Evaluating Modified Soil Erodibility Factors with the Aid of Pedotransfer Functions and Dynamic Remote-Sensing Data for Soil Health Management

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

It can be accepted in present form.

Author Response

Response to Reviewer’s Comments on Land Manuscript -3435148   The authors are grateful for the Reviewers and Editor for their insightful comments and recommendations. Following the suggestions, we have revised the manuscript and a point-by-point response to the comments is prepared as listed below.   Comments and Suggestions for Authors – Academic Editor   According to the report of reviewer #2, the authors are mainly encouraged to improve the material and methods section by providing further information about the images.   The other points should also be taken into account but are secondary. Response: The comments from the Academic Editor are well received, acknowledged, and revised accordingly. The materials and methods are rewritten now and we have added the text to the response document in Reviewer #2 Comment #1 and Comment #2.   Comments and Suggestions for Authors – Assistant Editor   Please cite all references with reference numbers and place the numbers in square brackets ("[ ]"), e.g., [1], [1–3], or [1,3].  Response: The comments from the Assistant Editor are well received, acknowledged and revised accordingly. Following your suggestion, we have updated the citation formats consistently.   Comments and Suggestions for Authors – Reviewer 1   It can be accepted in present form. Response: The comments from the reviewer are well received and acknowledged.    Comments and Suggestions for Authors – Reviewer 2   Whilst the article is very important, and novel in its proposal, the text needs drastic improvements for it to be properly submitted as a research article.    1) Under materials and methods, you must list materials first, which, in this case, should be the images and DEMs used, their spectral characteristics, resolutions and other aspects. A process map could come at the beginning, but only in general terms.  Response: The comments from the Reviewer are well received, acknowledged and revised accordingly. The materials are listed first followed by methods as suggested by the Reviewer. A section “2.1. Datasets for the Study Area” and a table (Table 1) showing the image details, DEMs used, their spectral characteristics, resolutions and other aspects are added to the manuscript. The process map used (Figure 1, modified as Figure 2) is moved to the ending part of section “2.2. Kmlr Modeling”.  2.1.Datasets for the Study Area Table 1. Materials and datasets employed for the watershed modeling of the study area. Dataset Sources Data Type Spatial Resolution Temporal Resolution Details Digital Elevation Models Web GIS Raster 30 m Yearly, 2001-2011 Supplies elevation data for analyzing terrain and delineating watersheds Land Cover United States Geological Survey (USGS) Raster 30 m Yearly, 2001-2011 Comprises 8 land use types, which are utilized to model the impacts of land cover on hydrology Soil United States Department of Agriculture (USDA) Raster 60 m Yearly, 2001-2011 Contains data on 5 soil types with hydrological properties, crucial for understanding soil-water interactions Meteorology U.S. National Weather Service Gridded Data 1 km Yearly, 2001-2011 Provides data on temperature, precipitation, wind speed, solar radiation, and relative humidity, which is used in Arc SWAT’s weather generator Hydrology United States Geological Survey (USGS) Gridded Data 1 km Monthly, 2001-2011 Offers surface runoff and sediment concentration data from hydrological stations (Silver Hill and Loxley River) Crop Management Factor Remote Sensed MODIS Data Raster 250 m Yearly, 2001-2011 Supplies crop management data that reflects land use and agricultural practices influencing hydrological processes     In this study, the watershed modeling was performed using the process based hydrological model, Soil and Water Assessment Tool (SWAT). The SWAT model was employed to delineate the study area of Fish River watershed with an area of 783 sq.km. The model inputs include Digital Elevation Models (DEM), land use land cover data, soil data, and meterological data (Table 1). The model classified the delineated watershed into 7 sub basins and 36 hydrological response units (HRU). Each HRU is a specific combination of a particular land use land cover, a particular soil type, and a particular slope gradient. Then the meterological data is fed into the model and the model is calibrated and validated for the spatial units of sub basins and HRUs and temporal scales of years and months. All the simulations from the SWAT model produced outputs in three different spatial scales including watershed level, sub basin level and HRU level. The study also employs MODIS based yearly data of land use land cover and undertakes a supervised classification of the land use land cover data to capture the temporal land cover changes which has implications on soil erosion rates and soil health in the watershed. The land cover dynamics are reflected through the crop and cover management factors, C-factors used in the study. Figure 1 shows the Fish River watershed with land cover dynamics spatially spread across the watershed. The SWAT model outputs predict the sediment yield (SY) for the watershed, sub basins, HRUs, and reaches for Fish River watershed between 2001 and 2011.       Figure 1: Map showing the annual land cover classification and the respective C-factor (Cc) values for Fish River watershed.    2) Yet, the section already mixes methods with the materials and even shows some early conclusions.  Response: The comments from the Reviewer are well received, acknowledged and revised accordingly.  The section “2.1. Datasets for the Study Area” which are the materials used, are explained first as suggested by the Reviewer.  