Conference Agenda

Organic farming is an agricultural system that is a priority in EU Mitova (2014), it shows clear environmental advantages in terms of environmental toxicity and the use of biological resources (Nemecek et al. 2006). Cereals occupy a particularly important place in organic farming. They are the main arable crops from which baby and dietary foods are produced and are very in demand on both our and international markets (Atanasova et al. 2014).

Perhaps the most common alternative cereal is the einkorn, which has already found its place in organic farms and among consumers (Konvalina 2011). The einkorn is an alternative for farmers who can incorporate another crop into their crop rotation, which guarantees them stable yield in conditions of sharp climate change. Due to its advantages, the mezza is not only an extremely valuable plant, as a healthy product of high biological value, but its cultivation does not require the use of plant protection products and mineral fertilizers (Eisele & Korke, 1997).

Ground data and drone Unmanned Aerial Vehicle (UAV) footage were collected during the agricultural year 2020-2021 on a biologically certified field with an einkorn located in central southern Bulgaria in the land of Byala Reka village, Parvomai Municipality, Plovdiv region. The boundaries of the field are determined with the help of the farmer who grows the crop in Google Earth Pro.

The field was divided into three separate parts depending on the condition and development of the harvest at the end of March 2021, when the crop was en masse entered the fraternal phase (BBCH 29). The field status was established using the EOS Crop Monitoring platform, in which a KMZ field boundary file was uploaded and the Vegetation Index (VI) NDVI was generated, on the basis of which the field was divided into three separate parts with high NDVI values, those with average NDVI values and low NDVI values. In the selected three different parts of the field, 3 GPS points were generated in the EOS Crop Monitoring platform. On the field were established three squares with sides 10 m × 10 m in the corners of which were permanent twelve permanent sites markers marked with 12 GPS points.

Ground data was collected in the spindle phases (BBCH 45) and milk maturity (BBCH 75), and biological yield was also taken into account when reaching technological maturity (BBCH 99). On the day of filming with UAV, all the lint plants and weed plants were counted in each of the 12 permanent test sites. All the limp plants and weeds were collected, measuring the fresh and dry biomass.

In May in phase spindle (BBCH 45) and June Milk Maturity (BBCH 75) UAV Wingtra (was used with multispectral camera Micasense and Sony RGB camera, The UAV capture data was processed with the Pix4D software. Vegetation indices EVI, MSAVI, NDVI, Chlorophyll Index Green and Chlorophyll Index RedEdge and other 29 VI were generated using the same software.

The results for the indicators characterizing the terrestrial mass of organic sowing were obtained and analyzed in three statistically proven differences in the values of the vegetation index NDVI generated with data from the Sentinel-2 satellite through phase fraternation (BBCH 25) respectively – 0.86; 0.74 and 0.63. From the ground data collected during the spindle phase, it was found that the average weight of the fresh green mass of einkorn gives way to the fresh weight of weeds. In the spindle phase, the most suitable VI for characterization of the sowing of einkorn grown in the conditions of organic farming are VARI and BIM. While VI such as GLI, HI, GRVI and GLAI could be used to assess the degree of entanglance in the crop.

During the milk maturity phase (BBCH 75) of seed development, the sowing of the weeds, most of which are in the initial phases of development. In the milk maturity phase, the vegetation indices SI, BIM, BIS and VVI have no proven differences in the three differences in sowing. They may describe sowing by indicator % dry matter in plants and number of plants and weeds in m2.

In conclusion, it can be said that in terms of indicators characterizing the state of sowing, it is appropriate to perform a filmed with UAV during the spindle phase, and not during the milk maturity phase, since during this phase much of the chlorophyll in plants has already been withdrawn and you cannot well characterize the state of the sowing.

In terms of yield and productivity elements, it is found that data obtained from the BDs during the milk maturity phase are more appropriate to characterize the elements of productivity and yield. Of all the BSI studied, only BSI was found to have a strong positive correlation with yield, and VARI was in a medium negative correlation with yield. During your milk maturity phase, which are in a strong correlation with yield are CVI, SCI and Chlorophyl index.

