An Overall Evaluation of Water Transparency in Lake Malawi from MERIS Data

Augusto Vundo,Fajar Setiawan,Rossi Hamzah,Takehiko Fukushima,Dalin Jiang,Bunkei Matsushita,Mangaliso Gondwe

doi:10.3390/rs11030279

Abstract

Lake Malawi is an important water resource in Africa. However, there is no routine monitoring of water quality in the lake due to financial and institutional constraints in the surrounding countries. A combination of satellite data and a semi-analytical algorithm can provide an alternative for routine monitoring of water quality, especially in developing countries. In this study, we first compared the performance of two semi-analytical algorithms, Doron11 and Lee15, which can estimate Secchi disk depth (SD) from satellite data in Lake Malawi. Our results showed that even though the SD estimations from the two algorithms were very highly correlated, the Lee15 outperformed the Doron11 in Lake Malawi with high estimation accuracy (RMSE = 1.17 m, MAPE = 18.7%, R = 0.66, p < 0.05). We then evaluated water transparency in Lake Malawi using the SD values estimated from nine years of Medium Resolution Imaging Spectrometer (MERIS) data (2003–2011) with the Lee15 algorithm. Results showed that Lake Malawi maintained four water transparency levels throughout the study period (i.e., level 1: SD > 12 m; level 2: SD between 6–12 m; level 3: SD between 3–6 m; level 4: SD between 1.5–3 m). The level 1 and 2 water areas tended to shift or trade places depending on year or season. In contrast, level 3 and 4 water areas were relatively stable and constantly distributed along the southwestern and southern lakeshores. In general, Lake Malawi is dominated by waters with SD values larger than 6 m (>95%). This study represents the first overall and comprehensive analysis of water transparency status and spatiotemporal variation in Lake Malawi.

Highlights

Lake Malawi, with an area of 29,252 km2, is the third largest lake in Africa and the ninth largest in the world, if the Aral Sea is excluded [1]
Results show that the Lee15 algorithm generally performed better than the Doron11 algorithm, with smaller Root mean square error (RMSE) value of 2.1 m (4.87 m for Doron11) and Mean absolute percent error (MAPE) value of 30.76% (83.94% for Doron11)
We first compared the performance of two semi-analytical algorithms (i.e., Doron11 and Lee15) in Lake Malawi

Summary

Introduction

Lake Malawi, with an area of 29,252 km, is the third largest lake in Africa and the ninth largest in the world, if the Aral Sea is excluded [1]. The lake serves as an important water resource, providing economic, recreational, and domestic uses for riparian countries [2]. Two main techniques are used for monitoring water quality: (1) field survey by a boat; and (2) using remote sensing data. Financial and institutional constraints in Africa make for poor availability of in situ water quality data in most African lakes [6]. In situ monitoring of a large lake such as Lake Malawi has spatial constraints, which makes it difficult to represent the characteristics of water quality across the entire lake. The remote sensing technique should be considered as an effective method for providing water quality information on African lakes, especially for monitoring a big lake such as Lake Malawi [6,7]

Objectives

Results

Conclusion