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

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

doi:10.3390/agronomy11081620

Dorijan Radočaj, Mateo Gašparović + Show 3 more

Open Access

https://doi.org/10.3390/agronomy11081620

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Abstract

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.

Highlights

The sustainability of present agricultural production faces severe global challenges in the form of rapid population growth [1], climate change [2] and increasing environmental contamination [3]
The cropland suitability classes were determined for soybean cultivation, while potentially supporting its universal applicability with the adjustments related to the vegetation period of the selected crop type
The B-spline interpolation method produced the highest downscaling accuracy, achieving a higher correlation with the ground truth data for precipitation compared with the original CHELSEA climate data

Summary

Introduction

The sustainability of present agricultural production faces severe global challenges in the form of rapid population growth [1], climate change [2] and increasing environmental contamination [3]. These factors are projected to cause serious global food nutrient deficiency by 2050 [4], urging for more efficient utilization of the current agricultural land. Determining the cropland suitability for major crop types is the mandatory process for efficient agricultural land management planning [7] This procedure is a key basis of globally sustainable agriculture and food security, meeting the Sustainability Development Goals of the United

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