The crop growth condition in the spring of 2022 in Ukraine was attracted the attentions from agricultural community in the world. Thanks to the global coverage of the second generation FENGYUN polar orbiting satellite, the normalized difference of vegetation index NDVI retrieved from FY3C VIRR and FY3D MERSI were used to monitor the crop growth condition in Ukraine. The NDVI difference model between the current value and the historical mean in the past 5 year and the time series of NDVI at present and the historical mean were used to closely monitor the changes of the crop vegetation from March to June when was the winter crop growth season in 2022. Based on the NDVI difference model, the spatial condition of crop growth was mapped every dekad since the early March and the crop growth condition was categorized into five classes, such as worse, poor, normal, favorable, and good. The NDVI value averaged for the entire country of Ukraine and five states in the north and east neighboring with Russia were used for following the crop growth cycle. The results showed that the crop growth in Ukraine from March to early July 2022 was in a condition of "poor in the early season and better in the late season" that was better than the multi-year average. The generated NDVI time series curve presented a slight lag in April, a gradual increase from the end of April to the end of June, and a slight decrease in NDVI values in the beginning of July but still higher than the past 5 years average. It proofs that the growth of winter crops in Ukraine was not seriously affected.

Monitoring agricultural process of Jiansanjiang Farm based on multi-source remote sensing data

Abstract

Food security is an important foundation of national security, Jiansanjiang farm has a total cultivated land area of 776000 hectares, the average annual grain output accounts for about 1/11 of Heilongjiang Province, 1/100 of the country, An important grain production base in China, in order to fully and timely understand the progress of spring ploughing in Jiansanjiang, to ensure food security, to Jiansanjiang Branch under the jurisdiction of 15 farms as the research object, based on sentinel-2 satellite April 19, April 24, Images from April 29 and images from the Landsat8 satellite on April 28. Monitored the progress of spring preparations for paddy fields on fifteen farms. Based on the random forest algorithm and expert prior knowledge, the images of each period are divided into three categories: undisturbed, irrigated and flooded. According to the classification results, the growth rate of irrigated plots between April 19 and April 24 was faster, and the process of flooded was slower; As of April 29, the proportion of the flooded in Jiansanjiang Farm that has been the promotion of large-scale mechanization operations has increased rapidly, accounting for about 90% of the total paddy field area, and the spring preparation of paddy fields has basically ended

Key words:Food security;Random forest;Supervised classification;Jiansanjiang farm

Microwave retrieval of soil moisture is an underdetermined problem, as microwave emission from the landscape is affected by a variety of surface parameters. Increasing observation information is an effective means to make retrievals more robust. In this study, a multi-temporal and multi-angular (MTMA) approach is developed using SMOS (Soil Moisture and Ocean Salinity) satellite L-band data for retrieving vegetation optical depth (VOD_p, p indicates the polarization (H: horizontal, or V: vertical)), effective scattering albedo (ω_p^eff), soil surface roughness (Z_p^S), and soil moisture (SM_p). The main conclusions are as follows: this paper for the first time at the global scale produced a polarization-dependent SMOS VOD_p and ω_p^eff products, and their global spatial patterns follow global vegetation distributions; the retrieved surface roughness (Z_p^S) range from 0.04 to 0.22 cm, and its spatial distribution is partially different from the existing roughness products/auxiliary data from SMOS and SMAP (Soil Moisture Active Passive). The retrieved MTMA SM_p shows generally high correlations with in-situ measurements with overall correlation coefficients of more than 0.75, and the overall ubRMSE of MTMA-SM_H (0.050 m³/m³) and MTMA-SM_V (0.054 m³/m³) are also lower than that of SMOS-IC Version 2 (V2) (referred to as SMOS-IC) (0.058 m³/m³) and SMOS-L3 (SMOS Level 3) (0.066 m³/m³) products. Therefore, it is concluded that by incorporating multi-temporal SMOS data, the proposed method can be used to systematically retrieve soil moisture, VOD and additional surface parameters (effective scattering albedo and surface roughness were retrieved in addition in this study) with comparable or better performance of soil moisture than that of SMOS-IC and SMOS-L3.

