Analysis of Land surface Temperature change based on MODIS data, Case study: Inner Delta of Niger

Abstract

The authors thank the reviewer for thoroughly reviewing our manuscript, providing valuable suggestions to improve the manuscript. Therefore a language review has been made to the entire manuscript after all corrections addressed in the following paragraphs The authors appreciate the comments of the critics and agree that this study focuses on the influence of the characteristic places on the variation of LST during 18 years. Accordingly, a common response has been prepared for this part, and the same is given below.

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

“…Land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) are the key environmental parameters used in different disciplines such as hydrology, meteorology, ecology, land cover change and climate change studies (Cheng et al, 2010;Tomlinson et al, 2011;Gao et al, 2013;Cristóbal et al, 2017;Dembélé et al, 2018;Ibrahim and Abu-Mallouh, 2018;Mahato and Pal, 2019). A spatiotemporal variation of LST information is useful to understand energy balance, water balance, and radiation budget at different scales ranging from local to global scales (Yang et al, 2019;Phan et al, identification of cloud pixels using spectral-based methods is out of the scope of the present study rather we used presently available cloud mask algorithms to identify the cloud and shadow pixels for predicting the missing values.…”