A project of the Framework Partnership Agreement on Copernicus User Uptake


Open Atlas is an open-source algorithm registry for earth observation. Our platform is designed for researchers interested in developing and sharing algorithms related to the water and food nexus. We focus on earth observation data from sources like the Copernicus satellite missions. By publishing algorithms on Open Atlas, researchers can accelerate the development of new tools and applications related to thewater and food nexus. Join our community and contribute to our growing library of algorithms.

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We collected 94,986 high-quality aerial images from 3,432 farmlands across the US, where each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers. As a pilot study of aerial agricultural semantic segmentation, we perform comprehensive experiments using popular semantic segmentation models; we also propose an effective model designed for aerial agricultural pattern recognition.

The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). The research time was two consecutive years (2018 and 2019), which were
used to verify the robustness of the method. Sentinel-1 images of the
crop growth period (May to September) in each study area were composited
with three time intervals (10 d, 15 d and 30 d).

This work assesses the potential of Sentinel-2A images in precision agriculture for Barley production in a case study. Two workflows are proposed: 1) images were acquired with a relatively simple methodology to follow the crop development; 2) two images around harvest time were downloaded and processed using a more complex and accurate methodology to calculate four vegetation indices (NDVI, WDRVI, GRVI and GNDVI) to be correlated to yield with linear regression models.

Utilise time-series Sentinel-1 data of canola and wheat fields over a Canadian test site to show the sensitivity of θxP to the development of crop morphology at different phenological stages. During the initial growth stages, θxP values are low due to the low vegetation density. In contrast, at advanced phenological stages, we observe decreased values of θxP due to the appearance of complex canopy structure.

The Tracking Radar Vegetation Index (TRVI) is a script used in agriculture development that combines data from radar images and vegetation indices to monitor and assess vegetation growth and health over time.

The TRVI script works by analyzing radar data from satellites, which can penetrate through clouds and vegetation to measure the roughness and moisture content of the Earth’s surface. The script then uses this data to calculate the vegetation index, which is a measurement of the amount and health of vegetation in a given area.

By combining the radar data with the vegetation index, the TRVI script is able to track vegetation growth and health over time. This information can be used by farmers and other agricultural stakeholders to monitor crop yields, identify areas of concern, and make decisions about irrigation, fertilization, and other management practices.

The TRVI script can be applied to a variety of crops and vegetation types, including both annual and perennial crops, forests, and grasslands. It has the advantage of being able to provide information even in cloudy or rainy conditions, when optical sensors such as cameras or satellites cannot provide reliable data.

Overall, the TRVI script is a powerful tool for monitoring and managing agricultural landscapes, providing farmers and other stakeholders with valuable information to make informed decisions about their land management practices.

Open source thresholding and segmentation algorithms were applied to extract aquaculture ponds from Sentinel-1 time series data based on object and shape metrics.

Nearly 10,000 km² of free high-resolution and matched low-resolution satellite imagery of unique locations which ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities. Each high-resolution image (1.5 m/pixel) comes with multiple temporally-matched low-resolution images from the freely accessible lower-resolution Sentinel-2 satellites (10 m/pixel).

Major challenges for satellite image analysis in the context of Rwanda include heavily clouded scenes and small plot sizes that are often intercropped. Sentinel-2 scenes corresponding to mid-season were analyzed, and spectral signatures of maize could be distinguished from those of other crops. Seasonal mean filtering was applied to Sentinel-1 scenes, and there was significant overlap in the spectral signatures across different types of vegetation. Random Forest models for classification of Sentinel scenes were developed using a training dataset that was constructed from high-resolution multispectral images acquired by unmanned aerial vehicles (UAVs) in several different locations in Rwanda and labeled as to the crop type by trained observers.

The exploitation of the unprecedented capacity of Sentinel-1 (S1) and Sentinel-2 (S2) data offers new opportunities for crop mapping. In the framework of the SenSAgri project, this work studies the synergy of very high-resolution Sentinel time series to produce accurate early seasonal binary cropland mask and crop type map products.

The main objective of this paper is to develop a synergic method of radar and optical data, as well as an on-site sampling scheme, to accurately estimate SMC in the basin. The reason is that in the winter wheat production area of Dawen River basin in China, there are few conventional SMC testing equipment.