2.1.Datasets for the Study Area Table 1. Materials and datasets employed for the watershed modeling of the study area. Dataset Sources Data Type Spatial Resolution Temporal Resolution Details Digital Elevation Models Web GIS Raster 30 m Yearly, 2001-2011 Supplies elevation data for analyzing terrain and delineating watersheds Land Cover United States Geological Survey (USGS) Raster 30 m Yearly, 2001-2011 Comprises 8 land use types, which are utilized to model the impacts of land cover on hydrology Soil United States Department of Agriculture (USDA) Raster 60 m Yearly, 2001-2011 Contains data on 5 soil types with hydrological properties, crucial for understanding soil-water interactions Meteorology U.S. National Weather Service Gridded Data 1 km Yearly, 2001-2011 Provides data on temperature, precipitation, wind speed, solar radiation, and relative humidity, which is used in Arc SWAT’s weather generator Hydrology United States Geological Survey (USGS) Gridded Data 1 km Monthly, 2001-2011 Offers surface runoff and sediment concentration data from hydrological stations (Silver Hill and Loxley River) Crop Management Factor Remote Sensed MODIS Data Raster 250 m Yearly, 2001-2011 Supplies crop management data that reflects land use and agricultural practices influencing hydrological processes     In this study, the watershed modeling was performed using the process based hydrological model, Soil and Water Assessment Tool (SWAT). The SWAT model was employed to delineate the study area of Fish River watershed with an area of 783 sq.km. The model inputs include Digital Elevation Models (DEM), land use land cover data, soil data, and meterological data (Table 1). The model classified the delineated watershed into 7 sub basins and 36 hydrological response units (HRU). Each HRU is a specific combination of a particular land use land cover, a particular soil type, and a particular slope gradient. Then the meterological data is fed into the model and the model is calibrated and validated for the spatial units of sub basins and HRUs and temporal scales of years and months. All the simulations from the SWAT model produced outputs in three different spatial scales including watershed level, sub basin level and HRU level. The study also employs MODIS based yearly data of land use land cover and undertakes a supervised classification of the land use land cover data to capture the temporal land cover changes which has implications on soil erosion rates and soil health in the watershed. The land cover dynamics are reflected through the crop and cover management factors, C-factors used in the study. Figure 1 shows the Fish River watershed with land cover dynamics spatially spread across the watershed. The SWAT model outputs predict the sediment yield (SY) for the watershed, sub basins, HRUs, and reaches for Fish River watershed between 2001 and 2011.       Figure 1: Map showing the annual land cover classification and the respective C-factor (Cc) values for Fish River watershed.    Next, the section “2.2. Kmlr Modeling” which is the core modeling methodology used, is explained as suggested by the Reviewer. The 2 sections are clearly separated to show the necessary information.    2.2. Kmlr Modeling The KUSLE for each type of soil with corresponding land cover is obtained from the SWAT model using the equation            (1) [44] where M is the particle size parameter, OM is the organic matter content of the soil (%), csoilstr is the soil structure code used in soil classification, cperm is the profile permeability class. The KUSLE is referred to as as the soil loss rate for every unit of soil erosivity in a specific soil as surveyed or observed on a unit plot [45].    The overestimation of the soil loss estimates by the USLE, RUSLE, and extensions of USLE such as MUSLE, USLE-M, and dUSLE resulted in novel approaches to dynamically and realistically assess K-factors, especially in catchments [46-48]. This study introduces a modified K-factor pedotransfer function uisng multiple linear regression modeling (Kmlr) integrating dynamic remotely sensed data on soil surface moisture and land cover to enhance K-factor accuracy for diverse soil health management applications. The dynamic functionality of the K-factor in the study was developed using the topographic factor (LSUSLE), C-factor, and soil properties of soil surface moisture (AWC in %), bulk density (BD in g/cm3), and permeability (Psoil in mm/h). While the original USLE is designed for bare soil, many applications of erosion modeling in more practical, real-world scenarios require accounting for vegetation to more accurately predict erosion rates in catchments or landscapes that aren't bare fallow. The study area contains an assortment of land use land covers as indicated below which justifies the need to use a crop management factor in the Kmlr equation to find the soil erodibility factor (Figure 1).    The topographic factor, LSUSLE and C-factor were calculated using the equation [49]. The values of the variables, such as AWC, BD, and Psoil were obtained from the model outputs of watershed delineation identified for the corresponding HRUs. The slope, S, was calculated from DEMs of the watershed. After categorizing the watershed into HRUs, the HRU map along with S and DEM, and the flow direction map were used to calculate HRU wise estimates of slope and longest flow length. The calculated longest flow length in each HRU was taken as slope length, L for the corresponding HRUs in the watersheds. The following operations were executed in GeoHMS-Terrain processing Tool as well as the Raster calculator in GIS. Thus, the variables of L and S used in the calculation of topographic factors were obtained from the watershed delineation results spatially joined to the corresponding HRUs from 2001 to 2011 in the watershed. The soil properties of BD and Psoil were obtained from the soil attribute characterization module (.sol) of the SWAT model. They were calculated by the spatial join of the soil map (soil type) and HRU map (HRU ID) in the SWAT model. Thus, the developed model of K-factor, Kmlr serves as a dynamic and realistic improvement of the KUSLE equation in terms of capturing the HRU wise as well as annual variations in soil erodibility and is given here:        (2) [50]     Both KUSLE and Kmlr are expressed as ton acre hour per acre feet ton inch in U.S. customary units (metric ton. hectare.hour per hectare.megajoule.millimeter in S.I. units). The coefficients presented in Eq. (2) were employed and the developed K-factor model was calibrated with the historical soil erodibility factor estimates, yielding substantial performance improvements in the study areas (R2 = 0.903, PR2 = 0.821, p < 0.05 [50]. The workflow describing SWAT model development and simulations using SWAT based inbuilt K-factors and the later phase where K-factor modification is performed using the additional soil properties of soil surface moisture and soil bulk density are shown in Figure 2.    