Conference Agenda

Accurate and recursive maps of crops at the field scale is of great interest for the farmers to optimize the agronomical practices by minimizing the intra-field yield variability. Sentinel 2 and Venμs free available multispectral satellite imagery, with a spectral configuration optimized for vegetation and a revisit time less than 5 days, opens up new perspectives in the framework of precision agriculture. These satellite data can lead to the development of higher level products both at the farm and field scale such as yield estimation and prediction maps, crop nitrogen (N) balance assessment, weed patch detection and bare soil properties estimation (e.g. soil texture and organic matter).

The objective of the study, which was conducted in the framework of the Topic1 of the Dragon4 #32275 program, is to carry out a systematic work to explore the optimal configurations and possible alternative set-ups of algorithms allowing to exploit the full potential of S-2 and Venμs sensors in terms of their spectral and spatial resolutions.

To this aim, the Maccarese farm located in Central Italy, which is the second largest Italian private farm with about 3500 ha of agricultural fields (typically 10 ha or larger) was selected as study area. This because the farmers were equipped of yield maps machinery and in 2018 Venμs new generation satellite started programmed acquisitions on this study area (ADEPAMAC project).

Freely available toolboxes such as BV-Net (Baret et al., 2007), ARTMO or SNAP (ESA) were used for semi-automated retrieval of biophysical parameters through radiative transfer model inversion, i.e. by optimizing LUT-based inversions.

The biophysical canopy variables, expressing the crop ability to intercept and convert solar radiation also reflecting the vigour of the plant canopy, were retrieved using both S2 and Venms sensors when near acquisitions occurred. In particular, LAI and Chl were retrieved and compared in terms of accuracy with respect to the ground truths (LAI measured with LAI2000 and Chlorophyll with Dualex) acquired during 4 different field campaigns in the winter wheat growing season in the study area.

Moreover, these analyses were coupled with the analysis of different spectral indexes/procedures in order to assess the capability of the red-edge bands in retrieving leaf/plant pigments (i.e. chlorophyll, carotenoids) and the Leaf Area Index (LAI).

The results show that both S2 and Venμs satellite sensors are able to retrieve with a good accuracy crop biophysical variables such as LAI and chlorophyll, both by using retrieving algorithms from RT codes or spectral indexes/procedures. Moreover, the few available experimental results suggest that the use of multi-temporal remote sensing data can significantly improve estimation of canopy biophysical variables.

Introduction

Yellow rust (Puccinia striiformis) is one of the most severe epidemic diseases for winter wheat in China, annual affected area of yellow rust on winter wheat is greater than 6.7 million ha during 2000-2016. Pathologically, the development of yellow rust comprises five spore stages, including uredospores, appressorium, basidiospores, spermatia, and aeciospores, and the foliar biophysical variations are critical indicators for tracking the progressive host-pathogen interactions at different stage. The interaction of electromagnetic radiation with plant leaves is governed by their biophysical constituents, and response to infestations. However, current researches for agricultural pests and diseases monitoring generally are premised on a given infestation stage. Hyperspectral- and multispectral- continuum observations permit the acquiring of the host-pathogen processes within entire epidemic stages of rust on wheat. Tracking the progressive infestation is complicated by the following aspects: 1) the pre-existing VIs are not disease-specific, 2) these VIs nonlinearly varying as the increase of pathogen attack hard to express progressive spectral variations caused by the infestation process, 3) spatial and spectral redundancy have to be taken into account. The continuous wavelet transformation (CWT) have been proven to be a promising tool to capture subtle spectral absorption characteristics in detection of foliar constituents. The CWT-derived wavelet features are capable of decomposing raw spectral data into different amplitudes and scales (frequencies) in order to facilitate the recognition of subtle variation (or signals) and held the potential on retrieving foliar constituents.

Objective

The contributions of this paper are: 1) to identify a wavelet-based rust sensitive feature set (WRSFs) for characterizing the spectral changes caused by rust infestation at different stages, 2) to provide insight of the proposed WRSFs into specific leaf biophysical variations in the rust development progress, 3) to evaluate the performance of the proposed WRSFs as input feature space for tracking rust progress and retrieving rust severities on hyperspectral and multispectral images, such as sentinel-2. These continuous goals depend on a multi-temporal hyperspectral observation which covered entire circle of rust infestation.

Study Area

A series of in-situ observations were conducted at the Scientific Research and Experimental Station of Chinese Academy of Agricultural Science (39°30’40’’N, 116°36’20’’E) in Langfang, Hebei province, China, from jointing season (20^th April) to milk-ripe season (25^th May) of winter wheat in the 2017. We selected a cultivar, ‘Mingxian 169’, due to their susceptibility to yellow rust infestation, which were inoculated with yellow rust by spore inoculation in 13^th April. The concentration levels of 9 mg 100^-1 ml^-1 spores solution was implemented to naturally generate infestation levels (all treatments applied 200 kg ha^-1 nitrogen and 450 m³ ha^-1 water). Each treatment and repeat occupied 220m² of field campaigns. The makeup of topsoil nutrients (0 ~ 30 cm deep) in the experiment sites were as follows: soil organic matter 1.41~1.47%, nitrogen 0.07~0.11%, available phosphorus content 20.5~55.8 mg kg^–1, and rapidly available potassium 116.6~128.1 mg kg^–1.

Methodology

A wavelet-based technique for extracting the shape-based reflectance spectral feature was proposed based on the implementation of continuous wavelet transform (CWT), which provides a powerful method for detecting and analyzing weak signals at various scales and resolutions, and for analyzing multidimensional hyperspectral signals across a continuum of scales

A total of 9 hyperspectral VIs that have been reported as the rust-related proxies in relevant researches were selected and compared with the extracted WRSFs for disease detection. These adopted VIs have proved to (1) sensitive to crop growth: modified simple ratio (MSR); (2) pigment variation: structural independent pigment index (SIPI), normalized pigment chlorophyll index (NPCI), anthocyanin reflectance index (ARI), and modified chlorophyll absorption reflectance index (MCARI); (3) water and nitrogen content: Ratio Vegetation Structure Index (RVSI), (4) photosynthetic activity: photosynthetic radiation index (PRI), physiological reflectance index (PHRI); and (5) crop disease: yellow rust-index (YRI), aphid index (AI), and powdery mildew-index (PMI),

In the past, various supervised classification frames have been developed to detect plant stresses from remotely sensed observation, such as Artificial Neural Network (ANN), Decision Trees (DT), and Support Vector Machines (SVM). In this section, linear discrimination analysis (LDA) model and SVM model were used as the example frames for testing and comparison of the performance of WRSFs and VIs on detecting the progressive rust development under the linear and non-linear conditions, respectively.

Conclusion

This study proposed a new shape-based WRSFs from the wavelet transformed reflectance spectra of winter wheat leaves inoculated with yellow rust. The identified wavelet features in WRSFs is capable of capturing and tracking rust related biophysical indices (CHL, ANTH, NBI, and PDM) in progressive host-pathogen interaction. The performance of WRSFs as input feature space for DR estimation and lesions detection of rust was evaluated and compared with traditional VIs that sensitive to disease infestation. Our findings suggest that the WRSFs-PLSR model provide insight into specific host-pathogen interaction during rust development progress, which is more effective than VIs-PLSR model in DR estimation. For the rust lesion detection, the WRSFs-based feature space performed best for both LDA and SVM classification frame. Unlike the traditional techniques, the CWT based technique for WRSFs extraction is simple and straightforward to reflectance spectral signals. No predetermination of wavelength delimitation or other parameterization is needed. The practical WRSFs has great robustness for better understanding the pathological progress in tracking the rust development with hyperspectral data from various sensors. This method may be even applicable to others plan-pathogen systems.