A sequential complex Wishart-based algorithm which is applicable to the dual polarization intensity data archived on the GEE is used. Both the algorithm and a software interface to the GEE Python API for convenient data exploration and analysis are presented; the latter can be run from a platform independent Docker container and the source code is available on GitHub. Application examples are given involving the monitoring of anthropogenic activity (shipping, uranium mining, deforestation) and disaster assessment (flash floods). These highlight the advantages of the good temporal resolution resulting from cloud cover independence, short revisit times and near real time data availability.

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2).

Tidal heights and reflectance of 33 Sentinel-2 images were used to calculate a multitemporal composite of Sentinel-2 images, which was classified using the Random Forest classifier. UAV images classified with Object-based image analysis provided ground truth for training and validation. The effectiveness of water column correction and using the multitemporal composite was evaluated by comparing overall classification accuracy.

This study aims to explore the accuracy of the gray level of co-occurrence model (GCLM) textures of multitemporal Sentinel-1 data for aquaculture pond mapping in Brebes Regency, Central Java Province, Indonesia. In addition, single-date Sentinel-2 optical imagery was used to compare the results from Sentinel-1 data. The Sentinel-2 data has been identified using supervised classifications, e.g., maximum likelihood (ML), minimum distance (MD), random forest (RF), and K-nearest neighbor (KNN) algorithms, and the most accurate algorithm was selected to classify the Sentinel-1 data using GLCM textures.

The main goal of this study is to assess the performances and constraints of a SAR-based crop classification in an operational large-scale application. The Random Forest object-oriented classification model is built on Sentinel-1 time series from January to August 2020 only. It can identify crops in the Walloon Region (south part of Belgium) with high performance: 93.4% of well-classified area, representing 88.4% of the parcels.

a C-band SAR classification algorithm mapping agricultural crops dominated/non-dominated by volume scattering is described and assessed. The algorithm exploits cross-polarized SAR data and it is a part of the SMOSAR algorithm (“Soil MOisture retrieval from multi-temporal SAR data”) developed in view of the forthcoming Sentinel-1 data. The results indicate that the selected method is fairly robust versus changes of site location and in average it is expected to achieve an overall accuracy equal or better than 80%.

A multiscale temporal change detection on Sentinel-1 VH backscatter can be applied over bare or scarcely vegetated agricultural fields.

This study focused on exploring the utility of Sentinel-2 data in mapping of crop types and testing the two machine learning algorithms which are Random Forest and Support Vector Machine performance in classifying crop types in a heterogeneous agriculture landscape in Free state province, South Africa. Nine crop types were successfully classified.

This work presents a framework that combines supervised learning for crop type classification on satellite imagery time-series with semantic web and linked data technologies to assist in the implementation of rule sets by the European common agricultural policy (CAP). The framework collects georeferenced data that are available online and satellite images from the Sentinel-2 mission. The research analyzes image time-series that covers the entire cultivation period and link each parcel with a specific crop.

For the derivation of crop maps a method has been developed with which time series crop information can be predicted based on remote sensing data. The training of the crop classification model has been performed on the cropland data of the LUCAS Land Use / Cover Area Frame Survey of year 2015 and 2018 – revised by d’Andrimont et al. (2020) – merged with the Sentinel-1A and -1B satellite radar images based on d’Andrimont et al. (2021). The pixel based crop classification has been derived using a random forest algorithm on Google Earth Engine platform. The method can be applied for 2015 and all following years. By adding a map of field boundaries the pixel based prediction can be overwritten by the majority of the predicted crop.

This work investigates a Sentinel-2 based crop identification methodology for the monitoring of the Common Agricultural Policy’s (CAP) Cross Compliance (CC) and Greening obligations. In this regard, we implemented and evaluated a parcel-based supervised classification scheme to produce accurate crop type mapping in a smallholder agricultural zone in Navarra, Spain.

he SARSense campaign was conducted to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multispectral and thermal infrared measurements.

The study explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, researchers tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested.

This study used the recursive feature elimination (RFE) technique to select optimum SAR indices from the backscattering coefficients of the sampling date as well as the averaged backscattering coefficients of the neighbouring dates. The newly developed support vector regression (SVR) model provided more accurate SM estimation than that from the random forest regression (RFR) and multivariate linear regression (MLR) models.

