Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1139444

Cropland suitability assessment using satellite- based biophysical vegetation properties and machine learning


Radočaj, Dorijan; Jurišić, Mladen; Gašparović, Mateo; Plaščak, Ivan; Antonić, Oleg
Cropland suitability assessment using satellite- based biophysical vegetation properties and machine learning // Agronomy, 11 (2021), 8; 1620, 21 doi:10.3390/agronomy11081620 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1139444 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Cropland suitability assessment using satellite- based biophysical vegetation properties and machine learning

Autori
Radočaj, Dorijan ; Jurišić, Mladen ; Gašparović, Mateo ; Plaščak, Ivan ; Antonić, Oleg

Izvornik
Agronomy (2073-4395) 11 (2021), 8; 1620, 21

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
leaf area index (LAI) ; fraction of absorbed photosynthetically active radiation (FAPAR) ; random forest (RF) ; support vector machine (SVM) ; soybean ; GIS-based multicriteria analysis ; covariates

Sažetak
The determination of cropland suitability is a major step for adapting to the increased food demands caused by population growth, climate change and environmental contamination. This study presents a novel cropland suitability assessment approach based on machine learning, which overcomes the limitations of the conventional GIS- based multicriteria analysis by increasing computational efficiency, accuracy and objectivity of the prediction. The suitability assessment method was developed and evaluated for soybean cultivation within two 50 × 50 km subsets located in the continental biogeoregion of Croatia, in the four-year period during 2017– 2020. Two biophysical vegetation properties, leaf area index (LAI) and a fraction of absorbed photosynthetically active radiation (FAPAR), were utilized to train and test machine learning models. The data derived from a medium-resolution satellite mission PROBA-V were prime indicators of cropland suitability, having a high correlation to crop health, yield and biomass in previous studies. A variety of climate, soil, topography and vegetation covariates were used to establish a relationship with the training samples, with a total of 119 covariates being utilized per yearly suitability assessment. Random forest (RF) produced a superior prediction accuracy compared to support vector machine (SVM), having the mean overall accuracy of 76.6% to 68.1% for Subset A and 80.6% to 79.5% for Subset B. The 6.1% of the highly suitable FAO suitability class for soybean cultivation was determined on the sparsely utilized Subset A, while the intensively cultivated agricultural land produced only 1.5% of the same suitability class in Subset B. The applicability of the proposed method for other crop types adjusted by their respective vegetation periods, as well as the upgrade to high-resolution Sentinel-2 images, will be a subject of future research.

Izvorni jezik
Engleski

Znanstvena područja
Interdisciplinarne tehničke znanosti, Poljoprivreda (agronomija), Interdisciplinarne biotehničke znanosti



POVEZANOST RADA


Ustanove:
Geodetski fakultet, Zagreb,
Fakultet agrobiotehničkih znanosti Osijek,
Sveučilište u Osijeku - Odjel za biologiju

Citiraj ovu publikaciju

Radočaj, Dorijan; Jurišić, Mladen; Gašparović, Mateo; Plaščak, Ivan; Antonić, Oleg
Cropland suitability assessment using satellite- based biophysical vegetation properties and machine learning // Agronomy, 11 (2021), 8; 1620, 21 doi:10.3390/agronomy11081620 (međunarodna recenzija, članak, znanstveni)
Radočaj, D., Jurišić, M., Gašparović, M., Plaščak, I. & Antonić, O. (2021) Cropland suitability assessment using satellite- based biophysical vegetation properties and machine learning. Agronomy, 11 (8), 1620, 21 doi:10.3390/agronomy11081620.
@article{article, year = {2021}, pages = {21}, DOI = {10.3390/agronomy11081620}, chapter = {1620}, keywords = {leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), random forest (RF), support vector machine (SVM), soybean, GIS-based multicriteria analysis, covariates}, journal = {Agronomy}, doi = {10.3390/agronomy11081620}, volume = {11}, number = {8}, issn = {2073-4395}, title = {Cropland suitability assessment using satellite- based biophysical vegetation properties and machine learning}, keyword = {leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), random forest (RF), support vector machine (SVM), soybean, GIS-based multicriteria analysis, covariates}, chapternumber = {1620} }
@article{article, year = {2021}, pages = {21}, DOI = {10.3390/agronomy11081620}, chapter = {1620}, keywords = {leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), random forest (RF), support vector machine (SVM), soybean, GIS-based multicriteria analysis, covariates}, journal = {Agronomy}, doi = {10.3390/agronomy11081620}, volume = {11}, number = {8}, issn = {2073-4395}, title = {Cropland suitability assessment using satellite- based biophysical vegetation properties and machine learning}, keyword = {leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), random forest (RF), support vector machine (SVM), soybean, GIS-based multicriteria analysis, covariates}, chapternumber = {1620} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font