Figure 2. Methodology applied for the generation of a modified K-factor for annual estimates of soil erodibility and soil loss in watersheds, sub basins and HRUs.   The section “2.3. Kmlr Modification Approaches” is reworked to show only the methods part as suggested by the Reviewer.  2.3. Kmlr Modification Approaches  2.3.1. High-Resolution Satellite Data in Kmlr  High-resolution satellite data enhances the spatial and temporal accuracy of soil property estimation, providing essential information on land cover, vegetation indices, soil moisture, and topographic features that are crucial for calculating the K-factor. The soil surface moisture estimates which are vital for calculating the K-factor could be generated from satellite data with higher resolution, providing detailed information on soil moisture profiling at a fine resolution. The steps used in this modification process are as follows:  Replace STATSGO soil data with satellite-derived soil moisture data. Process the satellite-derived soil moisture data for integrating into GIS with spatial compatibility.   Integrate the satellite-derived soil moisture data into ArcGIS (which operates on GIS principles) and superimpose it for the study area boundary. Analyze the integrated data and generate spatially changing soil moisture data, AWC for the study area. Incorporate AWC data into the SWAT model to estimate the K-factors and SY.  2.3.2. Dynamic C-factors in Kmlr In the context of the Kmlr model, high-resolution satellite imagery enables the accurate estimation of the crop and cover management factor, which varies annually depending on land cover and vegetation dynamics. In the Kmlr model, the USLE K-factor is modified by incorporating dynamic factors inbuilt in the hydrological model such as soil surface moisture, bulk density, and permeability, which change seasonally or annually across a watershed. Hence, Kmlr model is modified with the aid of dynamic C-factors representing real land cover changes and satellite derived spatially changing soil moisture data. The steps used in this modification process are as follows:  Acquire high-resolution satellite imagery of enhanced vegetation index (EVI), fraction of photosynthetically active radiation (SR), and leaf area index (LAI), to develop the dynamic C-factor, Cdynamic functionality for the study area. Process EVI, SR, and LAI data for integrating into GIS with spatial compatibility.   Integrate EVI, SR, LAI, and the developed AWC data into ArcGIS and superimpose it for the study area boundary. Calculate Cdynamic for the HRUs and sub basins of the study area. Modify the Kmlr model by incorporating the Cdynamic estimates.  Incorporate the modified Kmlr model estimates into the SWAT model to estimate the SY. 2.3.3. Downscaled Satellite Data in Kmlr The use of downscaled data and data products are crucial for adapting coarse global or regional climate and land-use models to local conditions, especially in heterogeneous landscapes with varied soil types and land management practices [54-55]. By integrating these downscaled datasets with SWAT model outputs, the Kmlr model can more accurately capture the temporal and spatial variations in soil properties, land cover, and topography. This leads to a more realistic and adaptive K-factor calculation that can respond to environmental changes, such as changing precipitation patterns or vegetation cover, thus improving the reliability of sediment yield predictions. The steps used in this modification process are as follows:  Obtain downscaled satellite imagery of soil moisture from Soil Moisture Active Passive (SMAP). Refine spatial scales of SMAP data using the tool GeoDa and resampling methods.  Integrate the refined SMAP data into ArcGIS and superimpose it for the study area boundary. Incorporate SMAP data generated in Step 3 and Cdynamic estimates together to modify the Kmlr model.  Incorporate the modified Kmlr model estimates into the SWAT model to estimate the SY. 2.4. Sediment Yield Predictions  Sediment yield predictions are crucial for understanding soil erosion dynamics, assessing water quality, and managing soil health. Accurate predictions help in developing effective soil conservation strategies and managing the impacts of erosion on agricultural productivity. The study uses SWAT (Soil and Water Assessment Tool) for predicting the sediment yield responses in the watershed, sub basins and HRUs from 2001 to 2011. The sediment yield will be simulated in the SWAT model for the three spatial units of the basin including watershed, sub basins and HRUs under the five developed Kmlr scenarios listed in the section 2.2 and 2.3. The sediment yield in ton/ha will be calculated based on KUSLE, Kmlr, Kmlr-c, Kmlr-sat, and Kmlr-dsat and will be validated against the ground truth estimates.      Early results or findings are moved to the section “3. Results” as “3.1. Kmlr Model Modification Results” as suggested by the Reviewer.      3.1. Kmlr Model Modification Results   3.1.1. Kmlr-sat Modeling using High-Resolution Satellite Data  The traditionally used STATSGO soil data generated AWC modeled estimates with a spatial resolution of 1 km x 1 km and provided a constant soil moisture variable of AWC for the entire simulation period [51-52]. This task replaced the STATSGO dataset with spatially changing soil moisture data derived from the remotely sensed satellite imagery of AWC based on the surface reflectance dataset of MODIS with a spatial resolution of 250 m x 250 m. The task aims to investigate the enhancement in the spatial representation of K-factor outcomes with one finer spatially resolute satellite derived data. The analysis was conducted using Python programming, ArcGIS, and the Google Earth Engine Code Editor. The remotely sensed environmental data of AWC were obtained for each pixel of the derived images for the watershed. These datasets were extracted for the Fish River watershed from the LP DAAC database for the period 2001–2011. AWC refers to the soil moisture percentage in the surface soil layer and has a resolution of 250 m. The model developed is referred to as Kmlr-sat (Kmlr + MODIS based AWC data).           (3)  3.1.2. Kmlr-c Modeling using Dynamic C-factors To refine the K-factor, the modified K-factor model, Kmlr-c introduces an additional dynamic factor, Cdynamic (Kmlr + MODIS based AWC data + MODIS based Cdynamic equation).        (4) where Cdynamic is the crop and cover management factor [53]. The developed C-factor model implements the satellite remotely-sensed data of enhanced vegetation index (EVI), fraction of photosynthetically active radiation (SR), leaf area index (LAI), ratio of the area of hydrologic response unit to the total watershed area (A), slope gradient (S). a and b are parameters that determine the shape of the exponential curve of C and EVI. A value of 1.5 and 1 was assigned for a and b, respectively and the coefficients presented in Eq. (3) were employed which yielded the best fit for the C-factor model, Cdynamic for the study areas (R2 = 0.68, PR2 = 0.51, p < 0.05 [53]. The modified Kmlr equation is as follows:        (5) In this study, we utilized the remotely sensed satellite imagery of EVI, LAI, SR, and AWC from MODIS. The task aims to investigate the enhancement in the spatial representation of K-factor outcomes with a collection of four finer spatially resolute satellite derived data. The EVI, AWC, LAI, and SR values were obtained for each pixel of the derived images for the watershed from the LP DAAC database for the period 2001–2011. The EVI data (product ID: MOD13Q1) had a spatial resolution of 250 m, while the LAI and SR data product (MOD15A2H) had a resolution of 500 m. LAI is defined as the ratio of half the total surface area of leaves to the unit ground area, while SR represents the fraction of photosynthetically active radiation absorbed by green vegetation, as identified in the MODIS data product subset.  3.1.3. Kmlr-dsat Modeling using Downscaled Satellite Data In this study, the downscaled soil moisture data obtained from the satellite product Soil Moisture Active Passive (SMAP) were refined to finer spatial scales using the spatial data analysis software, GeoDa. SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture Version 3 (SPL3SMP_E) product, spanning from 2001 to 2011, were utilized in this task [56]. The study uses the inverse distance weighted method and nearest neighbor resampling method to improve the raw SMAP data’s spatial resolution of 9 km to 1 km [57-58]. GeoDa's strength lies in spatial analysis, and it can be useful for understanding spatial patterns after resampling. The model developed is referred to as Kmlr-dsat (Kmlr + SMAP based downscaled AWC data + SMAP and MODIS based Cdynamic equation).        (6)    3) The formulae are also confusing and need to be properly formatted. LaTeX would work much better than word in this case (it's obvious the authors didn't use LaTeX since formulae appear poorly formatted, which is typical of MS Word Equation). Response: The comments from the Reviewer are well received, acknowledged and revised accordingly. The formulae are properly formatted using LaTeX Overleaf tool as suggested by the Reviewer and is added to the submission.   4) Therefore, the section should have: materials (what data were used, what their characteristics are); methods (each step in the modified KLMR method) Response: The comments from the Reviewer are well received, acknowledged and revised accordingly. The materials are listed first followed by methods as suggested by the Reviewer. A section “2.1. Datasets for the Study Area” and a table (Table 1) showing the image details, DEMs used, their spectral characteristics, resolutions and other aspects are added to the manuscript. The section “2.3. Kmlr Modification Approaches” is reworked to include each step in the Kmlr modification process.   2.1.Datasets for the Study Area Table 1. Materials and datasets employed for the watershed modeling of the study area. Dataset Sources Data Type Spatial Resolution Temporal Resolution Details Digital Elevation Models Web GIS Raster 30 m Yearly, 2001-2011 Supplies elevation data for analyzing terrain and delineating watersheds Land Cover United States Geological Survey (USGS) Raster 30 m Yearly, 2001-2011 Comprises 8 land use types, which are utilized to model the impacts of land cover on hydrology Soil United States Department of Agriculture (USDA) Raster 60 m Yearly, 2001-2011 Contains data on 5 soil types with hydrological properties, crucial for understanding soil-water interactions Meteorology U.S. National Weather Service Gridded Data 1 km Yearly, 2001-2011 Provides data on temperature, precipitation, wind speed, solar radiation, and relative humidity, which is used in Arc SWAT’s weather generator Hydrology United States Geological Survey (USGS) Gridded Data 1 km Monthly, 2001-2011 Offers surface runoff and sediment concentration data from hydrological stations (Silver Hill and Loxley River) Crop Management Factor Remote Sensed MODIS Data Raster 250 m Yearly, 2001-2011 Supplies crop management data that reflects land use and agricultural practices influencing hydrological processes     In this study, the watershed modeling was performed using the process based hydrological model, Soil and Water Assessment Tool (SWAT). The SWAT model was employed to delineate the study area of Fish River watershed with an area of 783 sq.km. The model inputs include Digital Elevation Models (DEM), land use land cover data, soil data, and meterological data (Table 1). The model classified the delineated watershed into 7 sub basins and 36 hydrological response units (HRU). Each HRU is a specific combination of a particular land use land cover, a particular soil type, and a particular slope gradient. Then the meterological data is fed into the model and the model is calibrated and validated for the spatial units of sub basins and HRUs and temporal scales of years and months. All the simulations from the SWAT model produced outputs in three different spatial scales including watershed level, sub basin level and HRU level. The study also employs MODIS based yearly data of land use land cover and undertakes a supervised classification of the land use land cover data to capture the temporal land cover changes which has implications on soil erosion rates and soil health in the watershed. The land cover dynamics are reflected through the crop and cover management factors, C-factors used in the study. Figure 1 shows the Fish River watershed with land cover dynamics spatially spread across the watershed. The SWAT model outputs predict the sediment yield (SY) for the watershed, sub basins, HRUs, and reaches for Fish River watershed between 2001 and 2011.       Figure 1: Map showing the annual land cover classification and the respective C-factor (Cc) values for Fish River watershed.    