This article describes the method of remote monitoring of agricultural land using Sentinel 2 satellite imagery. The NDVI index identifies problem areas of an agricultural field. The local filtering algorithm smoothes images for further processing. An algorithm for filtering images by threshold obtained experimentally is also described. An algorithm for detecting contours by the Rosenfeld method of healthy vegetation is described.

This study explores the potential of Sentinel-1 time series to map field heterogeneities as an additional information into optical vegetation index products into operational farming practices. The presentation explains the data processing strategies as well as their analysis and validation to calculate the crop parameters.

the construction of a near-real-time deliverable cropland mask product has been studied here. A set of 12 selected test sites are used to benchmark the proposed method with regard to the diversity of agro-ecological context, the various landscape patterns, the different agriculture practices and the actual satellite observation conditions. The classification results yield very promising accuracies achieving around 90 % at the end of the agricultural season.

Therefore, the goal of the current study was to make the Sentinel-1 data available efficiently, so that the experts in this field can focus on correlating and comparing it to reference data and measurements collected in the field. The function of the web service is illustrated with concrete application examples in the agricultural domain.

A globally applicable model for the estimation of NDVI values from Sentinel-1 C-band SAR backscatter data. First, the newly created dataset SEN12TP consisting of Sentinel-1 and -2 images is introduced. Its main features are the sophisticated global sampling strategy and that the images of the two sensors are time-paired. Using this dataset, a deep learning model is trained to regress SAR backscatter data to NDVI values. The benefit of auxiliary input information, e.g., digital elevation models, or land-cover maps is evaluated experimentally.

Earth reflected Global Navigation Satellite System (GNSS) missions provide as a main product the delay-Doppler map (DDM) of the power of the reflected GNSS signals. Some of these missions have collected a large amount of raw intermediate frequency (IF) data. Due to the unprocessed nature of these raw IF data provides an unique opportunity to explore the potential of GNSS Reflectometry (GNSS-R) technique for advanced geophysical applications such as inland water detection and surface altimetry.

A proposed deep learning architecture is trained from scratch to be tailored to the specific properties of the long time series of Sentinel 2 multispectral images.

Algorithm for determining crop harvesting dates based on time series of coherence and backscattering coefficient ( σ 0 ) derived from Sentinel-1 single look complex (SLC) synthetic-aperture radar (SAR) images is proposed. The algorithm allows the ability to monitor harvesting over large areas without having to install additional sensors on agricultural machinery.

Using Google Earth Engine (GEE) a new Sentinel-2 (S2) phenology end-to-end processing chain was developed. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS)

This work aims to predict the corn Nitrogen concentration (Nc) during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods.

This paper examines how temporal series Sentinel-2 satellite images can be used to detect the plastic rings from aquaculture fisheries in the Vassiliko area in the south coast of Cyprus. This detection methodology can be used to manage and monitor fisheries using Sentinel-2 images.

A simplified morphological analysis was performed on Sentinel data and Digital Terrain Model (DTM) by DTM tessellation to map water depth. Submerged areas were classified by proximity analysis to be able to distinguish between areas with soil already saturated versus areas where water was coming from the river. Sentinel-2 pre-flood images were used to map crops that were still to be harvested at the time of the flood.

The research presents a multi-sensor approach which utilizes Earth Observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as a buffer of 200km from the coastline. This research developed an object-based framework to detect and extract aquaculture at single pond level based on temporal features derived from high spatial resolution SAR and optical satellite acquired from the Sentinel-1 and Sentinel-2 satellites. A second step was performed through spatial and statistical data analyses of the Earth observation derived aquaculture dataset to investigate spatial distribution and to identify production hotspots in various administrative units at regional, national, and sub-national scale.

The researchers developed a map of coastal aquaculture ponds in China using Google Earth Engine (GEE) and the ArcGIS platform, Sentinel-1 SAR image data for 2020, the Sentinel-1 Dual-Polarized Water Index (SDWI), and water frequency obtained by identifying the special object features of aquaculture ponds and postprocessing interpretation

This study focuses on different machine learning algorithms for crop classification for the region of interest (ROI) in the study area of Kendarapara district, Odisha, for the year 2021 utilizing Sentinel-1 data. The study was performed using Google Earth Engine. The performance of four machine learning techniques Random Forest (RF), Classification and Regression Trees(CART), Gradient Boosting, and Support Vector Machine(SVM) algorithm, for three different crop type classifications, were evaluated. The results demonstrated that CART has the highest accuracy of 98.77%.

