Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations

Rui Jiang,Junhao Huang,Yu Zang,Zhiyan Zhou,Yan Xu,Xin Chen,Xiwen Luo,Arturo Sanchez-Azofeifa,Kati Laakso

doi:10.3390/rs13152961

Abstract

Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors must be determined to verify how much their observations are affected by clouds. This study determines the quantified wide-ranging impact of clouds on optical satellite observations by mapping the annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs) maps, and acquisition probability (AP) of ACFOs for the Sentinel 2 (2017–2019) and Landsat 8 (2014–2019) for all the paddy rice fields in Guangdong province (APRFG), China. The ATOs of Landsat 8 showed relatively stable observations compared to the Sentinel 2, and the per-field ACFOs of Sentinel 2 and Landsat 8 were unevenly distributed. The MCFOs varied on a monthly basis, but in general, the MCFOs were greater between August and December than between January and July. Additionally, the AP of usable ACFOs with 52.1% (Landsat 8) and 47.7% (Sentinel 2) indicated that these two satellite sensors provided markedly restricted observation capability for rice in the study area. Our findings are particularly important and useful in the tropics and subtropics, and the analysis has described cloud cover frequency and pervasiveness throughout different portions of the rice growing season, providing insight into how rice monitoring activities by using Sentinel 2 and Landsat 8 imagery in Guangdong would be impacted by cloud cover.

Highlights

Rice is a staple crop for more than half of the world’s population, especially in theAsian regions [1]
Earth observation satellite GF−8), could be used for analyzing the cloud cover [63]. In subtropical areas such as Guangdong, China, the large amount of remote sensing data provided by the optical satellite sensors (e.g., Landsat 8 OLI and Sentinel 2 MSI) could have provided new opportunities to observe VIs for rice at a large scale, while the cloud cover reduced the chances of cloud-free observation for rice
In order to quantitatively analyze the influence, we presented annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs), and ACFO-related acquisition probability (AP) to elaborate on the probability of satellite data (Landsat 8 and Sentinel 2) for acquired the uncontaminated imagery for managing rice in Guangdong Province, China

Summary

Introduction

Rice is a staple crop for more than half of the world’s population, especially in theAsian regions [1]. It is crucial to manage rice production in a sustainable manner for alleviating food insecurity [3]. 2021, 13, 2961 of rice growth is an important aspect of high-quality and sustainable agricultural management [4]. Vegetation index inversion of spectral information from optical remote sensing platforms was considered to be a promising and convenient method to contribute to the PNM [6,7,8]. Due to the unique advantages of stable observation time and solar radiation, as well as relatively large coverage, earth-orbiting optical satellite sensors-acquired imagery has been proven and widely used for estimating rice growth by using vegetation indices (Vis), such as normalized difference vegetation index (NDVI) [9], soil-adjusted vegetation index (SAVI) [10], and enhanced vegetation index (EVI) [11], etc

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Discussion

Conclusion