2.2. Kmlr Modeling The KUSLE for each type of soil with corresponding land cover is obtained from the SWAT model using the equation            (1) [44] where M is the particle size parameter, OM is the organic matter content of the soil (%), csoilstr is the soil structure code used in soil classification, cperm is the profile permeability class. The KUSLE is referred to as as the soil loss rate for every unit of soil erosivity in a specific soil as surveyed or observed on a unit plot [45].    The overestimation of the soil loss estimates by the USLE, RUSLE, and extensions of USLE such as MUSLE, USLE-M, and dUSLE resulted in novel approaches to dynamically and realistically assess K-factors, especially in catchments [46-48]. This study introduces a modified K-factor pedotransfer function uisng multiple linear regression modeling (Kmlr) integrating dynamic remotely sensed data on soil surface moisture and land cover to enhance K-factor accuracy for diverse soil health management applications. The dynamic functionality of the K-factor in the study was developed using the topographic factor (LSUSLE), C-factor, and soil properties of soil surface moisture (AWC in %), bulk density (BD in g/cm3), and permeability (Psoil in mm/h). While the original USLE is designed for bare soil, many applications of erosion modeling in more practical, real-world scenarios require accounting for vegetation to more accurately predict erosion rates in catchments or landscapes that aren't bare fallow. The study area contains an assortment of land use land covers as indicated below which justifies the need to use a crop management factor in the Kmlr equation to find the soil erodibility factor (Figure 1).    The topographic factor, LSUSLE and C-factor were calculated using the equation [49]. The values of the variables, such as AWC, BD, and Psoil were obtained from the model outputs of watershed delineation identified for the corresponding HRUs. The slope, S, was calculated from DEMs of the watershed. After categorizing the watershed into HRUs, the HRU map along with S and DEM, and the flow direction map were used to calculate HRU wise estimates of slope and longest flow length. The calculated longest flow length in each HRU was taken as slope length, L for the corresponding HRUs in the watersheds. The following operations were executed in GeoHMS-Terrain processing Tool as well as the Raster calculator in GIS. Thus, the variables of L and S used in the calculation of topographic factors were obtained from the watershed delineation results spatially joined to the corresponding HRUs from 2001 to 2011 in the watershed. The soil properties of BD and Psoil were obtained from the soil attribute characterization module (.sol) of the SWAT model. They were calculated by the spatial join of the soil map (soil type) and HRU map (HRU ID) in the SWAT model. Thus, the developed model of K-factor, Kmlr serves as a dynamic and realistic improvement of the KUSLE equation in terms of capturing the HRU wise as well as annual variations in soil erodibility and is given here:        (2) [50]   Both KUSLE and Kmlr are expressed as ton acre hour per acre feet ton inch in U.S. customary units (metric ton. hectare.hour per hectare.megajoule.millimeter in S.I. units). The coefficients presented in Eq. (2) were employed and the developed K-factor model was calibrated with the historical soil erodibility factor estimates, yielding substantial performance improvements in the study areas (R2 = 0.903, PR2 = 0.821, p < 0.05 [50]. The workflow describing SWAT model development and simulations using SWAT based inbuilt K-factors and the later phase where K-factor modification is performed using the additional soil properties of soil surface moisture and soil bulk density are shown in Figure 2.    Figure 2. Methodology applied for the generation of a modified K-factor for annual estimates of soil erodibility and soil loss in watersheds, sub basins and HRUs. 2.3. Kmlr Modification Approaches  2.3.1. High-Resolution Satellite Data in Kmlr  High-resolution satellite data enhances the spatial and temporal accuracy of soil property estimation, providing essential information on land cover, vegetation indices, soil moisture, and topographic features that are crucial for calculating the K-factor. The soil surface moisture estimates which are vital for calculating the K-factor could be generated from satellite data with higher resolution, providing detailed information on soil moisture profiling at a fine resolution. The steps used in this modification process are as follows:  Replace STATSGO soil data with satellite-derived soil moisture data. Process the satellite-derived soil moisture data for integrating into GIS with spatial compatibility.   Integrate the satellite-derived soil moisture data into ArcGIS (which operates on GIS principles) and superimpose it for the study area boundary. Analyze the integrated data and generate spatially changing soil moisture data, AWC for the study area. Incorporate AWC data into the SWAT model to estimate the K-factors and SY.  2.3.2. Dynamic C-factors in Kmlr In the context of the Kmlr model, high-resolution satellite imagery enables the accurate estimation of the crop and cover management factor, which varies annually depending on land cover and vegetation dynamics. In the Kmlr model, the USLE K-factor is modified by incorporating dynamic factors inbuilt in the hydrological model such as soil surface moisture, bulk density, and permeability, which change seasonally or annually across a watershed. Hence, Kmlr model is modified with the aid of dynamic C-factors representing real land cover changes and satellite derived spatially changing soil moisture data. The steps used in this modification process are as follows:  Acquire high-resolution satellite imagery of enhanced vegetation index (EVI), fraction of photosynthetically active radiation (SR), and leaf area index (LAI), to develop the dynamic C-factor, Cdynamic functionality for the study area. Process EVI, SR, and LAI data for integrating into GIS with spatial compatibility.   Integrate EVI, SR, LAI, and the developed AWC data into ArcGIS and superimpose it for the study area boundary. Calculate Cdynamic for the HRUs and sub basins of the study area. Modify the Kmlr model by incorporating the Cdynamic estimates.  