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Decameter Cropland LAI/FPAR Estimation From Sentinel-2 Imagery Using Google Earth Engine | IEEE Journals & Magazine | IEEE Xplore
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Decameter Cropland LAI/FPAR Estimation From Sentinel-2 Imagery Using Google Earth Engine

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Yuanheng Sun; Qiming Qin; Huazhong Ren; Yao Zhang
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Abstract:

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) products at regional and global scales have already been extensively and routinely genera...Show More

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Abstract:

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) products at regional and global scales have already been extensively and routinely generated from medium-resolution sensors. However, there is a lack of high-resolution LAI/FPAR product, which is especially essential for crop growth and drought monitoring of cropland in patches. This article proposes a processing framework for the derivation of decameter cropland LAI and FPAR in the Northern China plain from Sentinel-2 surface reflectance data with a random forest (RF) algorithm by exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The training database is generated from the spatially aggregated Sentinel-2 surface reflectance and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR product over homogeneous cropland, and the training samples are strictly filtered for the best quality. RF is then trained over the processed Sentinel-2 surface reflectance and the filtered MODIS LAI/FPAR under two input groups—one group is for Sentinel-2 spectral bands of 10-m resolution only, and the other group supplements the Sentinel-2 red-edge (RE) and shortwave infrared (SWIR) bands of 20-m resolution. Extensive comparisons and validation are carried out, and they demonstrate that the new method can generate spatial and temporal consistent LAI/FPAR with MODIS at high spatial resolution. The retrieval accuracy is slightly better for 20-m input groups than that for 10-m input groups, confirming the value of RE and/or SWIR in cropland LAI/FPAR estimate. This article also demonstrates that GEE is a suitable high-performance processing tool for high-resolution biophysical variables estimation.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 60)
Article Sequence Number: 4400614
Date of Publication: 28 January 2021

ISSN Information:

DOI: 10.1109/TGRS.2021.3052254

Funding Agency:

References is not available for this document.
Contents

I. Introduction

The world’s population is projected to be 10 billion by the year 2050, which was estimated by Food and Agriculture Organization (FAO) of the United Nation (UN) [1], and it boosts great agricultural demand for food security. The increase in food production must be accompanied by a sustainable management of agricultural lands, which requires the dynamic and massive monitoring and forecasting of crop growing status and yields [2]. Remote sensing appears as an essential tool to respond to the abovementioned requirements since it offers a nondestructive mean of providing recurrent information from the local to the global scale in a systematic way, thereby enabling the characterization of the spatial and temporal variability within a given region.

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