The distributed hydrological models are important tools potentially used for policy planning and decision-making in terms of water-soil balance at the catchment level in different environmental conditions. However, the model calibration and validation present a crucial challenging task in poorly gauged basins, e.g. many river basins in Africa. Our study contributed to providing an operational framework to calibrate hydrological models by using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated in monthly steps using only twelve months of satellite-based actual evapotranspiration (ETa) geospatially distributed in the 37 sub-basins of the Lake Chad Basin in Africa. The identification of influential model parameters was done based on global sensitivity analysis by applying the Sequential Uncertainty Fitting Algorithm–version 2 (SUFI-2), incorporated in the SWAT-Calibration and Uncertainty Program (SWAT-CUP). This technique is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. Fifteen influential parameters were selected for calibration based on the sensitivity analysis. The optimized parameters set could achieve the best model performance judging by the high Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and determination coefficient (R²). Four sets of ET were tested for SWAT model calibration, i.e. ETMonitor, GLEAM, SSEBop and WaPOR. Overall, the calibration performance was very good, especially when matching the SWAT ET calculated with Hargreaves-equation based potential ET (ETp), to the ETMonitor ET and GLEAM ET, with performance metrics R²> 0.9, NSE>0.8 and KGE>0.75. The ETMonitor ET product was finally adopted for the SWAT model calibration in this study for further application, since it showed the best calibration results. The calibrated SWAT model were further validated by comparing its outputs with the total water storage change (TWSC) derived from GRACE and surface soil moisture from ESA – CCI product. The validation during 2010-2015 using total water storage derived from GRACE gave an acceptable performance, i.e. R2=0.56 and NSE=0.55. The evaluation against the ESA – CCI soil moisture showed NSE=0.85.

Net ecosystem productivity (NEP) is an important indicator to describe ecosystem function and the global carbon cycle. In this paper, the Carnegie Ames Stanford approach (CASA) model was optimized, and the NEP value of the European terrestrial ecosystem was estimated by coupling the soil respiration model. The results showed that the R² between the estimated value of NEP and the observed value increased from 0.252 to 0.403, and the RMSE decreased from 84.557 gC∙m^-2∙month^-1 to 64.466 gC∙m^-2∙month^-1 after optimizing the maximum light use efficiency () of the CASA model parameters using the vegetation classification data. After further optimizing the optimal temperature, R² increased to 0.428, and the RMSE decreased to 63.720 gC∙m^-2∙month^-1. These results have shown that it is an effective method to improve the NEP estimation accuracy by optimizing and the optimal temperature to improve the CASA model. On this basis, the spatial and temporal changes in NEP in various regions in Europe were analyzed using the optimization results. The results show that NEP in Europe is in the spatial distribution pattern of Western Europe > Southern Europe > Central Europe > Eastern Europe > Northern Europe. The monthly changes in NEP in all regions show a unimodal curve with summer as the peak, and the annual overall value is positive (i.e., it shows a carbon sink). The research results can deepen the understanding of the carbon source/sink distribution in Europe and provide a reference for carbon cycle research and carbon balance policy formulation in the region.

Land cover data is an important information in natural resource survey, land management, environmental monitoring, etc. It is of great significance to evaluate its accuracy and reveal its category confusion characteristics for many scientific fields. In this study, nine urban agglomerations were selected as the study area, and samples were collected by visual interpretation of Google Earth's high-resolution images. Then, the spatial distribution characteristics and classification accuracy of four land cover data products (GlobeLand30, FROM-GLC, GLC-FCS30 and CCI-LC) were analyzed quantitatively and qualitatively. The results show that all products have achieved good results in the classification of urban agglomeration features, among which the overall accuracy of FROM-GLC is the highest, reaching 82.34%, and CCI-LC is relatively low, with the overall accuracy of 78.09%. Further analysis shows that the classification accuracy of various data products for different land types is different. The classification accuracy of farmland, forest and other large and concentrated land types is higher, while the accuracy of shrubs, wetlands and other small and scattered land types is relatively low. The research results can help users choose data products according to their needs, and provide reference for data producers to improve product accuracy.

