Noninteger Dimension of Seasonal Land Surface Temperature (LST)

Round 1

Reviewer 1 Report

The manuscript has been wrote well. But I have some modification points,

1-Please describe the background physically based efforts to estimate/pattern detection of LST (such as 10.3390/en15041264). 

2- In manuscript, some advantages of the similar research are listed such as the human alternations in local scale land use and ..., so It is proposed to evaluation of pattern in one or two other time such as year 2000 and 2010 to shoe the effect of temporal issues on the research topic.

3- It is highly interest to describe some experimental importance point of the research. 

Author Response

We are so thankful to the reviewer for the constructive comments to help with improving the manuscript. We have significantly revised the manuscript:

 

1. Abstract: We have revised abstract to address the reviewer comment.
2. Introduction: We have added the required background regarding statistical analysis and the importance of this work and tried to provide some explanation regarding the current work and its novelty, contributions and importance.
3. We have updated the Figure 2to address the reviewer comment.
4. We have added a schematic representation regarding our methodology.
5. Discussion: The discussion section has been updated to address the reviewer comment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Please see attached file

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 2:

 

We are so thankful to the reviewer for the constructive comments to help with improving the manuscript. We have significantly revised the manuscript:

 

1. Abstract: We have revised abstract to address the reviewer comment.
2. Introduction: We have added required background regarding statistical analysis and the importance of this work and tried to provide some explanation regarding the current work and its novelty, contributions and importance.
3. We have updated the Figure 2to address the reviewer comment.
4. Discussion: The discussion section has been updated to address the reviewer comment.

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Here are our explanations regarding some of the constructive feedbacks we received:

 

MODIS (Moderate Resolution Imaging Spectroradiometer) has several advantages over Sentinel-3 and Landsat for obtaining Land Surface Temperature (LST) products. Here are some of the key advantages of MODIS:

1. Temporal Resolution: MODIS provides a higher temporal resolution compared to Sentinel-3 and Landsat. MODIS has a revisit time of 1-2 days, allowing for more frequent observations of the Earth's surface. This is especially useful for monitoring changes in LST over time and capturing diurnal variations.
2. Spectral Bands: MODIS has a broader spectral coverage compared to Sentinel-3 and Landsat. It operates in 36 spectral bands, including thermal infrared bands, which are specifically designed for measuring LST. The additional spectral bands allow for more accurate LST retrievals by accounting for atmospheric effects and improving the separation of land surface and atmospheric radiance.
3. Resolution: MODIS has a moderate spatial resolution of 250 meters (bands 1-2) and 500 meters (bands 3-7). While this resolution is coarser than the spatial resolution of Landsat (30 meters) and Sentinel-3 (300 meters), it still provides valuable information at a regional or global scale. The coarser resolution is often suitable for many applications related to climate studies and large-scale environmental monitoring.
4. Global Coverage: MODIS provides global coverage, allowing for the monitoring of LST over large areas. This is particularly important for studying global climate patterns, land surface dynamics, and monitoring changes in temperature across different regions.
5. Long-Term Data Record: MODIS has been operational since 2000 and has built a long-term data record, providing a valuable resource for studying long-term trends in LST and climate change impacts. The consistent data collection over the years allows for the analysis of interannual variations and long-term climate trends. ( https://search.earthdata.nasa.gov/ )

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In Google Earth Engine (GEE), the calibration and atmospheric correction processes for Land Surface Temperature (LST) images obtained from the Terra satellite, as well as other satellites, involve several steps. Here is an overview of the process and a comparison with other satellites and ambient temperature:

1. Data Access: In GEE, you can access the MODIS Terra LST data using the "MODIS/006/MOD11A1" image collection. This collection provides 8-day composite LST images at 1 km spatial resolution.
2. Radiometric Calibration: The LST data from MODIS Terra is already calibrated and provided in radiance units. GEE does not require additional radiometric calibration for the MODIS LST product.
3. Atmospheric Correction: GEE provides a built-in atmospheric correction algorithm called the "Split Window Algorithm" for the MODIS Terra LST product. This algorithm uses the LST values in two thermal infrared bands (band 31 and band 32) and applies a correction to account for atmospheric effects.
4. Emissivity Correction: GEE incorporates default land cover-specific emissivity values for the atmospheric correction process. These emissivity values are used to correct for variations in surface emissivity and improve the accuracy of LST estimates.
5. Comparison with Other Satellites: GEE allows you to compare LST data from different satellites, including Landsat and Sentinel-2, among others. These satellites also provide LST products that require similar calibration and atmospheric correction processes.
6. Ambient Temperature: It's important to note that LST is not equivalent to ambient temperature. LST represents the temperature of the Earth's surface, while ambient temperature refers to the temperature of the surrounding air. LST is influenced by factors such as land cover, solar radiation, and thermal properties of the surface, whereas ambient temperature is influenced by atmospheric conditions and can vary with height above the surface.

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SDS Name	Long Name	number type	unit	valid range	fill value	scale factor	add offset
LST_Day_1km	Daily daytime 1km grid Land-surface Temperature	uint16	kelvin	7500-65535	0	0.02	0.0

According to our research goal to classify LST data using fractal geometry, we could successfully show that this analytical technique provides an appropriate framework for characterizing this type data. We have also provided the following information to address reviewer comment number 5 which can be included as supporting information if this would help to gain more insight on this work:

 

 

Here is a table outlining the spatial resolution of various MODIS satellite products:

MODIS Product	Spatial Resolution
MODIS Level 1A	Varies
MODIS Level 1B	Varies
MODIS Aerosol Product	10 km
MODIS Vegetation Indices	250 m
MODIS Land Surface Temperature/Emissivity Product (MOD11)	1 km
MODIS Land Cover Product (MCD12Q1)	500 m
MODIS Snow Cover Product (MOD10A1)	500 m
MODIS Sea Surface Temperature Product (MYD28)	1 km
MODIS Chlorophyll-a Product (OC3M)	1 km
MODIS Ocean Color Product (MODISA/L2)	1 km
MODIS Fire Product (MOD14)	1 km

 

The MOD11A1 product from the Terra satellite which was used in this paper has a consistent spatial resolution of 1 kilometer (km). It provides land surface temperature (LST) and emissivity data at this spatial resolution globally ( https://search.earthdata.nasa.gov/ ).

 

 

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The authors have responded satisfactorily to the questions raised.