Wheat powdery mildew is one of the serious crop diseases which affect the food safety of China. Integrating multi-source information (Earth Observation-EO, meteorological, etc.) to support decision making in the sustainable management of wheat powdery mildew in agriculture is demanded. With the development of satellite and sensor, the amount of available remote sensing data has increased dramatically. However, the high cost of filed survey data in regional level causes the inconsistency between the number of filed survey samples and the amount of remote sensing data, thus affecting the accuracy of crop monitoring model. In this study, a framework of transfer learning, TrAdaBoost, was used to monitoring the distribution of wheat powdery mildew in study area using the auxiliary field data from another region. This study was carried out in western Guanzhong Plain, Shaanxi province and the auxiliary field survey samples were acquired from south-central part of Hebei province. The Landsat-8 OLI images were used to extract vegetation indices which could indicate the growth status of wheat and meteorological data including Climate Hazards Group InfraRed Precipitation with Station data and the MODIS/Terra Land Surface Temperature and Emissivity (LST/E) product were used to describe the environmental conditions of wheat from booting stage to grain filling stage. With these features, TrAdaBoost with weak learner of Support Vector Machines was used to develop the wheat powdery mildew monitoring model. To evaluate the effect of auxiliary data, a referenced model which only used the samples available in study area was developed using Support Vector Machine. The experimental results suggested that two models provided similar disease distribution patterns over the study area while TrAdaBoost had significant higher accuracy than Support Vector Machine when too few samples available in study area and it could give better or comparative performance with the increase of available samples. When all the samples became available, TrAdaBoost had a higher overall accuracy (80%) and kappa coefficient (0.66) than Support Vector Machine (overall accuracy was 75% and kappa coefficient was 0.59). All these results reveal that transfer learning could be used to monitor the occurrence of wheat powdery mildew.

Key words: wheat powdery mildew; transfer learning; TrAdaBoost; disease monitoring;

Biophysical variables such as Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) are of crucial importance for a range of agricultural, forestry and ecological applications. Many approaches have been developed to extract these variables from satellite images. Broadly, these methods can be categorized as Statistical (Parametric and Non-Parametric), based on Radiative Transfer Physical Models and Hybrid approach. Recent studies show that hybrid methods, which combine Radiative transfer modeling (RTM) with intelligent Machine Learning (ML) algorithms may overcome many of the disadvantages of the other methods, for example, by being fast, more robust and having higher generalization capabilities. Different ML methods have been used with data simulated from RTM to extract LAI and LCC, but only Neural Networks (NN) have been successful to reach to the operational use. Although, the retrieval of biophysical variables using NN has been widely applied, the algorithm has some drawbacks: 1) low accuracy at higher values of LAI due to the saturation effect in RTM simulation and NN inversion algorithm and 2) unpredictable results, if training and test data are deviating from each other, even slightly. In the recent years, a suite of kernel-based algorithms have been explored to estimate LAI and LCC from satellite images, and have been shown to be a valid alternative to NN. For example, Kernel Ridge Regression (KRR) algorithm is simple for training and it provides competitive accuracy as compared to NN. Gaussian Processes Regression (GPR), performs well in terms of computational costs and speed, it provides higher accuracies, and uncertainty intervals. However, if trained on large simulated datasets from RTM, these algorithms are computationally expensive and this limits their operational use. Active Learning (AL) techniques have been proposed to reduce the size of the input training data generated from RTM, as they only selects the most informative cases from a large dataset, based on either the uncertainty or diversity of the data points.

In this work a study is presented on the comparison of different hybrid approaches for the retrieval of biophysical variables from Sentinel-2 data, based on the training of kernel-based ML algorithms with simulations from RTM. As a benchmark, the results obtained from these methods are compared to those from the biophysical processor implemented into the ESA Sentinel Application Platform (SNAP) software, which relies on the training of NN with PROSAIL simulations. The same simulated training set, sampled and optimized using active learning techniques is tested with different kernel based machine learning algorithms for the retrieval of biophysical variables from Sentinel-2 images acquired over European and Chinese test sites. Ground data measurement campaigns, on the wheat crop, have been carried out in Maccarese (Italy) and Shunyi (Beijing, China) in correspondence with Sentinel-2 acquisitions, to verify the accuracy of the algorithms. The results of this comparison study allow to obtain useful information in terms of quantitative statistical assessment, as well as of the practicality, computational time and cost of emerging hybrid approaches.

