Next Article in Journal
Improvement of Soil Health and System Productivity through Crop Diversification and Residue Incorporation under Jute-Based Different Cropping Systems
Next Article in Special Issue
Remote Sensing for Palmer Amaranth (Amaranthus palmeri S. Wats.) Detection in Soybean (Glycine max (L.) Merr.)
Previous Article in Journal
Defense Response to Hemileia vastatrix in Susceptible Grafts onto Resistant Rootstock of Coffea arabica L.
Previous Article in Special Issue
Estimating Farm Wheat Yields from NDVI and Meteorological Data
Article

Cropland Suitability Assessment Using Satellite-Based Biophysical Vegetation Properties and Machine Learning

1
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
2
Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia
3
Department of Biology, Josip Juraj Strossmayer University of Osijek, Cara Hadrijana 8/A, 31000 Osijek, Croatia
*
Author to whom correspondence should be addressed.
Academic Editor: Belen Gallego-Elvira
Agronomy 2021, 11(8), 1620; https://doi.org/10.3390/agronomy11081620
Received: 1 July 2021 / Revised: 6 August 2021 / Accepted: 13 August 2021 / Published: 16 August 2021
(This article belongs to the Special Issue Remote Sensing in Agriculture)
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. View Full-Text
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 leaf area index (LAI); fraction of absorbed photosynthetically active radiation (FAPAR); random forest (RF); support vector machine (SVM); soybean; GIS-based multicriteria analysis; covariates
Show Figures

Figure 1

MDPI and ACS Style

Radočaj, D.; Jurišić, M.; Gašparović, M.; Plaščak, I.; Antonić, O. Cropland Suitability Assessment Using Satellite-Based Biophysical Vegetation Properties and Machine Learning. Agronomy 2021, 11, 1620. https://doi.org/10.3390/agronomy11081620

AMA Style

Radočaj D, Jurišić M, Gašparović M, Plaščak I, Antonić O. Cropland Suitability Assessment Using Satellite-Based Biophysical Vegetation Properties and Machine Learning. Agronomy. 2021; 11(8):1620. https://doi.org/10.3390/agronomy11081620

Chicago/Turabian Style

Radočaj, Dorijan, Mladen Jurišić, Mateo Gašparović, Ivan Plaščak, and Oleg Antonić. 2021. "Cropland Suitability Assessment Using Satellite-Based Biophysical Vegetation Properties and Machine Learning" Agronomy 11, no. 8: 1620. https://doi.org/10.3390/agronomy11081620

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop