial patterns, whereas SMF underestimated (overestimated) the variance in the early (late) phenological stages. We expect that the improved crop phenology estimates obtained with SMF-S could benefit various agricultural activities.

Mapping paddy rice and rice phenology with Sentinel-1 SAR time series using a unified dynamic programming framework

AbstractMonitoring rice planting areas and their phenological phases is crucial for yield estimation and informed decision-making. This study proposed a unified method for mapping rice field and rice phenology with a dynamic time wrapping (DTW) distance-based classifier and its variant sub-DTW algorithm using Sentinel-1’s synthetic aperture radar (SAR) VH band. Field samplings were conducted for broad landcover types in one of the areas of interest (AOIs). We implemented a pixel-wisek-nearest neighbor classification model with DTW distance to identify paddy rice pixels. Standard rice phenological profiles of the SAR VH band were defined by ground monitoring of a sample rice field. Based on rice planting maps and the standard phenological profiles, rice phenological phases were estimated by pattern matching strategy with the sub-DTW algorithm. Experiments on six counties in Northeast China presented promising results. The overall producer and user accuracy reached 92.9 and 91.9% for rice mapping, respectively. The mean root mean square error (RMSE) for phenology estimation was 3.5 days. Rice planting and rice phenology maps were generated for the six AOIs. The phenological variances of the AOIs implied the effects of climate and rice cultivars on phenological development.

Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI.

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.

Rice phenology mapping using novel target characterization parameters from polarimetric SAR data

ABSTRACT We require spatio-temporal information about rice for executing and planning diverse management practices. In this regard, data obtained from Synthetic Aperture Radar (SAR) sensors are well suited for tracking morphological developments of rice across its phenology stages. This study proposes different target characterization parameters from polarimetric SAR data for rice phenology mapping. Six C-band Radarsat-2 images acquired over Vijayawada, India, are used for complete analysis. It is known that polarimetric information provides excellent sensitivity for identifying crop phenology stages. Hence, in this study, we assessed phenology classification results using a scattering-type parameter and scattering powers for full-polarimetric (FP) and extracted dual-polarimetric (DP) SAR data. Here, we utilized the real 44 Kennaugh matrix elements to derive these parameters equivalently for the two polarimetric modes (i.e. FP and DP). We obtained better overall classification accuracy for each phenology stages using the proposed parameters than the existing ones from FP and DP SAR data. We noted that the overall classification accuracy using the DP SAR data was only marginally lower than the FP SAR data. This marginal difference in the accuracies could be due to the absence of the cross-polarized component in the DP SAR data. We also demonstrate the usefulness of the scattering powers from DP SAR data for rice phenology monitoring.

Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe

Vegetation phenology obtained from time series of remote sensing data is relevant for a range of ecological applications. The freely available Sentinel-2 imagery at a 10 m spatial resolution with a ~ 5-day repeat cycle provides an opportunity to map vegetation phenology at an unprecedented fine spatial scale. To facilitate the production of a Europe-wide Copernicus Land Monitoring Sentinel-2 based phenology dataset, we design and evaluate a framework based on a comprehensive set of ground observations, including eddy covariance gross primary production (GPP), PhenoCam green chromatic coordinate (GCC), and phenology phases from the Pan-European Phenological database (PEP725). We test three vegetation indices (VI) — the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2), and the plant phenology index (PPI) — regarding their capability to track the seasonal trajectories of GPP and GCC and their performance in reflecting spatial variabilities of the corresponding GPP and GCC phenometrics, i.e., start of season (SOS) and end of season (EOS). We find that for GPP phenology, PPI performs the best, in particular for evergreen coniferous forest areas where the seasonal variations in leaf area are small and snow is prevalent during wintertime. Results are inconclusive for GCC phenology, for which no index is consistently better than the others. When comparing to PEP725 phenology phases, PPI and EVI2 perform better than NDVI regarding the spatial correlation and consistency (i.e., lower standard deviation). We also link VI phenometrics at various amplitude thresholds to the PEP725 phenophases and find that PPI SOS at 25% and PPI EOS at 15% provide the best matches with the ground-observed phenological stages. Finally, we demonstrate that applying bidirectional reflectance distribution function correction to Sentinel-2 reflectance is a step that can be excluded for phenology mapping in Europe.