The productivity of wheat, including grain yield and quality, directly determines its market price and related agriculture policies. Currently, most prediction models of wheat yield and grain protein content (GPC), one parameter of grain quality, by remote sensing are a little mechanism and difficult to expand at interannual and regional scales. The objective of this study is to use Hierarchical Linear Model (HLM) integrating hyperspectral data at anthesis and environmental data to achieve yield and GPC prediction at interannual scales. Eight experiments during seven growing seasons, during 2008/2009, 2010/2011, and 2012-2017, were carried out. Fifteen spectral indices from hyperspectral data correlated with GPC at anthesis were calculated, and environmental information including daily radiation, maximum and minimum temperature, and rainfall was mean counted one month before anthesis at each growing season. Results suggested that Standardized leaf area index determining index (sLAIDI) and spectral polygon vegetation index (SPVI) showed the best correlation with yield (r = 0.77) and GPC (r = 0.38), respectively. The estimation of yield and GPC based HLM model considering environmental variations showed higher accuracy (Yield: R² = 0.75 and RMSE = 0.96; GPC: R² = 0.58 and RMSE = 1.21%) than the simple linear models (Yield: R² = 0.60 and RMSE = 0.97; GPC: R² = 0.13 and RMSE = 1.73%). A high consistency between the predicted values and the measured values with HLM method was shown at different years. Overall, these results in this study have demonstrated the potential applicability of HLM model for yield and GPC prediction at various years.

This study estimated wheat aboveground biomass (AGB) based on GF-3 synthetic aperture radar (SAR) data. In the Gaocheng research area, We collected ground. Including: biomass data (aboveground fresh biomass, aboveground dry biomass, fresh ear biomass, dry ear biomass) and soil moisture data. The collected ground samples data and the corresponding SAR data were used to establish biomass estimated models, that were water cloud model and polarized water cloud model. Finally, the effects of different biomass types, ROI window sizes and location accuracy about the biomass estimation result were analyzed. The result shows that the water cloud model is the best wheat biomass estimation model. However, the polarized water cloud model can replace the water cloud model for wheat biomass estimation when there is no soil moisture data. The final results provide a reference for estimating wheat biomass based on GF-3 data.

Introduction

In this paper an approach towards the automatic olive tree extraction from satellite imagery is presented. Automatic olive trees detection at a large geographical scale and their health status evaluation are necessary in order to provide an inventory map that may help in a better planning of the management activities and for predicting the olive production. The olive planted areas can be determined more precisely and in a short time through high resolution satellite images at low cost.

In a previous paper the possibility to detect olive groves affected by xylella was demonstrated by considering several fields and observing the behavior of the annual variation of the NDVI. Therefore, unchanged and changed (eradicated) olive groves show a distinctive behavior due to the change in the presence of olive trees as consequence of the trees eradication requested to stop the spread of the disease. In the plot where the olive trees have been eradicated the NDVI standard deviation (STD) increases significantly due to the reduced importance of the evergreen olive trees in determining the behavior of NDVI with respect to the background characterized by the presence of grass or shrub. However, this analysis was carried out on several plots selected taking into account the sites where the presence of the disease was evaluated with different results. In some cases it was not necessary to remove the plants, in other cases the olive plants were eradicated. The delineation of the plots was carried out manually by visual inspection of the image. In fact, the use of polygons of olive groves taken from the 2012 Corine Land Cover map (CLC 223) was not effective due to the variability of the surface cover types within such polygons. Thus, we decided to devote this paper to develop a technique suitable to identify olive groves with higher accuracy than CLC. Since olive groves are characterized by a significant variability in terms of tree density our classification introduce also a way to assess the olive trees surface cover fraction. A vegetation classification method based on plant biochemical composition and phenological development through the year is presented.

Objectives

The enhanced classification of olive groves has been achieved by utilizing EO data, developing new algorithms, and combining new and existing data from multi-source EO sensors to produce high spatial and temporal land surface information. Concerning this last point. The research activity follows two main approaches:

- Improving the classification of the agricultural areas devoted to olive trees, starting from what has been made available from the Corine Land Cover initiative;

- Developing an approach suitable to be automated for counting trees by using very high spatial resolution images in areas at high risk of infection.

