Design and Use of a Stratum-Based Yield Predictions to Address Challenges Associated with Spatial Heterogeneity and Sample Clustering in Agricultural Fields Using Remote Sensing Data

Keltoum Khechba (), Ahmed Laamrani, Mariana Belgiu, Alfred Stein, Qi Dong and Abdelghani Chehbouni
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Keltoum Khechba: Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Lot 660, Ben Guerir 43150, Morocco
Ahmed Laamrani: Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Lot 660, Ben Guerir 43150, Morocco
Mariana Belgiu: Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
Alfred Stein: Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
Qi Dong: Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Abdelghani Chehbouni: Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Lot 660, Ben Guerir 43150, Morocco

Sustainability, 2024, vol. 16, issue 21, 1-14

Abstract: Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propose decomposing the landscape complexity into homogeneous zones using existing landform, agroecological, and climate classification datasets, and subsequently applying stratum-based ML to estimate cereal yield. This approach was tested in a heterogeneous region in northern Morocco, where wheat is the dominant crop. We compared the results of the stratum-based ML with those applied to the entire study area. Sentinel-1 and Sentinel-2 satellite imagery were used as input variables to train three ML models: Random Forest, Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression. The results showed that the XGBoost model outperformed the other assessed models. Furthermore, the stratum-based ML approach significantly improved the yield estimation accuracy, particularly when using landform classifications as homogeneous strata. For example, the accuracy of XGBoost model improved from R 2 = 0.58 and RMSE = 840 kg ha −1 when the ML models were trained on data from the entire study area to R 2 = 0.72 and RMSE = 809 kg ha −1 when trained in the plain area. These findings highlight that developing stratum-based ML models using landform classification as strata leads to more accurate predictions by allowing the models to better capture local environmental conditions and agricultural practices that affect crop growth.

Keywords: spatial heterogeneity; machine learning; zoning; remote sensing; landform; local variations (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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