Incorporate the modified Kmlr model estimates into the SWAT model to estimate the SY. 2.3.3. Downscaled Satellite Data in Kmlr The use of downscaled data and data products are crucial for adapting coarse global or regional climate and land-use models to local conditions, especially in heterogeneous landscapes with varied soil types and land management practices [54-55]. By integrating these downscaled datasets with SWAT model outputs, the Kmlr model can more accurately capture the temporal and spatial variations in soil properties, land cover, and topography. This leads to a more realistic and adaptive K-factor calculation that can respond to environmental changes, such as changing precipitation patterns or vegetation cover, thus improving the reliability of sediment yield predictions. The steps used in this modification process are as follows:  Obtain downscaled satellite imagery of soil moisture from Soil Moisture Active Passive (SMAP). Refine spatial scales of SMAP data using the tool GeoDa and resampling methods. Integrate the refined SMAP data into ArcGIS and superimpose it for the study area boundary. Incorporate SMAP data generated in Step 3 and Cdynamic estimates together to modify the Kmlr model.  Incorporate the modified Kmlr model estimates into the SWAT model to estimate the SY.     5) The results section is ok, but because the previous section was so confusing, it is hard to understand some of the steps taken to achieve these results. Response: The comments from the Reviewer are well received, acknowledged and revised accordingly.  The section “3. Results” is reworked into the following sub sections for clarity and organization as suggested by the Reviewer.  “3.1. Kmlr Model Modification Results”,  “3.1.1. Kmlr-sat Modeling using High-Resolution Satellite Data”,  “3.1.2. Kmlr-c Modeling using Dynamic C-factors”,  “3.1.3. Kmlr-dsat Modeling using Downscaled Satellite Data”,  “3.2. Results of Descriptive Statistics of Kmlr Model”,  “3.3. Results of Descriptive Statistics of Modified Kmlr Models”   6) The discussion, on the other hand, is very short and merely complements the findings under results, and so are the conclusions. Response: The comments from the Reviewer are well received, acknowledged and revised accordingly.    The section “4. Discussions” is reworked and expanded into the following sub sections for clarity and organization and to complement the findings in the section “3. Results” as suggested by the Reviewer.  “4.1. Kmlr Versus KUSLE”,  “4.2. KUSLE and Kmlr Effects on Sediment Yield Predictions”,  “4.3. Spatial Effects of K-Factors on Sediment Yields”,  “4.4. Validation of Sediment Yield Predictions: KUSLE Versus Kmlr-c”,  “4.5. Sediment Yield Predictions and Soil Loss Representation”,  “4.6. Categorization of Erosive Hotspots”   The section “5. Conclusions” is expanded to include the main outcomes of the study as suggested by the Reviewer.    5. Conclusions Soil erosion remains a critical challenge for soil health and agricultural productivity, with the K-factor commonly used to assess soil erodibility in models like the Universal Soil Loss Equation. However, traditional methods often lack spatiotemporal accuracy, particularly when accounting for dynamic factors like soil moisture and land cover variations. This study presents a modified K-factor pedotransfer function (Kmlr) that integrates dynamic remotely sensed data of land use land cover, to improve the accuracy of K-factor estimates for diverse soil health management applications. The Kmlr model, incorporating high-resolution MODIS based soil surface moisture (Kmlr-sat), dynamic crop and cover management factors, Cdynamic (Kmlr-c), and downscaled soil surface moisture data from SMAP (Kmlr-dsat), is tested across spatial and temporal scales within the Fish River watershed in Alabama, a coastal region with complex soil-water interactions.  Results show that the Kmlr model, augmented by dynamic soil moisture and land cover data, significantly enhances spatial accuracy in estimating soil erodibility. The Kmlr values are generally lower than the USLE K-factor, suggesting that the modified approach reflects less erodibility in certain areas, likely due to additional dynamic factors considered in the model. Sediment yield predictions using the modified Kmlr-c model demonstrated the strongest correlation with observed sediment yield (R² = 0.980), outperforming the USLE model (R² = 0.911). This suggests that the modified Kmlr-c model, which integrates high-resolution satellite data for factors like soil surface mositure, solar radiation, leaf area index, and enhanced vegetation index, provides a more accurate representation of sediment dynamics. While both models showed strong relationships with observed sediment yield, the modified Kmlr-c model had a slightly better calibration for the specific landscape conditions of the watershed, offering more accurate predictions. The analysis of sediment yield across 36 HRUs further highlights the advantages of the modified Kmlr model in predicting soil loss. Areas with high land use variations, such as wetlands, agriculture, and forested regions, exhibited significant differences in soil loss between the modified and USLE-based models. These findings underscore the need for more tailored soil conservation strategies in regions with high erosion risk. The categorization of erosive hotspots in the watershed reveals that most areas fall under the "Low" K-factor category, with moderate soil loss. However, critical areas identified as "High" or "Medium" risk, although smaller in size, represent regions that require immediate attention for erosion control and land management interventions. The results demonstrate the potential of the modified K-factor model to enhance soil erosion predictions, guiding more effective watershed management strategies. This research highlights the importance of refining traditional K-factor models using dynamic remotely sensed data and tailored approaches like Kmlr-c. The study outcomes signifies that with a union of dynamic crop and cover management estimates and high resolution satellite data of soil surface moisture, the sediment yield and soil loss predictions could be improved, thus enhancing soil health assessments and contributing to better management practices for soil conservation and ecosystem resilience.     My suggestion is that the article should benefit from a major rewrite where each step is adequately described and the discussion section is enhanced - especially showing in which aspects the new model is better.  Response: The comments from the Reviewer are well received, acknowledged and revised accordingly. The manuscript is rewritten to adequately describe the study goals, and the discussion section is enhanced highlighting the aspects where the new model is better.     