The analysis starts from the following observations and hypothesis:

- the CLC polygons, corresponding to the class 223, outline areas containing fields characterized by different distribution density of olive trees;

- an accurate classification of the olive groves is required for applying further analysis aiming at the assessment of the plant tress status potentially due to diseases;

- the assessment of the olive groves status is based on the analysis of a temporal series of NDVI (Normalized Difference Vegetation Index) taking into account that olive trees exhibit values almost constant of NDVI during the year.

Data and study Area

The study area corresponds to the Province of Lecce, located in the Southern part of the Apulia Region. 77 Sentinel-2 (MSI) cloud free images of the area of interest covering the period February 2015 – July 2017, were found in the ESA database (tale T33XE). The research activity covers the Province of Lecce, that is the Italian area most affected by the Xylella fastidiosa disease causing a rapid decline in olive plantations, the so-called olive quick decline syndrome (OQDS, in Italian: complesso del disseccamento rapido dell'olivo). By the beginning of 2015 it had infected up to a million trees in the southern region of Apulia (Lecce Province).

Methodology

The tree density in the olive groves varies significantly and significantly influences our ability to detect them. The lower is the density, the greater the contribution of the underlying and surrounding vegetation to the detected spectral signature. The changes of leaf Carotenoid (Car) content and their proportion to Chlorophyll (Chl) are widely used for monitoring the physiological state of plants during development, senescence, acclimation and adaptation to different environments and stresses.

Then we developed an automatic olive tree detection technique based on tracking the NDVI and CRI2 (Carotenoid Reflectance Index 2) indexes development during the year. The chlorophyll/carotenoid index CRI2 was specifically implemented to help detecting sparsely populated olive orchards. Decisional rules selected on the basis of NDVI and CRI2 characteristics, retrieved over different test, sites were implemented to carry out the classification. The first objective was to clean the CLC 223 polygons removing the areas that shows no olive coverage at all; the second objectives consisted of adding new olive areas not previously classified to the CLC 223 polygons.

Then, the segmentation of the classified areas has been carried out by using NDVI maps of the area of interest and a mathematical morphology approach. The processing procedure has been implemented in Matlab. The procedure to estimate the fractional cover of olive trees within the previously classified areas foresees the following steps:

1. apply a segmentation function (gradient filter) to each CLC polygon;
2. Compute foreground markers (morphologic operators). These are connected blobs of pixels within each of the objects.
3. Compute max, min, average and standard deviation of NDVI in each polygon;
4. Estimate FCOV (Fraction of trees in each field).

Based on the above described procedure, the accurate olive tree distribution is retrieved for the area of interest. An automatic and continuous olive groves monitoring system is currently under development; this system will be capable of tracking the olive groves status, detecting and evaluating the presence and effects of stressing factors such as pests infestation, specific disease affecting olive plants, and general adverse environmental conditions due to climate changes.

Abstract: Yellow rust is one of the most destructive diseases for winter wheat and has led to a significant decrease in winter wheat quality and yield. Identifying and monitoring yellow rust is of great importance for guiding agricultural production over large areas. Compared with traditional crop disease discrimination methods, remote sensing technology has proven to be a useful tool for accomplishing such a task at large scale. This study explores the potential of the Sentinel-2 Multispectral Instrument (MSI), a newly launched satellite with refined spatial resolution and three red-edge bands, for discriminating between yellow rust infection severities (i.e., healthy, slight, and severe) in winter wheat. The corresponding simulative multispectral bands for the Sentinel-2 sensor were calculated by the sensor’s relative spectral response (RSR) function based on the in situ hyperspectral data acquired at the canopy level. Three Sentinel-2 spectral bands, including B4 (Red), B5 (Re1), and B7 (Re3), were found to be sensitive bands using the random forest (RF) method. A new multispectral index, the Red Edge Disease Stress Index (REDSI), which consists of these sensitive bands, was proposed to detect yellow rust infection at different severity levels. The overall identification accuracy for REDSI was 84.1% and the kappa coefficient was 0.76. Moreover, REDSI performed better than other commonly used disease spectral indexes for yellow rust discrimination at the canopy scale. The optimal threshold method was adopted for mapping yellow rust infection at regional scales based on realistic Sentinel-2 multispectral image data to further assess REDSI’s ability for yellow rust detection. The overall accuracy was 85.2% and kappa coefficient was 0.67, which was found through validation against a set of field survey data. The combination of REDSI and the optimized thresholding method proved to be a powerful method for detecting YR infection in winter wheat at regional scales. This study suggests that the Sentinel-2 MSI has the potential for yellow rust discrimination, and the newly proposed REDSI has great robustness and generalized ability for yellow rust detection at canopy and regional scales. Furthermore, our results suggest that the above remote sensing technology can be used to provide scientific guidance for monitoring and precise management of crop diseases and pests.

