Sentinel for Applications in Agriculture

Gomarasca, Mario A.; Tornato, Antonella; Spizzichino, Daniele; Valentini, Emiliana; Taramelli, Andrea; Satalino, G.; Vincini, Massimo; Boschetti, Mirco; Colombo, Riccardo; Rossi, Lorenza; Mondino, E. Borgogno; Perotti, Luigi; Alberto, W.; Villa, Francisco Javier Donaire

doi:10.5194/isprs-archives-xlii-3-w6-91-2019

Cited by 13 publications

(11 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, the characterization of plot size and shape with respect to S2 geometrical resolution is fundamental. Not reliable measures can in fact occur while working with fields showing small size and/or a high shape anisotropy [41,42], mostly due to the so called mixed pixels whose spectral response results from the joint contribution of different type of covers. To take care about this issue, the Shape Index (SI, Equation ( 1)) and the area of GSAA polygons were computed by ordinary GIS tools available SAGA GIS 7.5 [43].…”

Section: Selection Of Controllable Fieldsmentioning

confidence: 99%

A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

et al. 2021

View full text Add to dashboard Cite

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.

show abstract

Section: Selection Of Controllable Fieldsmentioning

confidence: 99%

A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Agricultural cartography contributes to the mapping of crops based on classification methods considering the distribution of plots, numbers of crops and cadastre applications [11]. An agricultural cadastre is a tool that responds to agrarian management needs (e.g., plot boundaries analysis) [12].…”

Section: Introductionmentioning

confidence: 99%

Geomatic Tools in Agricultural Management

Escandón-Panchana,

Herrera-Franco,

Cuevas

2023

Cigeo 2023

View full text Add to dashboard Cite

show abstract

“…In particular, the Sentinel-1 (S-1) and Sentinel-2 (S-2) mission's data make it possible to observe the Earth's surface with a spatial resolution starting at about 10 m and a frequency in Europe of about 5-6 days. Several studies are being conducted to explore the potential utilization of Sentinel-1 and Sentinel-2 imagery, separately and as a synergy, to provide support for the development and management of agriculture [10][11][12][13]. The synergy of S-1 and S-2 imagery is being investigated for the reason that optical data from S-2, unlike radar data from S-1, are often disturbed by cloudiness and do not allow ongoing observations during crucial growth stages of crops.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential Use of Sentinel-1 and 2 Satellite Imagery for Monitoring Winter Wheat Growth under Agricultural Drought Conditions in North-Western Poland

Jędrejek,

Pudełko

2023

Agriculture

View full text Add to dashboard Cite

This paper presents analyses of the potential use of Sentinel-1 (S-1) and Sentinel-2 (S-2) imagery to generate models of winter wheat growth under agricultural drought vs. normal conditions identified based on potential yield losses calculated in the Agricultural Drought Monitoring System (ADMS). The analyses carried out showed the sensitivity of satellite images to agricultural drought conditions determined in ADMS. The study was conducted in a large region, the West Pomeranian Voivodeship (NUTS PL42), and the analysis covered about 22,935 polygons with winter wheat production that constituted a total area of about 108,000 ha in the period from the 1st of April to the 1st of July 2021. For S-1 data, VH and VV backscatter and the VH/VV ratio were calculated, and for S-2 data, NDVI and NDWI indices were calculated, which were used to build models of winter wheat growth under water stress and in normal conditions. The obtained results presented in this work include: (i) Development of a test version of a model describing the winter wheat crop’s growth, with a preliminary assessment showing the potential for recognizing water shortage effects; and (ii) identification of promising indicators of water scarcity for crops, calculated based on S-1 and S-2 images, that could be recommended for application in remote sensing (RS) of drought effects as complementary multispectral and radar observations. The results obtained in this work also gave many clues regarding the direction and method of including satellite remote sensing in national monitoring programmes, which involves operations on many types of big data sets.

show abstract

Sentinel for Applications in Agriculture

Cited by 13 publications

References 6 publications

A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Geomatic Tools in Agricultural Management

Exploring the Potential Use of Sentinel-1 and 2 Satellite Imagery for Monitoring Winter Wheat Growth under Agricultural Drought Conditions in North-Western Poland

Contact Info

Product

Resources

About