An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China

Yueran Hu,Sven Gilliams,Miao Zhang,Hongwei Zeng,Yuming Lu,Fuyou Tian,Yuanchao Li,Honghai Yang,Sen Li,Bingfang Wu

doi:10.3390/rs14051208

Abstract

Crop type classification is critical for crop production estimation and optimal water allocation. Crop type data are challenging to generate if crop reference data are lacking, especially for target years with reference data missed in collection. Is it possible to transfer a trained crop type classification model to retrace the historical spatial distribution of crop types? Taking the Hetao Irrigation District (HID) in China as the study area, this study first designed a 10 m crop type classification framework based on the Google Earth Engine (GEE) for crop type mapping in the current season. Then, its interannual transferability to accurately retrace historical crop distributions was tested. The framework used Sentinel-1/2 data as the satellite data source, combined percentile, and monthly composite approaches to generate classification metrics and employed a random forest classifier with 300 trees for crop classification. Based on the proposed framework, this study first developed a 10 m crop type map of the HID for 2020 with an overall accuracy (OA) of 0.89 and then obtained a 10 m crop type map of the HID for 2019 with an OA of 0.92 by transferring the trained model for 2020 without crop reference samples. The results indicated that the designed framework could effectively identify HID crop types and have good transferability to obtain historical crop type data with acceptable accuracy. Our results found that SWIR1, Green, and Red Edge2 were the top three reflectance bands for crop classification. The land surface water index (LSWI), normalized difference water index (NDWI), and enhanced vegetation index (EVI) were the top three vegetation indices for crop classification. April to August was the most suitable time window for crop type classification in the HID. Sentinel-1 information played a positive role in the interannual transfer of the trained model, increasing the OA from 90.73% with Sentinel 2 alone to 91.58% with Sentinel-1 and Sentinel-2 together.

Highlights

IntroductionZero hunger is a critical sustainable development goal (SDG) initiative proposed by the United Nations [1]
Introduction distributed under the terms andZero hunger is a critical sustainable development goal (SDG) initiative proposed by the United Nations [1]
We evaluated the transfer performance of the established crop classification model

Summary

Introduction

Zero hunger is a critical sustainable development goal (SDG) initiative proposed by the United Nations [1]. It faces considerable challenges from the increasing number of people suffering food insecurity due to extreme weather events (floods, droughts, and others), the COVID-19 pandemic, and local conflicts. In 2020, the Food and Agriculture conditions of the Creative Commons. Organization of the United Nations (UN FAO) estimated that more than 720 million people worldwide suffered hunger [2]. Accurate and early crop production estimation is vital to food security situation assessments. High-resolution crop type mapping can provide critical information to estimate crop production and food security assessment [3]. As the largest consumer of freshwater, agriculture is responsible for more than 70%

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