Keywords: yellow rust; Sentinel-2 MSI; red edge disease stress index (REDSI); winter wheat; detection

Objective

The aims of this study were to: (1) select the most sensitive bands of multispectral data (Sentinel-2) for identifying healthy wheat and both slight and severe yellow rust infection in winter wheat; (2) propose a new red-edge multispectral vegetation index for discriminating yellow-rust-infected winter wheat from healthy wheat; and (3) map yellow rust infection using realistic Sentinel-2 satellite imagery at regional scales.

Data and study Area

A series of in-situ canopy hyperspectral observations were conducted at the Scientific Research and Experimental Station of Chinese Academy of Agricultural Science (39°30’40’’N, 116°36’20’’E) in Langfang, Hebei province, China, at grain filling stage on 15, 18, and 25 May 2017. The winter wheat cultivar known as ‘Mingxian 169’ was selected, the yellow rust pathogens infected the winter wheat through an inoculation process (spore solution concentration of 9 mg 100⁻¹ mL⁻¹) according to the National Plant Protection Standard (NPPS) on 13 April 2017.

Field surveys of wheat yellow rust infection were conducted in Chuzhou and Hefei, Anhui Province, China (32°6.36′–32°38.02′ N, 117°6.09′–117°49.10′ E) on 9 May 2017 at grain filling stage, where winter wheat is considered to be one of the area’s major crops. Two simultaneous Sentinel-2 multispectral images were acquired on 12 May 2017, from https://scihub.copernicus.eu/, and the full coverage image was mosaicked by two images acquired simultaneously.

Methodology

We integrated the field canopy hyperspectral data based on the sensor’s RSR function to simulate the multispectral reflectance of Sentinel-2 to assess its potential for winter wheat yellow rust monitoring and detection. B4 (red), B7 (Re3), and B5 (Re1) were the most sensitive bands for identifying winter wheat infected with yellow rust through random forest model. Consisting of these three sensitive bands, Red Edge Disease Stress Index (REDSI) was proposed.

REDSI and nine SVIs (NDVI, VARI_green, EVI, RGR, NDVIre1, NREDI1, NREDI2, NREDI3, and PSRI) were selected for testing and comparison of their performances on detecting the wheat yellow rust at the canopy and the regional scales, respectively. The overall accuracy (OA) and kappa coefficient were used to evaluate the classification and discrimination performance of FLDA.

Conclusion

In this study, we developed a new index, REDSI (consisting of Red, Re1, and Re3 bands), for detecting and monitoring yellow rust infection of winter wheat at the canopy and regional scale. Compared with other common spectral vegetation indexes, REDSI has excellent performance in detecting and monitoring yellow rust in winter wheat at the canopy and regional scale, with the overall accuracy of 84.1% and 85.2%, respectively. Furthermore, the index had to be continually validated with other diseases and other cultivars to guide agriculture precision management.