Author Response File: Author Response.pdf

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

Whilst the article is very important, and novel in its proposal, the text needs drastic improvements for it to be properly submitted as a research article. 

1) Under materials and methods, you must list materials first, which, in this case, should be the images and DEMs used, their spectral characteristics, resolutions and other aspects. A process map could come at the beginning, but only in general terms. 

2) Yet, the section already mixes methods with the materials and even shows some early conclusions. 

3) The formulae are also confusing and need to be properly formatted. LaTeX would work much better than word in this case (it's obvious the authors didn't use LaTeX since formulae appear poorly formatted, which is typical of MS Word Equation).

4) Therefore, the section should have: materials (what data were used, what their characteristics are); methods (each step in the modified KLMR method)

5) The results section is ok, but because the previous section was so confusing, it is hard to understand some of the steps taken to achieve these results.

6) The discussion, on the other hand, is very short and merely complements the findings under results, and so are the conclusions.

My suggestion is that the article should benefit from a major rewrite where each step is adequately described and the discussion section is enhanced - especially showing in which aspects the new model is better. 

Author Response

Response to Reviewer’s Comments on Land Manuscript -3435148   The authors are grateful for the Reviewers and Editor for their insightful comments and recommendations. Following the suggestions, we have revised the manuscript and a point-by-point response to the comments is prepared as listed below.   Comments and Suggestions for Authors – Academic Editor   According to the report of reviewer #2, the authors are mainly encouraged to improve the material and methods section by providing further information about the images.   The other points should also be taken into account but are secondary. Response: The comments from the Academic Editor are well received, acknowledged, and revised accordingly. The materials and methods are rewritten now and we have added the text to the response document in Reviewer #2 Comment #1 and Comment #2.   Comments and Suggestions for Authors – Assistant Editor   Please cite all references with reference numbers and place the numbers in square brackets ("[ ]"), e.g., [1], [1–3], or [1,3].  Response: The comments from the Assistant Editor are well received, acknowledged and revised accordingly. Following your suggestion, we have updated the citation formats consistently.   Comments and Suggestions for Authors – Reviewer 1   It can be accepted in present form. Response: The comments from the reviewer are well received and acknowledged.    Comments and Suggestions for Authors – Reviewer 2   Whilst the article is very important, and novel in its proposal, the text needs drastic improvements for it to be properly submitted as a research article.    1) Under materials and methods, you must list materials first, which, in this case, should be the images and DEMs used, their spectral characteristics, resolutions and other aspects. A process map could come at the beginning, but only in general terms.  Response: The comments from the Reviewer are well received, acknowledged and revised accordingly. The materials are listed first followed by methods as suggested by the Reviewer. A section “2.1. Datasets for the Study Area” and a table (Table 1) showing the image details, DEMs used, their spectral characteristics, resolutions and other aspects are added to the manuscript. The process map used (Figure 1, modified as Figure 2) is moved to the ending part of section “2.2. Kmlr Modeling”.  2.1.Datasets for the Study Area Table 1. Materials and datasets employed for the watershed modeling of the study area. Dataset Sources Data Type Spatial Resolution Temporal Resolution Details Digital Elevation Models Web GIS Raster 30 m Yearly, 2001-2011 Supplies elevation data for analyzing terrain and delineating watersheds Land Cover United States Geological Survey (USGS) Raster 30 m Yearly, 2001-2011 Comprises 8 land use types, which are utilized to model the impacts of land cover on hydrology Soil United States Department of Agriculture (USDA) Raster 60 m Yearly, 2001-2011 Contains data on 5 soil types with hydrological properties, crucial for understanding soil-water interactions Meteorology U.S. National Weather Service Gridded Data 1 km Yearly, 2001-2011 Provides data on temperature, precipitation, wind speed, solar radiation, and relative humidity, which is used in Arc SWAT’s weather generator Hydrology United States Geological Survey (USGS) Gridded Data 1 km Monthly, 2001-2011 Offers surface runoff and sediment concentration data from hydrological stations (Silver Hill and Loxley River) Crop Management Factor Remote Sensed MODIS Data Raster 250 m Yearly, 2001-2011 Supplies crop management data that reflects land use and agricultural practices influencing hydrological processes     In this study, the watershed modeling was performed using the process based hydrological model, Soil and Water Assessment Tool (SWAT). The SWAT model was employed to delineate the study area of Fish River watershed with an area of 783 sq.km. The model inputs include Digital Elevation Models (DEM), land use land cover data, soil data, and meterological data (Table 1). The model classified the delineated watershed into 7 sub basins and 36 hydrological response units (HRU). Each HRU is a specific combination of a particular land use land cover, a particular soil type, and a particular slope gradient. Then the meterological data is fed into the model and the model is calibrated and validated for the spatial units of sub basins and HRUs and temporal scales of years and months. All the simulations from the SWAT model produced outputs in three different spatial scales including watershed level, sub basin level and HRU level. The study also employs MODIS based yearly data of land use land cover and undertakes a supervised classification of the land use land cover data to capture the temporal land cover changes which has implications on soil erosion rates and soil health in the watershed. The land cover dynamics are reflected through the crop and cover management factors, C-factors used in the study. Figure 1 shows the Fish River watershed with land cover dynamics spatially spread across the watershed. The SWAT model outputs predict the sediment yield (SY) for the watershed, sub basins, HRUs, and reaches for Fish River watershed between 2001 and 2011.       Figure 1: Map showing the annual land cover classification and the respective C-factor (Cc) values for Fish River watershed.    2) Yet, the section already mixes methods with the materials and even shows some early conclusions.  