7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa

      ,
      Remote Sensing
      MDPI AG

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Much effort has been applied in estimating the concentrations of chlorophyll-a (Chl a) in lakes. The optical complexity and lack of in situ data complicate estimating Chl a in such water bodies. We compared four established satellite reflectance algorithms—the two-band and three-band algorithms (2BDA, 3BDA), fluorescence line height (FLH), and normalized difference chlorophyll index (NDCI)—to estimate Chl a concentration in Lake Chad. We evaluated the performance and applicability of Landsat-8 (L8) and Sentinel-2 (S2) images with the four Chl a estimation algorithms. For accuracy, we compared the concentration levels from the four algorithms to those from Worldview-3 (WV3) images. We identified two promising algorithms that could be used alongside L8 and S2 satellite images to monitor Chl a concentrations in Lake Chad. With an averaged R2 of 0.8, the 3BDA and NDCI Chl a algorithms performed accurately with S2 and L8 images. For the S2 and L8 images, 3BDA had the highest performance when compared to the WV3 estimates. We demonstrate the usefulness of sensor images in improving water quality information for areas that are difficult to access or when conventional data are limited.

          Related collections

          Most cited references62

          • Record: found
          • Abstract: found
          • Article: not found

          Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves.

          Leaf chlorophyll content provides valuable information about physiological status of plants. Reflectance measurement makes it possible to quickly and non-destructively assess, in situ, the chlorophyll content in leaves. Our objective was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation in leaves with a wide range of pigment content and composition using reflectance in a few broad spectral bands. Spectral reflectance of maple, chestnut, wild vine and beech leaves in a wide range of pigment content and composition was investigated. It was shown that reciprocal reflectance (R lambda)-1 in the spectral range lambda from 520 to 550 nm and 695 to 705 nm related closely to the total chlorophyll content in leaves of all species. Subtraction of near infra-red reciprocal reflectance, (RNIR)-1, from (R lambda)-1 made index [(R lambda)(-1)-(RNIR)-1] linearly proportional to the total chlorophyll content in spectral ranges lambda from 525 to 555 nm and from 695 to 725 nm with coefficient of determination r2 > 0.94. To adjust for differences in leaf structure, the product of the latter index and NIR reflectance [(R lambda)(-1)-(RNIR)-1]*(RNIR) was used; this further increased the accuracy of the chlorophyll estimation in the range lambda from 520 to 585 nm and from 695 to 740 nm. Two independent data sets were used to validate the developed algorithms. The root mean square error of the chlorophyll prediction did not exceed 50 mumol/m2 in leaves with total chlorophyll ranged from 1 to 830 mumol/m2.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Application of vegetation index and brightness temperature for drought detection

            F.N. Kogan (1995)
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              SOLS: A lake database to monitor in the Near Real Time water level and storage variations from remote sensing data

                Bookmark

                Author and article information

                Journal
                Remote Sensing
                Remote Sensing
                MDPI AG
                2072-4292
                August 2020
                July 29 2020
                : 12
                : 15
                : 2437
                Article
                10.3390/rs12152437
                8ec022a6-1169-4707-9d90-0c075cd85b44
                © 2020

                https://creativecommons.org/licenses/by/4.0/

                History

                Comments

                Comment on this article