Introduction

Powdery mildew (Blumeria graminis) is one of the most destructive foliar diseases of winter wheat and occurs in areas with cool or maritime climates. The infection of this disease results in a reduction of yield and quality of wheat. According to the statistics of National Agricultural Technology Extension and Service Center (NATESC) of China, the average outbreak area of powdery mildew was recorded to be as high as 10 million ha in the last 17 years. Powdery mildew can infect winter wheat in the whole growth period. Generally, powdery mildew hypha recovers growth in the first decade of February, the beginning development period of the disease is in March and the disease occurs generally in April and greatly in May. However, the current studies on crop diseases were mostly based on one single growth phase image in late stage of disease development, did not consider the temporal change characteristics of diseased crops. Otherwise, remote sensing-based time series were successfully used for crop phenology detection and, crop classification, crop area estimation, etc.

Objective

the objectives of this study were: (1) to analyze the relationship between normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time series and winter wheat powdery mildew, (2) to monitor the occurrence severity of winter wheat powdery mildew through NDVI and EVI time series, (3) to map the spatial distribution of winter wheat powdery mildew occurrence severity, and (4) to assess the performance of the proposed disease monitor models.

Data and study Area

A total of 42 field survey points were collected in 10th May 2014 in western Guanzhong plain in Shaanxi Province, China, which area is a commonly occurred area of winter wheat powdery mildew. In order to field investigation of diseases occurrence match with the spatial resolution of the remotely sensed image, five 1m×1m representative ranges were relatively uniformly selected in a 30m×30m spatial extent. The central latitude and longitude of each point were recorded by sub-meter differential GPS. The specific survey included wheat growth condition, height and occurrence severity. The occurrence severity was reclassified there levels which include normal, slight and severe to reduce the difficulty of monitoring.

Methodology

A monitoring model for monitoring of powdery mildew occurrence severity based on the NDVI and EVI time series was established. The model almost contained all the critical disease infected information in whole growth period of winter wheat.

Totally, 18 remote sensing images were acquired, for the period from 16^th November 2013 to 9^th April 2014. In order to reduce the impact of cloud cover, three sensors’ data (include WFV sensor data of Gaofen-1 satellite, CCD sensor data of the environment and disaster reduction small satellites and the OLI sensor data of Landsat-8) were chosen. The NDVI and EVI which sensitive to green vegetation and is often used to calculate the quantity and viability of surface vegetation and adverse effects of environmental factors such as atmospheric conditions and soil background were selected to develop time series for disease monitoring, and compared the performance of models with NDVI and EVI time series, respectively.

A significant level of noise was present in the temporal signatures due to clouds, aerosols and snow, etc. Hence, in order to ensure the quality, the NDVI and EVI time series need to be smoothed by discrete wavelet transformation (DWT) before being used, which is an orthogonal function which can be applied to finite group of data and has been widely used in the fields of signal processing and image compression. Support vector machines (SVM) exhibits many unique advantages in solving small sample, non-linear and high-dimensional pattern recognition problems and largely overcomes the problems of dimensionality disaster and over-study. And SVM has been widely used in text recognition, face recognition, gene classification, time series prediction, risk assessment, image classification, etc. In this study, SVM was used to construct monitor model with NDVI and EVI time series, and a leave-one-out cross validation method was used to testing and evaluate the performance of NDVI and EVI time series on monitoring the disease occurrence severity due to the small total sample size.

Conclusion

This study developed a monitoring model of disease occurrence severity based on NDVI and EVI time series features. The difference between NDVI and EVI time series curves of winter wheat infected with different disease severities was obvious. The NDVI and EVI time series were both able to discriminate the disease severities. Both the accuracies of the NDVI and EVI time series models suggested that the NDVI and EVI time series preformed good in quantifying disease severity. Compared the NDVI time series models, the EVI time series achieved a higher monitor accuracy for powdery mildew occurrence severity on winter wheat. Furthermore, the monitoring models with NDVI and EVI time series de-noised by DWT outperformed the models with original NDVI and EVI time series. These results reveal that the disease severity monitoring models based on satellite image time series can be a reference for field disease management.

Crop counting is of great importance for harvest yields estimating, to detect crop stress emergencies, to locate plants and tree species, among others. At the same time, in case of commercial orchards the tree identification is essential for the subsidies given by the European Union. Lately, and thanks the easy accessibility to remote sensing datasets provided by an increasing number of Earth Observation satellites (as Landsat, Sentinel, Planet, or GeoFen constellations) and Unmanned Aerial Vehicles (UAVs), the possibility to include these datasets to retrieve value added information, reducing time and simultaneously covering wide areas is a reality.