The main goal of the research was to develop a machine learning algorithm for the detection of water overgrowth with Phragmites australis based on Sentinel-2 data. The research was conducted based on field botanical and vegetation investigations in 2020–2021 in Soleniy and Chumyanniy firths. Collected field and remote sensing data were processed with the semi-automatic classification plugin for QGIS.

We present an earth observation based approach to detect aquaculture ponds in coastal areas with dense time series of high spatial resolution Sentinel-1 SAR data. An open source segmentation algorithm supported the extraction of aquaculture ponds based on backscatter intensity, size and shape features.

A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical–vertical) and VH (vertical–horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed.

The main objective of this study is to develop an operational approach for mapping irrigated agricultural plots using Sentinel-1 (S1) and Sentinel-2 (S2) data. The application is carried out on two agricultural sites in Europe with two different climatic contexts. Different classifiers are identified to allow the separation between irrigated and rainfed areas.

In this research there are 3 algorithms that will be used in determining the content of chlorophyll a, and for determining TSS. Image accuracy test is done to find out how far the image can give information about Chlorophyll-a and TSS in the waters. The results of the image accuracy test will be compared with the value of chlorophyll-a and TSS that have been tested through laboratory analysis. The result of this research is the distribution map of chlorophyll-a and TSS content in the waters.

This paper summarises the case for HydroGNSS, as developed during its System Consolidation study. HydroGNSS is a high value dual small satellite mission, which will prove new concepts and offer timely climate observations that supplement and complement existing observations and are high in ESAs Earth Observation scientific priorities.

This paper proposes a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and finally obtains a soil moisture product with a high spatial and temporal resolution of 500 m per day.

The purpose of this research is to study fishing-vessel detection using SAR Sentinel-1 data. In this study, the constant false alarm rate method (CFAR) for Sentinel-1 data is used for the detection of fishing vessels in Indramayu sea waters. The data used to detect ships includes SAR Sentinel-1A images and vessel monitoring system (VMS) data acquired on 8 March and 20 March 2018. SAR Sentinel-1 imagery data is obtained through preprocessing and object identification using Sentinel Application Platform (SNAP) software. Overlay analysis is then used to enable discrimination of immovable and movable objects and validation of ships detected from SAR Sentinel-1 imagery is performed using VMS data.

This research exploited the possibility of using monthly image composites from Sentinel-2 imageries for rice crop yield predictions one month before the harvesting period at the field level using ML techniques in Taiwan. Three ML models, including random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), were designed to address the research question of yield predictions in four consecutive growing seasons from 2019 to 2020 using field survey data.

Normalized Difference Vegetation Index (NDVI) bands generated from Sentinel-2A data are used to define the growth cycle of different vegetable types. NDVI time series allow the identification of several parameters, such as planting and maturation dates and crop cycle duration, that enable the characterization of each crop. A curve-matching algorithm, based on a set of NDVI curve parameters, were used to identify horticulture parcels.

Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes.

This study proposes a method for detecting irrigated and rainfed plots in a temperate area (southwestern France) jointly using optical (Sentinel-2), radar (Sentinel-1) and meteorological (SAFRAN) time series, through a classification algorithm. Monthly cumulative indices calculated from these satellite data were used in a Random Forest classifier. Two data years have been used, with different meteorological characteristics, allowing the performance of the method to be analysed under different climatic conditions.

This research explores the sensitivity of Sentinel-1 coherent dual-polarised data for two different aquaculture structures, i.e. fish cages and floating mussel production systems.

The purpose of this paper is to derive quantitative indicators of crop productivity using synthetic aperture radar (SAR). This study shows that the field-specific SAR time series can be used to characterise growth and maturation periods and to estimate the performance of cereals.

Nowadays, deep learning algorithms are becoming more popular due to the presence of trained models and one-time processing. However, the deep learning model required a large amount of computation time and needs to be tested in different regions for different applications. In the present work, the deep learning algorithm has been tested over agricultural land (over a part of Punjab state, India) using Sentinel-2 imagery. The major classes considered in the present analysis are vegetation area, water, and buildup area. The statistical results have shown that more than 80% of accuracy has been obtained using a deep learning algorithm.