Net primary productivity of vegetation refers to the ability of green plants to fix and convert inorganic carbon into organic carbon by using sunlight for photosynthesis. It is not only an important variable to characterize plant activity and has practical significance in crop yield estimation, forest stock volume survey, grassland yield and ecosystem material circulation, but also the main factor to determine the carbon source and sink of the ecosystem and regulate the ecological process, and is mainly affected by environmental factors such as climate and land use.

Africa is the region with the least greenhouse gas emissions but the most affected. The report of United Nations Development Programme (UNDP) pointed out that CO₂ and other greenhouse gases emitted by developed and developing countries will have a serious impact on Africa, especially the sub-Saharan region in the future. Africa has the lowest greenhouse gas emissions of all continents (except Antarctica), but the worst effects of climate change on the stability of African ecosystems come first. Given that NPP is one of the key indicators to characterize the health of ecosystems, it is crucial to analyze the temporal and spatial variation trends of African vegetation NPP, which is of practical significance for ecological protection in Africa. Therefore, using the long time-series data of global NPP from 1981 to 2018, this paper will solve the following problems: (1) using trend analysis and coefficient of variation to analyze the change trend of African NPP; (2) using anomaly index analysis and Mann-Kendall test to study African NPP; (3) using wavelet analysis to explore the periodic variation and temporal patterns of African NPP.

The results suggest that: (1) The annual NPP has a significant change trend with a total of 48.56% of the pixels NPP changing. Among them, the NPP reduced in 32.44% of the pixels and significantly decreased in 29.37% of the land area, mainly concentrated in the Sahara Desert to the north of 15°N. The NPP increased in only a small part of the region, approximately 16.13%, and the NPP increased significantly in 12.08% of the areas, mainly in the north and south sides of the tropical rainforest area. (2) 46.27% of the pixels have low degree of NPP fluctuation, which are mainly concentrated in the Sahara Desert to the north of 15°N, northeastern East Africa and western South Africa. 39.49% of the regions with high degree of NPP fluctuation are mainly located in the north and south sides of the equatorial tropical rainforest. Among them, the Central African region with the equator as the center and extending about 5° from north to south has the highest NPP fluctuation. (3) The period from 1981 to 2018 can be divided into four stages. In 1981, NPP in Africa was generally higher than the average level, indicating that Africa's carbon sink capacity was strong at this stage. From 1982 to 1995, the NPP during different seasons in Africa was basically lower than the average level, indicating that Africa's carbon sequestration capacity was low during this period. In particular, NPP declined significantly during 1987-1992. From 1996 to 2018 (except for 2015 and 2016), the NPP of Africa in different seasons were basically higher than the average level, showing an overall upward trend, indicating that Africa’s carbon sink capacity was increasing. (4) Seasonal NPP increased over time, and there were mutation points in both annual and four-season NPPs in Africa, all occurring around 1995. (5) On the annual scale, NPP has a short period of 4-8 years, 15-21 years and 23-35 years, and a long period of 42-62 years, and exists on the time scale of 7 years, 20 years, 29 years and 55 years. Significant oscillations, of which the 55-year cycle has the strongest signal, is the first main cycle, and the second and third cycles are 29 and 20 years, respectively.

Xuzhou is one of the main producing areas of garlic in the country. Accurate and rapid acquisition of garlic spatial distribution information plays a very important role in estimating garlic production and daily prices. This paper takes Xuzhou as the research area, and based on the Google Earth Engine (GEE) cloud platform and Sentinel-2 data, the training samples are determined through visual interpretation and field inspection, and the NDVI index time series curve of typical crops in the study area is calculated. Construction and spectral index feature construction. After comparing the three classification algorithms of random forest classification, classification regression tree and support vector machine, the classification performance of different algorithms is evaluated, and the accuracy is verified. Among them, the random forest algorithm has obvious advantages over other algorithms. In the research of land object classification, the random forest algorithm has obvious advantages. Compared with the other two algorithms, the overall accuracy is 37.4‰ and 87.2‰ higher, and the kappa accuracy is 53.3‰ and 122.3‰ higher than the other two algorithms, respectively.