In particular forest management applications, and those related to tree identification, give an essential input in order to increase the efficiency of orchards management and the potential detection of pest outbreaks scenarios.

During last years, several projects have been carried out focused on forest monitoring, and some of them in particular related to analyze and control the diffusion of pests, as the case of Xyllela Fastidiosa (Xf), which is one of the most dangerous plant bacteria worldwide, causing a variety of diseases, with huge economic impact for forestry and the environment. In particular in Italy, more than 30,000 trees are under monitoring, and almost 2% of these resulted positive for Xf. And in this context the development of methodologies for tree detection, and a rapid anomaly detection is one of the main challenges, whose outcomes will support further surveys and inspections.

Among the most important international initiatives recently carried out, we could mention the Xf-Actors and POnTE projects, funded by the European Union within the Horizon 2020 EU Framework. Also in this context, the AMEOS project, sponsored by an ESA-Dragon agreement, aims to bring together cutting edge research to provide pest and disease monitoring and forecast information, integrating multi-source information (Earth Observation-EO, meteorological, entomological and plant pathological, etc.) to support decision making in the sustainable management of insect pests and diseases in agriculture. In particular the project team also explores the possibility of using remote sensing images to assess the evolution of diseases on permanent crops (olive groves, vineyards).

For the tests carried out in the present work the area of interest is located in Puglia region, Italy, widely infected with Xf. The region has been divided by the regional authorities in Infected, Containment and Bearing areas. The surveillance of big areas requires the assistance of a remote sensing approach, that has proved its effectiveness in detecting infected trees. For each of these areas, in this work a comprehensive imagery dataset taking into account different spatial and spectral resolutions have been processed. The algorithm has been tested with a set of satellite images (Landsat8-OLI, Sentinel-2, QuickBird, Planet, Gaofen-1), and imagery acquired with the MicaSense-RedEdge sensor on board a UAV SkyRobotics-VTOL-SF6 platform.

Crop counting complexity depends on the quality and resolution of the image, the spacing between trees and the algorithm implemented. In this case, FX (Feature Extraction) algorithm performance has been tested in a wide range of scenarios ingesting the procedures with images of spatial resolutions from 30 m. to less than 5 cm, and spacing trees from 4m. to 10m. The algorithm can be summarized in the following main subtasks: Image calibration, Morphological Filtering, Binary thresholding, Rule based Segmentation, Regionalization, Crop Counting and Geodatabase ingestion.

While with Landsat-8 and Sentinel-2 imagery feature extraction (FX) algorithms are able to detect, extract and count the number of trees in most of aged well point-distributed orchards, FX algorithms applied on QuickBird and UAV imagery are capable to achieve the main goal with a high level of effectiveness, and in case of UAV imagery even in recently planted fields, where the dimension of the objects is within the centimeter scale.

An orchard database automatically enriched with the geo-location of detected trees, will be a valuable resource to update existing orchards monitoring systems, essential to detect unexpected anomalies with the assistance of information extracted from other sources (i.e. on-field sensors, meteorological station, or plant-water-transport sensors.).

This study estimated wheat Aboveground biomass (AGB) based on GF-3 synthetic aperture radar (SAR) data. In the Gaocheng research area, and 40 ground samples data were collected. Including: biomass data (aboveground fresh biomass, aboveground dry biomass, fresh ear biomass, dry ear biomass) and soil moisture data. The collected ground samples data and the corresponding SAR data were used to establish biomass estimated models in the Gaocheng Research Area, that were water cloud model and polarized water cloud model. Finally, the effects of different biomass types, ROI window sizes and location accuracy on the biomass estimation result were analyzed.
The result shows that the water cloud model is the best wheat biomass estimation model. However, the polarized water cloud model can replace the water cloud model for wheat biomass estimation under the situation of without soil moisture data. The final results provide a reference for estimating wheat biomass based on GF-3 satellites.