Topic Editors

National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
NTSG, University of Montana, Missoula, MT 59812, USA
Prof. Dr. Kebiao Mao
Institute of agricultural resources and regional planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Progress in Satellite Remote Sensing of Land Surface: New Algorithms, Sensors, and Datasets

Abstract submission deadline
30 October 2023
Manuscript submission deadline
30 December 2023
Viewed by
1287

Topic Information

Dear Colleagues,

In scientific research on Earth systems, satellite remote sensing plays the most important role in quantifying land surface states, which are represented by soil moisture, land surface temperature, vegetation, snow cover, water bodies, and glaciers. In addition to providing direct support for various industrial applications, it can also provide vital input data for researchers in the fields of climate change, river basin hydrology, agricultural application, energy budget, and the water and carbon cycle. This Topic on “Progress in Satellite Remote Sensing of Land Surface: New Algorithms, Sensors, and Datasets” will cover recent advances in remote sensing sensors, algorithms, and datasets for quantifying land surface parameters. Original research reports, review articles, and commentaries are welcome. The issue will host papers covering remote sensing algorithms for retrieving land surface parameters including, but not limited to, soil, snow and ice, forest, grass land, farmland, and water and urban areas. Papers focusing on new orbital sensors and land surface datasets are also welcome. Data from new satellites, such as the recently launched FengYun satellite series, are warmly encouraged to be used in this Topic.

Dr. Shengli Wu
Dr. Lingmei Jiang
Dr. Jinyang Du
Prof. Dr. Kebiao Mao
Dr. Tianjie Zhao
Topic Editors

Keywords

  • soil moisture
  • vegetation index
  • snow depth
  • snow water equivalents
  • water body
  • glacier
  • land surface temperature

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Atmosphere
atmosphere
3.110 3.7 2010 14.7 Days 2000 CHF Submit
Climate
climate
- 4.7 2013 13.9 Days 1600 CHF Submit
Land
land
3.905 3.2 2012 12.7 Days 2200 CHF Submit
Remote Sensing
remotesensing
5.349 7.4 2009 19.7 Days 2500 CHF Submit
Sensors
sensors
3.847 6.4 2001 15 Days 2400 CHF Submit

Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.

Published Papers (2 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
Article
Reprocessed MODIS Version 6.1 Leaf Area Index Dataset and Its Evaluation for Land Surface and Climate Modeling
Remote Sens. 2023, 15(7), 1780; https://doi.org/10.3390/rs15071780 - 27 Mar 2023
Viewed by 337
Abstract
Satellite-based leaf area index (LAI) products, such as the MODIS LAI, play an essential role in land surface and climate modeling research, from regional to global scales. However, data gaps and high-level noise can exist, thus limiting their applications to a broader scope. [...] Read more.
Satellite-based leaf area index (LAI) products, such as the MODIS LAI, play an essential role in land surface and climate modeling research, from regional to global scales. However, data gaps and high-level noise can exist, thus limiting their applications to a broader scope. Our previous work has reprocessed the MODIS LAI Collection 5 (C5) product, and the reprocessed data have been widely used these years. In this study, the MODIS C6.1 LAI data were reprocessed to broaden its application as a successor. We updated the integrated two-step method that is used for MODIS C5 LAI and implemented it into the MODIS C6.1 LAI product. Comprehensive evaluations for the original and reprocessed products were conducted. The results showed that the reprocessed LAI data had better performance in validation against reference maps. In addition, the site scale time series of reprocessed data was much smoother and more consistent with adjacent values. The global scale comparison showed that, though the MODIS C6.1 LAI does have improvements in ground validation with LAI reference maps, its spatial continuity, temporal continuity, and consistency showed little improvement when compared to C5. In contrast, the reprocessed data were more spatiotemporally continuous and consistent. Based on this evaluation, some suggestions for using various MODIS LAI products were given. This study assessed the quality of these different versions of MODIS LAI products and demonstrated the improvement of the reprocessed C6.1 data, which we recommended for use as a substitute for the reprocessed C5 data in land surface and climate modeling. Full article
Show Figures

Figure 1

Article
Laboratory Radiometric Calibration Technique of an Imaging System with Pixel-Level Adaptive Gain
Sensors 2023, 23(4), 2083; https://doi.org/10.3390/s23042083 - 13 Feb 2023
Viewed by 434
Abstract
In a routine optical remote sensor, there is a contradiction between the two requirements of high radiation sensitivity and high dynamic range. Such a problem can be solved by adopting pixel-level adaptive-gain technology, which is carried out by integrating multilevel integrating capacitors into [...] Read more.
In a routine optical remote sensor, there is a contradiction between the two requirements of high radiation sensitivity and high dynamic range. Such a problem can be solved by adopting pixel-level adaptive-gain technology, which is carried out by integrating multilevel integrating capacitors into photodetector pixels and multiple nondestructive read-outs of the target charge with a single exposure. There are four gains for any one pixel: high gain (HG), medium gain (MG), low gain (LG), and ultralow gain (ULG). This study analyzes the requirements for laboratory radiometric calibration, and we designed a laboratory calibration scheme for the distinctive imaging method of pixel-level adaptive gain. We obtained calibration coefficients for general application using one gain output, and the switching points of dynamic range and the proportional conversion relationship between adjacent gains as the adaptive-gain output. With these results, on-orbit quantification applications of spectrometers adopting pixel-level automatic gain adaptation technology are guaranteed. Full article
Show Figures

Figure 1

Back to TopTop