In this study, deep learning (U-Net) has been implemented in the mapping of different agricultural land use types over a part of Punjab, India, using the Sentinel-2 data. As a comparative analysis, a well-known machine learning random forest (RF) has been tested. To assess the agricultural land, the major winter season crop types, i.e., wheat, berseem, mustard, and other vegetation have been considered.

The study aims to develop 10-m fine-resolution cropland extent maps using multi-temporal Sentinel-2 datasets for a watershed in Tadepalliguden, India.

CropHarvest—a satellite dataset of more than 90,000 geographically-diverse samples with agricultural labels

Identification of crop during early stage of the crop cycle can help formulate better agriculture policies and management strategies. In this context, the objective of this article is to evaluate the potential of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery in crop identification for an Indian region. A multi-class classification algorithm based on random forest is applied to the features extracted from the above mentioned satellite data sets.

In this paper, we propose a data processing chain for processing multitemporal Sentinel-1 SAR data, and show how the temporal patterns of agricultural fields can be visualized to provide a valuable overview prior to classification. We then investigate the performance of 6 machine learning methods for crop type classification of 12 crop types based on 44333 fields

The data derived from Sentinel 1 allowed us to model the NDVI of agricultural crops throughout the phenological cycle.

A machine learning algorithm was developed and the corresponding model was trained using the average NDVI values for cotton, rice, and olive trees for the period 2017-2020.

The aim of the project is to develop crop growth monitoring system using Copernicus satellite data (Sentinel-1,-2,-3) and low-resolution data from Terra MODIS satellites.

This study analyses the recently proposed dual polarimetric descriptors (viz., the pseudo scattering entropy Hc, the co-pol purity parameter mc, and the pseudo scattering-type parameter θc) from GRD SAR data for crop growth assessment. The analysis of these descriptors is carried out over a time series of Sentinel-1 data for a winter crop (wheat) and a summer crop (potato) at a test site in Germany. The algorithm is implemented on the Google Earth Engine (GEE) cloud platform for Sentinel-1 SAR data.

Phenological information ( SAR metrics) obtained from Sentinel-1 time series and Support Vector Machine (SVM) algorithm, using a non-parametric supervised learning technique, that would allow to discriminate rice and maize as main crops in the Philippines

By analyzing the time-series curves of the four characteristic indexes of NDVI, RVI, EVI and Ref (NIR) in the study area, a total of four classification methods including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Maximum Likelihood (ML) were used to classify various crops in the study area. The RF results were compared with the classification results of DT, SVM and ML, and the spatial distribution of major crops such as rice, corn, stevia, dry rice and soybean were successfully extracted and identified.

The two most common classification algorithms, random forests (RF) and support vector machine (SVM), were applied to conduct cropland classification from MSI data. Additionally, super learning was applied for more improvement, achieving overall accuracies of 90.2% to 92.2%. Of the two algorithms applied (RF and SVM), the accuracy of SVM was superior and 89.3% to 92.0% of overall accuracies were confirmed.

The development of a classification workflow for fine-scale, object-based land cover mapping, employed on terrestrial ES mapping, within the Greek terrestrial territory. The processing was implemented in the Google Earth Engine cloud computing environment using 10 m spatial resolution Sentinel-1 and Sentinel-2 data.

The research utilizes Sentinel 2A satellite imagery where the image used is obtained through the earthexplorer.usgs.gov website. Image data will be analyzed through several correction analysis processes, namely reflectance correction, sunglint, and water column or Lyzenga in order to obtain an image display that can be used to identify benthic habitats in Serewe Bay. In addition, a field data survey was also carried out to test the accuracy of the results of processing the Sentinel 2A satellite image of Serewe Bay.

In this paper, a new approach is presented to extract individual aquaculture ponds from seasonal Sentinel-2 10 m images and to combine different sets of pond objects extracted from different images into a single set of pond objects. The approach is demonstrated for the province of Nakhon Pathom (216,800 ha) in central Thailand that is a major shrimp farming area. Aquaculture ponds were extracted from near-cloud-free Sentinel-2 images acquired in January and June 2019 to reduce confusion with rice paddies that are typically vegetation covered in these months. The ponds extracted at different times were then combined using a multi-temporal object combination strategy.