Evolution and application of digital technologies to predict crop type and crop phenology in agriculture

AbstractThe downside risk of crop production affects the entire supply chain of the agricultural industry nationally and globally. This also has a profound impact on food security, and thus livelihoods, in many parts of the world. The advent of high temporal, spatial and spectral resolution remote sensing platforms, specifically during the last 5 years, and the advancement in software pipelines and cloud computing have resulted in the collating, analysing and application of ‘BIG DATA’ systems, especially in agriculture. Furthermore, the application of traditional and novel computational and machine learning approaches is assisting in resolving complex interactions, to reveal components of ecophysiological systems that were previously deemed either ‘too difficult’ to solve or ‘unseen’. In this review, digital technologies encompass mathematical, computational, proximal and remote sensing technologies. Here, we review the current state of digital technologies and their application in broad-acre cropping systems globally and in Australia. More specifically, we discuss the advances in (i) remote sensing platforms, (ii) machine learning approaches to discriminate between crops and (iii) the prediction of crop phenological stages from both sensing and crop simulation systems for major Australian winter crops. An integrated solution is proposed to allow accurate development, validation and scalability of predictive tools for crop phenology mapping at within-field scales, across extensive cropping areas.

A Threshold Method for Robust and Fast Estimation of Land-Surface Phenology Using Google Earth Engine

Cloud-based platforms are changing the way of analyzing remotely sensed data by providing high computational power and rapid access to massive volumes of data. Several types of studies use cloud-based platforms for global-scale analyses, but the number of land-surface phenology (LSP) studies that use cloud-based platforms is low. We analyzed the performance of the state-of-the-art LSP algorithms and propose a new threshold-based method that we implemented in Google Earth Engine (GEE). This new LSP method, called maximum separation (MS) method, applies a moving window that estimates the ratio of observations that exceed a given threshold before and after the central day. The start and end of the growing season are the days of the year when the difference between the ratios before and after the central day are minimal and maximal. The MODIS phenology metrics estimated with the MS method showed similar performances as traditional threshold methods when compared with ground estimations derived from the PhenoCam dataset, a network of digital cameras that provides near-surface remotely sensed observations of vegetation phenology. The main advantage of the MS method is that it can be directly applied to daily nonsmoothed time series without any additional preprocessing steps. The implementation of the proposed method in GEE allowed the processing of global phenological maps derived from MODIS. The distribution of code in GEE allows the reproducibility of results and the rapid processing of LSP metrics by the scientific community.

Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series

The high spatial resolution and revisit time of Sentinel-2A/B tandem satellites allow a potentially improved retrieval of land surface phenology (LSP). The biome and regional characteristics, however, greatly constrain the design of the LSP algorithms. In the Arctic, such biome-specific characteristics include prolonged periods of snow cover, persistent cloud cover, and shortness of the growing season. Here, we evaluate the feasibility of Sentinel-2 for deriving high-resolution LSP maps of the Arctic. We extracted the timing of the start and end of season (SoS and EoS, respectively) for the years 2019 and 2020 with a simple implementation of the threshold method in Google Earth Engine (GEE). We found a high level of similarity between Sentinel-2 and PhenoCam metrics; the best results were observed with Sentinel-2 enhanced vegetation index (EVI) (root mean squared error (RMSE) and mean error (ME) of 3.0 d and −0.3 d for the SoS, and 6.5 d and −3.8 d for the EoS, respectively), although other vegetation indices presented similar performances. The phenological maps of Sentinel-2 EVI compared well with the same maps extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) in homogeneous landscapes (RMSE and ME of 9.2 d and 2.9 d for the SoS, and 6.4 and −0.9 d for the EoS, respectively). Unreliable LSP estimates were filtered and a quality flag indicator was activated when the Sentinel-2 time series presented a long period (>40 d) of missing data; discontinuities were lower in spring and early summer (9.2%) than in late summer and autumn (39.4%). The Sentinel-2 high-resolution LSP maps and the GEE phenological extraction method will support vegetation monitoring and contribute to improving the representation of Artic vegetation phenology in land surface models.