Key words: Remote Sensing Extraction; Google Earth Engine; Random Forest; Feature Extraction

Agricultural systems are the main consumers of freshwater resources at global scale, consuming 60 % to 90 % of the total available water. While the growing demand for agricultural products and the resulting intensification of their production will increase the dependency on available freshwater resources, this sector will become even more vulnerable because of the intensifying impacts of climate change. Detailed knowledge about soil moisture can help to mitigate these effects. Nevertheless, high resolution (space & time) surface soil moisture data for regional and local monitoring (down to precision farming level) are still challenging to obtain. By using current as well as future Synthetic Aperture Radar (SAR) satellite missions (e.g. Sentinel-1, ALOS-2, NISAR, ROSE-L), this knowledge gap can be filled. SAR observations are suitable for regional and local soil moisture estimations, but with a global extent. While the increasing resolution and total number of SAR recordings will contribute to an improvement of the estimation in general, the computational costs as well as the local memory capacity on the other hand become a limiting factor in processing the vast load of data. Here, on-demand cloud-based processing services are one way to overcome this challenge. This is especially interesting as most of the severely affected regions have limited access to computational resources.

Based on the alpha approximation approach of Balenzano et al. 2011, we developed an automated workflow for estimating soil moisture using temporal and spatial high-resolution Sentinel-1 timeseries. The workflow is established within the cloud processing platform Google Earth Engine (GEE), providing a fast and applicable way for on-demand computation of soil moisture for individual time periods and areas of interest around the globe. The algorithm was tested and validated over the Rur catchment (Germany); with an area of 2,354 km², it comprises a great diversity in agricultural cropping structure as well as topologies. A total of 711 individual Sentinel-1A and Sentinel-1B dual-polarized (VV + VH) scenes in Interferometric Wide-Swath Mode (IW) and Ground Range Detected High Resolution (GRDH) format are used for the analysis from January 2018 to December 2020. Using all available orbits (both ascending and descending), a temporal resolution of one to two days could be achieved with a spatial resolution of 200 m. The workflow includes multiple steps: despeckling, incidence angle normalization, vegetational detrending and low-pass filtering. The results were validated against eight Cosmic-Ray Neutron Stations (CRNS). In total, the method achieves an unbiased RMSE (uRMSE) of 5.84 % with an R² of 0.46. Looking at individual months, the highest correlation can be achieved in the months April and October with R² values range between 0.65 to 0.7, while the lowest correlation is observed in July and January, with R² values ranging between 0.15 o 0.2. Looking at individual landuse, the method achieves the best results for pastures, with an uRMSE of 0.42 and an R² value of 0.63.

Knowledge of Soil properties in agricultural fields allows more efficient use of resources, but this kind of information is rarely available. The qualitative information included in existing soil maps is often insufficient for site-specific management strategies, for these purposes, the quantitative estimation of soil properties reached through time-consuming and expensive on-site investigations is necessary.

Remote sensing data can be applied to acquire, in a cost-effective way, quantitative information about soil. The leaves and the soil are the main elements influencing the spectral reflectance of the image pixel. The position and intensity of the reflectance peaks are associated with absorption, high spectral resolution is essential to resolve the spectral features of interest with high accuracy. Existing instruments (eg. Sentinel 2, etc.) have not been designed to provide high spectral and spatial resolution capabilities in the spectral range of 400-2500nm to fulfil these needs. The assessment of soil variables from multispectral remote imagers is hindered by inadequate spectral resolution, multispectral satellite data are mainly used for qualitative assessments. The hyperspectral sensors, having the capability to observe the full spectrum between 400-2500nm with a better spectral resolution are more desirable for soil spectroscopy purposes.

This project represents the initial phase of retrieving topsoil properties with multiplatform and multi- hyper-spectral EO data, in particular hyperspectral data, using machine learning and multivariate regression.

The study areas are located in “Quzhou County”, a county of Hebei Province, China, administered by Handan Prefecture. Data collection on the ground has been carried out in synchronous with satellite observations. From about 50 fields, for a total of 95 , between 2019 and 2020, measurements of topsoil properties like Soil Organic Matter (SOM), pH, Effective Phosphorus, Available Potassium, and Total Nitrogen, were retrieved.

