Chlorophyll-a (Chl-a) and phytoplankton biomass in lakes are essential to understanding the carbon cycle [1] , primary production [2] , biogeochemical cycles [3] , and overall inland and coastal water quality [4] . It is an indicator of health of the waterbodies and provides the estimation of algae in the waterbodies [1] . It can be used to monitor the algae content in lakes [5] . Although algae are a natural part of freshwater ecosystems, too much algae can cause severe problems in lakes ecosystem leading to dead zone [2] . Dead zone is the area where the aquatic organisms cannot survive due to the low oxygen level [3] . The overgrowth of algal blooms leads to harmful algal blooms which cause bad odors, health threats [1] . Therefore, an increase in algal blooms in water is the indicator of degraded water quality [1] .

A number of remote sensing based algorithms have been used to estimate Chl-a presence in lakes ( [4] [6] [7] ). However, these techniques still face challenges because the lakes have several other constituents and components and their absorption features overlap with the Chl-a [8] . Fewer remote sensing methods have been proposed to reduce the estimation error of Chl-a in turbid productive waters ( [1] [3] [9] [10] ). Normalized Difference Chlorophyll Index (NDCI) is a novel method that has been proposed to study chlorophyll contents in lakes [1] . NDCI is calculated using the same bands as Normalized Difference Vegetation Index (NDVI) and its values ranges from −1 to 1 ( [1] [5] [11] [12] [13] ). It is being increasingly used in quantifying NDCI because it reduces the error which can occur due to seasonal variability and sun angle influences [1] .

We studied the variation in chlorophyll concentration (Table 1 and Figure 2) in Zhelin Bay, Southern China (Figure 1) in this study. Since the distinctive

location and various resources it possessed of, this bay area has become the main site for diverse economic activities [14] . This study aims to assess the variation in NDVI values in Zhelin Bay before and after the construction of marine ranching (2011 and 2013) using the MODIS dataset. MODIS has been widely used in similar research [3] . Marine ranching is a type of aquaculture that began in 1970s to cultivate the marine organisms for food, or for other products in open sea or in an enclosed section of ocean [9] . Marine ranches attract the marine species and therefore increase the productivity in sea/ocean [14] . The outcome of our project would help to evaluate the impacts of marine ranch construction in our study area.

We recorded variation in NDCI values in pre (2011) and post (2013) datasets in both stations of Zhelin Bay. NDCI value at all selected stations slightly increased (Figure 3).

Satellite data helps to acquire long-term, large-scale datasets ( [14] [17] [18] [19] [20] ). We studied pre and post effects of construction of marine ranch in Zhelin Bay. Our study showed the slight increase in NDCI values in Zhelin Bay, China after the construction of marine ranch. This results corresponds with the previous

research in the area [5] . Marine ranching is created by installing artificial structures to support marine life [14] . These kinds of ranches provide shelter [21] , feeding to marine life [14] . Y. F. Wang et al. (2018) showed that there is increase in the number of species and diversity index of marine life after Zhelin Bay marine ranching was established. The biomass and density of benthos in the marine ranching also increased [18] . This supports the increase in primary productivity of the marine area after the construction of marine ranching [8] . As a result, this is a good indicator for the marine organisms’ reproduction and survival ( [5] [8] [16] ).

The proposed web based tool helped to extract satellite remote sensing data in less than 3 minutes [12] . It has a web-client as the front-end and GEE as computing back-ends ( [11] [12] [13] ). The front end is the graphical user interface (GUI) web client where the users can specify the algorithms, date range, filter the datasets and send requests to run the analyses [12] . This tool can be used in similar research and the researchers from the non programming background can use this tool for their research.

We studied the variation in Chl-a concentration in Zhelin Bay, China in this study. We recorded the positive impacts of marine ranch construction in the study area. Further research in marine ecosystems after the marine ranch construction is recommended to gain in-depth understanding of the impacts imposed by ranch construction. In addition, we strongly believe that this web based application can be handy to researchers willing to conduct research using remote sensing method. This application greatly reduces the time and effort required by conventional methods to conduct similar research [12] . Moreover, it is user friendly and can be used by researchers from all backgrounds ( [11] [12] [22] ).

Conceptualization, Ritika Prasai; methodology, Ritika Prasai; data cleaning Ritika Prasai; writing—original draft preparation, Ritika Prasai; All authors have read and agreed to the published version of the manuscript.

Publication charge of this article was covered through Texas Institute for Applied Environmental Research, Stephenville, Texas, USA.

The author declares no conflicts of interest regarding the publication of this paper.

Prasai, R. (2022) Concentration Using MODIS Data and a Web-Based Tool. A Case Study in Zhelin Bay, China. Computational Water, Energy, and Environmental Engineering, 11, 85-92. https://doi.org/10.4236/cweee.2022.113005