PPMaP: Reproducible and Extensible Open-Source Software for Plant Phenological Phase Duration Prediction and Mapping in Sub-Saharan Africa

Understanding the detailed timing of crop phenology and their variability enhances grain yield and quality by providing precise scheduling of irrigation, fertilization, and crop protection mechanisms. Advances in information and communication technology (ICT) provide a unique opportunity to develop agriculture-related tools that enhance wall-to-wall upscaling of data outputs from point-location data to wide-area spatial scales. Because of the heterogeneity of the worldwide agro-ecological zones where crops are cultivated, it is unproductive to perform plant phenology research without providing means to upscale results to landscape-level while safeguarding field-scale relevance. This paper presents an advanced, reproducible, and open-source software for plant phenology prediction and mapping (PPMaP) that inputs data obtained from multi-location field experiments to derive models for any crop variety. This information can then be applied consecutively at a localized grid within a spatial framework to produce plant phenology predictions at the landscape level. This software runs on the ‘Windows’ platform and supports the development of process-oriented and temperature-driven plant phenology models by intuitively and interactively leading the user through a step-by-step progression to the production of spatial maps for any region of interest in sub-Saharan Africa. Maize (Zea mays L.) was used to demonstrate the robustness, versatility, and high computing efficiency of the resulting modeling outputs of the PPMaP. The framework was implemented in R, providing a flexible and easy-to-use GUI interface. Since this allows for appropriate scaling to the larger spatial domain, the software can effectively be used to determine the spatially explicit length of growing period (LGP) of any variety.

Analysis of vegetation dynamics using remote sensing and GIS: a case study of Madhya Pradesh, India

This paper is aimed to study the correct growth cycle of two staple foods (i.e., wheat and rice), of Madhya Pradesh state of India through an approach of vegetation dynamics which focuses on analysing various parameters, such as rainfall, soil temperature, soil moisture, and crop growth trend over time using temporal data based on regression analysis to generate crop maps. This paper analyses various stages of growth for two major staple crops in Madhya Pradesh (M.P.) region. Wheat and rice farming in M.P. is facing issues related to climate change, drought, fertilizer application, low selling prices, and other. Nature of growth and effect of various parameters like rainfall, temperature, was studied and a proper graph analysis was done for a better understanding of plant growth, and generation of various phenological maps. In multiple regression analysis, R2 value of 0.24 and 0.45 was found highest for HH and VV polarization for wheat and rice, respectively. This means that multi-linear regression for HH and VV explains 24% and 45% for wheat and rice, respectively. Analysis and simulation of crop phenology was done by using the trend analysis approach with the help of the various graphs values that explains the different stages of crop growth and the effect of changing temperature and rainfall over the growth of the crops, Use of ArcGIS 10.5 software simplifies the study process and helped in developing a better understanding.

Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning

ABSTRACT Regular monitoring and mapping of rice (Oryza Sativa) growth phases are essential for industry stakeholders to ensure food production is on track and to assess the impact of climate change on rice production. In Indonesia, high-cost field surveys have been widely used to monitor the rice growth phases. Alternatively, this research proposes a methodology to retrieve multi-temporal rice phenology (vegetative, reproductive, and ripening) and bare land mapping using medium resolution remote sensing imagery obtained from Landsat-8 Operational Land Imager (OLI) combined with machine learning techniques. In this study, we have used extensive ground validation information collected from 2014 to 2016 for training the models. This ground validation information was obtained from pre-installed webcams across Indonesia. Five different machine learning algorithms were used including random forest (RF), support vector machine (SVM) with three kernel functions (linear, polynomial, and radial) and artificial neural networks (ANN) to classify rice growth phases and bare land. This paper also evaluates the temporal evolution of rice phenology and bare land to check the prediction model consistency between two consecutive dates in 3 years. The results show that the nonlinear SVM algorithm gives the best model accuracy (70.5% with Kappa: 0.66) based on the test dataset and the lowest temporal changes (<11%). Spatial-temporal assessment of rice phenology and bare land from Landsat-8 indicated that the models were reliable and robust over different seasons and years. The distribution of rice phenology maps will enable Indonesian management authorities to supply fertilizer, allocate water resources, harvesting, and marketing facilities more efficiently.

Agriculture Phenology Monitoring Using NDVI Time Series Based on Remote Sensing Satellites: A Case Study of Guangdong, China

This article presents the use of the Normalized Differences Vegetation Index (NDVI) time series based change detection method for agriculture phenology monitoring. NDVI make use of the multi-spectral remote sensing data band combinations techniques to find out landscape such as agriculture, vegetation, land use/cover, water bodies and forest. Geographic Information System (GIS) technology is becoming an essential tool to combing multiple maps and information from different sources as satellite, field and socio-economic data. Landsat 8 and Sentinel-2 satellite data were used to generate NDVI time series from Sep. 2017 to Nov. 2018. This research work was the procedure by pre-processing, signal filtering and interpolation of monthly NDVI time series that represent a complete crop phonological cycle. NDVI method is applied according to its specialty range from –1 to +1. We divided whole agriculture area into five part according to NDVI Values such as no agriculture, low agriculture, medium agriculture, high agriculture and very high agriculture area. The simulation results show that the NDVI is highly useful in detecting the surface feature of the area, which is extremely beneficial for sustainable development of agriculture and decision making. The methodology of reform NDVI time series had been providing feasible to improve crop phenology mapping.