Automated field delineation is a challenging task due to variations within pixel values and limited differences between classes. This study proposes an effective strategy to tackle this issue. Firstly, a framework combining the Canny operator and Watershed segmentation algorithm (CW) quickly labels the training dataset, reducing manual vectorization workload. Secondly, a CW-trained recurrent residual U-Net deep semantic segmentation network mines both low-level and deep semantic features. Finally, a boundary connecting method integrates fragmented boundaries to generate the agricultural field boundary.

The aim of this communication is to present an operational approach for mapping soil moisture at high spatial resolution (plot scale) in agriculture areas by coupling S1 and S2 images. The proposed approach is based on the inversion of the Water Cloud Model (WCM) combined with the modified Integral Equation Model (IEM). Neural networks were developed and validated using synthetic SAR C-band database.

We present a simplified tool, AgroShadow, to gain full advantage from Sentinel-2 products and solve the trade-off between omission errors of Sen2Cor (the algorithm used by the ESA) and commission errors of MAJA (the algorithm used by Centre National d’Etudes Spatiales/Deutsches Zentrum für Luft- und Raumfahrt, CNES/DLR). AgroShadow was tested and compared against Sen2Cor and MAJA in 33 Sentinel 2A-B scenes, covering the whole of 2020 and in 18 different scenarios of the whole Italian country at farming scale. AgroShadow returned the lowest error and the highest accuracy and F-score, while precision, recall, specificity, and false positive rates were always similar to the best scores which alternately were returned by Sen2Cor or MAJA.

Agricultural Monitoring exploiting Sentinel 1 and Sentinel 2. SandboxNL contains detailed explanations about the creation and usage of the parcel based Sentinel datasets – covering The Netherlands.

The performance of single regression models is compared to those of multiple regression models, including backscatter coefficients in VV and VH polarisation as well as polarimetric decomposition parameters entropy and alpha. Characteristic temporal profiles of entropy, anisotropy, and alpha reflecting the main phenological changes in plants as well as the meteorological differences between the two years are observed for both crop types.

Agricultural Crop Monitoring from Space Script is a tool that uses remote sensing technology to monitor crop growth and health from space. This script combines various satellite-based sensors and algorithms to provide farmers with real-time data on the condition of their crops, enabling them to make informed decisions about planting, fertilization, irrigation, and pest control.

The script works by collecting data from various sensors, including optical sensors that measure visible and near-infrared light and microwave sensors that measure soil moisture and vegetation water content. This data is then analyzed using algorithms to create images and maps of crop growth and health, including measurements such as plant height, biomass, and chlorophyll content.

These images and maps provide farmers with information on the condition of their crops, allowing them to identify areas that may be experiencing stress or disease, and take corrective action as needed. This can help farmers optimize their yields and reduce the use of inputs like water and fertilizer, ultimately increasing their profitability while also reducing the environmental impact of their operations.

The Agricultural Crop Monitoring from Space Script is highly scalable, making it suitable for use on large agricultural operations as well as smallholder farms. It can also be used to monitor a wide range of crops, including row crops, fruits, vegetables, and even livestock grazing areas.

Overall, this script is a powerful tool for modern agriculture, providing farmers with the data they need to optimize their operations, increase their yields, and improve the sustainability of their farming practices.

The present study was carried out in the reservoirs viz., Sri Ram Sagar, Kaddam and Swarna from the Godavari Basin, covering the period 2016-2021, as a case study to demonstrate the use of remote sensed data in fisheries stock enhancement planning. The perennial and seasonal water spread area of the reservoirs under study, estimated through composite water maps prepared using Sentinel 2A data ranged between 8 to 19 and 4 to 29%, respectively.

This study proposed a large-area mapping framework for automatically identifying actively cropped fields by fusing Vegetation-Soil-Pigment indices and Synthetic-aperture radar (SAR) time-series images (VSPS). Three temporal indicators were proposed and highlighted cropped fields by consistently higher values due to cropping activities. The proposed VSPS algorithm was exploited for national-scale mapping in China without regional adjustments using Sentinel-2 and Sentinel-1 images.

The study proposed a framework to detect the flooding area and evaluated the degree of loss using satellite time series data. First, a double-Gaussian model to adaptively determine the threshold for flooding extraction using Synthetic Aperture Radar (SAR) data. Then, researchers evaluated the disaster levels of flooding with field survey samples and optical satellite images. Finally, given that crops vary in their resilience to flooding, the study measured the vegetation index change before and after the flooding event using satellite time series data.