Satellite data from European Space Agency (ESA) Sentinel-2 and the Italian Space Agency (ASI) Hyperspectral Precursor and Application Mission (PRISMA) are being utilized.

PRISMA was launched on 22 March 2019, the instrument is a prism spectrometer, the design is based on a push broom sensor type observation concept providing hyperspectral imagery (around 250 bands) at a spatial resolution of 30 m. The spectral resolution is about 12 nm in a spectral range of 400-2500 nm (VNIR and SWIR regions). Panchromatic imagery is provided along with the Hyperspectral cube, at a spatial resolution of 5 m, and is co-registered with the latter to allow testing of image fusion techniques.

Sentinel-2 is an Earth observation mission part of the European program Copernicus. Two satellite (S2A and S2B) compose a constellation allowing a 5 days revisit frequency. Each satellite is carrying a single multi-spectral instrument (MSI), with 13 spectral channels, in the range 400-2500nm, that acquires optical imagery at a spatial resolution from 10 to 60 m.

The site's phenology was retrieved by studying the temporal series of vegetation indices such as NDVI and NBR2 obtained by the Sentinel-2 L2A images from 2019 to 2021. The crop phenology was applied to identify the days of the year when the bare soil is visible [1].

PRISMA images have been co-registered with Sentinel-2 data through an Automated and Robust Open-Source Image Co-Registration Software (AROSICS) with the aim to improve their georeferencing.

To improve the retrieval of soil properties the images have been preprocessed to produce fused data with high spatial and spectral resolutions [1]. Both the pan-sharpening, with the panchromatic PRISMA images, and the multispectral/hyperspectral fusion, with the Sentinel 2 reference image used for co-registration, have been attempted to increase the spatial resolution of the hyperspectral cube to 5 and 10 m respectively [2].

Bibliography

[1]

N. Yokoya, T. Yairi and I. Akira, “Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion,” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, February 2021.

[2]

A. ARIENZO, G. VIVONE and A. GARZELLI, "Full Resolution Quality Assessment of Pansharpening: Theoretical and Hands-on Approaches," IEEE Geoscience and Remote Sensing Magazine, May 2022.

[3]

N. Mzid, F. Castaldi, M. Tolomio, S. Pascucci, R. Casa and S. Pignatti, "Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data," Remote Sensing MDPI, 2022.

Recently, Remote Sensing (RS) information has been involved in numerous hydrological modelling applications as a tool for quick collection of geophysical data. This data should be handled with care, being aware of its characteristics, such as spatial and temporal resolutions and target area composition.
In this work, evapotranspiration estimates over heterogeneous crops are subjected to a scale analysis. These have been obtained from a distributed energy-water balance model (FEST-EWB) employing, among others, high-resolution remotely-sensed Land Surface Temperature (LST) and vegetation data. FEST-EWB is calibrated, pixel-wise, on measured LST, by minimizing, for each pixel within the domain of interest, the model error against the satellite-retrieved LST. The case study is a Sicilian vineyard, with some observations days in the summer of 2008. During these days, meteorological and energetical fluxes data were gathered through an eddy-covariance station, whereas airborne instruments collected LST and vegetation data are obtained at the high resolution of 1.7 metres.
After a preliminary calibration on LST data and validation on energy fluxes, the scale analysis is performed: model results are compared after both input and output aggregation. In reference to some common satellite products, four coarse resolutions were selected for the analysis: 10.2 m (in reference to Sentinel’s 10 m), 30.6 m (Landsat, 30 m), 244.8 m (MODIS visible, 250 m) and 734.4 m (MODIS, 1000 m). Firstly, modelled surface temperature and evapotranspiration are upscaled to each scale by progressive averaging. Then, model inputs are aggregated to the same spatial resolutions and the model is calibrated anew, obtaining independent results directly at the target scale. The results of the two procedures are found to be similar, positively reporting the model flexibility in providing accurate products for heterogeneous areas even at lower spatial resolutions.