Application of Landsat 8 Satellite Image – NDVI Time Series for Crop Phenology Mapping: Case Study Balkh and Jawzjan Regions of Afghanistan

In this article, it was targeted to reveal the variations of NDVI which may represent the phenological stages of agricultural crops derived from Landsat 8 imagery from the start to end of growing seasons which eventually influence the final yields. An effective method was developed to map seasonal phenological variations of crops over large geographic regions using 16-day Landsat 30 m resolution NDVI time series data obtained from USGS. The Google Earth Engine (GEE) platform was used for processing the Landsat 8 data. The areas with cloud cover and cloud shadows were masked out, filled by no data and smoothing double logistic filter was fitted on the time series of the reflectance values. Phenological metrics extracted from the NDVI time series were obtained by the TIMESAT software. Seasonal data were extracted for growing seasons of the years of 2015 and 2016. The phenology maps were created for study area.

Distribution of threshold values of harmfulness of larvae of lamellar phytophages in time

Goal. Clarification of the threshold of harmful larvae for 2-year-old pine seedlings at different stages of the growing season.&#x0D; Methods. Phenological observation of the complex of plates of the phylogenetic phytophages: eastern and western, marble, red and shaggy worms. Methods of compilation of phenological maps and graphic modeling of harmfulness thresholds of phytophages.&#x0D; Results It has been established that the period of harmfulness for larvae of the plates of the breeding phytophages, depending on their phenology, during the vegetation is expediently divided into three stages: A (spring), B (mostly summer), C (autumn), which correspond to different models of thresholds of harmfulness. In particular, the model for the autumn period takes into account the increased likelihood of plant death due to the suppression of larvae of the crust in the spring and summer period and the effectiveness of the measures implemented to regulate their numbers. The system for regulating the number of phytophages should be aimed at maximal protection of the pine culture in period A — prevention of damaging the root system of larvae (chemical insecticide) and preventive saturation of the ecosystem with biological agents in such a way that their maximum efficiency is at period B.&#x0D; Conclusions. For the spring, mainly summer and autumn periods, it is expedient to use different models of hazard thresholds: for the spring and summer x = (0.96y + 0.63) / 3, and for the autumn x = ((0.96y + 0.63) / 3) × 0.577, where — x — ratio of the biomass of the root system to the biomass of the larvae; y — biomass of the root system. The hazard threshold model for the autumn period takes into account the increase in the likelihood of plant death due to the weakening of the crustal larvae in previous periods (spring, summer) and the effectiveness of the measures taken to regulate the number of these phytophages.

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products.

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5’s spatial resolution and at MODIS’s temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an R2 of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an R2 of the SOS ranging from 0.68 to 0.86 and with an R2 of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture.

Grapevine phenology and climate change in Georgia.

While the climate of Western Europe has been deeply affected by the abrupt climate change that took place in the late '1980s of the twentieth century, a similar signal is detected only few years later, in 1994, in Georgia. Grapevine phenology is deeply influenced by climate and this paper aimed to analyze how phenological timing changed before and after the climatic change of 1994. Availability of thermal resources in the two climatic phases for the five altitudinal belts in the 0-1250-m range was analyzed. A phenological dataset gathered in two experimental sites during the period 2012-2014, and a suitable thermal dataset was used to calibrate a phenological model based on the normal approach and able to describe BBCH phenological stages 61 (beginning of flowering), 71 (fruit set), and 81 (veraison). Calibration was performed for four relevant Georgian varieties (Mtsvane Kakhuri, Rkatsiteli, Ojaleshi, and Saperavi). The model validation was performed on an independent 3-year dataset gathered in Gorizia (Italy). Furthermore, in the case of variety Rkatsiteli, the model was applied to the 1974-2013 thermal time series in order to obtain phenological maps of the Georgian territory. Results show that after the climate change of 1994, Rkatsiteli showed an advance, more relevant at higher altitudes where the whole increase of thermal resource was effectively translated in phenological advance. For instance the average advance of veraison was 5.9 days for 250-500 m asl belt and 18.1 days for 750-1000 m asl). On the other hand, at lower altitudes, phenological advance was depleted by superoptimal temperatures. As a final result, some suggestions for the adaptation of viticultural practices to the current climatic phase are provided.