Response: The comments from the Reviewer are well received, acknowledged and revised accordingly.  The section “2.1. Datasets for the Study Area” which are the materials used, are explained first as suggested by the Reviewer.  2.1.Datasets for the Study Area Table 1. Materials and datasets employed for the watershed modeling of the study area. Dataset Sources Data Type Spatial Resolution Temporal Resolution Details Digital Elevation Models Web GIS Raster 30 m Yearly, 2001-2011 Supplies elevation data for analyzing terrain and delineating watersheds Land Cover United States Geological Survey (USGS) Raster 30 m Yearly, 2001-2011 Comprises 8 land use types, which are utilized to model the impacts of land cover on hydrology Soil United States Department of Agriculture (USDA) Raster 60 m Yearly, 2001-2011 Contains data on 5 soil types with hydrological properties, crucial for understanding soil-water interactions Meteorology U.S. National Weather Service Gridded Data 1 km Yearly, 2001-2011 Provides data on temperature, precipitation, wind speed, solar radiation, and relative humidity, which is used in Arc SWAT’s weather generator Hydrology United States Geological Survey (USGS) Gridded Data 1 km Monthly, 2001-2011 Offers surface runoff and sediment concentration data from hydrological stations (Silver Hill and Loxley River) Crop Management Factor Remote Sensed MODIS Data Raster 250 m Yearly, 2001-2011 Supplies crop management data that reflects land use and agricultural practices influencing hydrological processes     In this study, the watershed modeling was performed using the process based hydrological model, Soil and Water Assessment Tool (SWAT). The SWAT model was employed to delineate the study area of Fish River watershed with an area of 783 sq.km. The model inputs include Digital Elevation Models (DEM), land use land cover data, soil data, and meterological data (Table 1). The model classified the delineated watershed into 7 sub basins and 36 hydrological response units (HRU). Each HRU is a specific combination of a particular land use land cover, a particular soil type, and a particular slope gradient. Then the meterological data is fed into the model and the model is calibrated and validated for the spatial units of sub basins and HRUs and temporal scales of years and months. All the simulations from the SWAT model produced outputs in three different spatial scales including watershed level, sub basin level and HRU level. The study also employs MODIS based yearly data of land use land cover and undertakes a supervised classification of the land use land cover data to capture the temporal land cover changes which has implications on soil erosion rates and soil health in the watershed. The land cover dynamics are reflected through the crop and cover management factors, C-factors used in the study. Figure 1 shows the Fish River watershed with land cover dynamics spatially spread across the watershed. The SWAT model outputs predict the sediment yield (SY) for the watershed, sub basins, HRUs, and reaches for Fish River watershed between 2001 and 2011.       Figure 1: Map showing the annual land cover classification and the respective C-factor (Cc) values for Fish River watershed.    Next, the section “2.2. Kmlr Modeling” which is the core modeling methodology used, is explained as suggested by the Reviewer. The 2 sections are clearly separated to show the necessary information.    2.2. Kmlr Modeling The KUSLE for each type of soil with corresponding land cover is obtained from the SWAT model using the equation            (1) [44] where M is the particle size parameter, OM is the organic matter content of the soil (%), csoilstr is the soil structure code used in soil classification, cperm is the profile permeability class. The KUSLE is referred to as as the soil loss rate for every unit of soil erosivity in a specific soil as surveyed or observed on a unit plot [45].    The overestimation of the soil loss estimates by the USLE, RUSLE, and extensions of USLE such as MUSLE, USLE-M, and dUSLE resulted in novel approaches to dynamically and realistically assess K-factors, especially in catchments [46-48]. This study introduces a modified K-factor pedotransfer function uisng multiple linear regression modeling (Kmlr) integrating dynamic remotely sensed data on soil surface moisture and land cover to enhance K-factor accuracy for diverse soil health management applications. The dynamic functionality of the K-factor in the study was developed using the topographic factor (LSUSLE), C-factor, and soil properties of soil surface moisture (AWC in %), bulk density (BD in g/cm3), and permeability (Psoil in mm/h). While the original USLE is designed for bare soil, many applications of erosion modeling in more practical, real-world scenarios require accounting for vegetation to more accurately predict erosion rates in catchments or landscapes that aren't bare fallow. The study area contains an assortment of land use land covers as indicated below which justifies the need to use a crop management factor in the Kmlr equation to find the soil erodibility factor (Figure 1).    The topographic factor, LSUSLE and C-factor were calculated using the equation [49]. The values of the variables, such as AWC, BD, and Psoil were obtained from the model outputs of watershed delineation identified for the corresponding HRUs. The slope, S, was calculated from DEMs of the watershed. After categorizing the watershed into HRUs, the HRU map along with S and DEM, and the flow direction map were used to calculate HRU wise estimates of slope and longest flow length. The calculated longest flow length in each HRU was taken as slope length, L for the corresponding HRUs in the watersheds. The following operations were executed in GeoHMS-Terrain processing Tool as well as the Raster calculator in GIS. Thus, the variables of L and S used in the calculation of topographic factors were obtained from the watershed delineation results spatially joined to the corresponding HRUs from 2001 to 2011 in the watershed. The soil properties of BD and Psoil were obtained from the soil attribute characterization module (.sol) of the SWAT model. They were calculated by the spatial join of the soil map (soil type) and HRU map (HRU ID) in the SWAT model. Thus, the developed model of K-factor, Kmlr serves as a dynamic and realistic improvement of the KUSLE equation in terms of capturing the HRU wise as well as annual variations in soil erodibility and is given here:        (2) [50]     Both KUSLE and Kmlr are expressed as ton acre hour per acre feet ton inch in U.S. customary units (metric ton. hectare.hour per hectare.megajoule.millimeter in S.I. units). The coefficients presented in Eq. (2) were employed and the developed K-factor model was calibrated with the historical soil erodibility factor estimates, yielding substantial performance improvements in the study areas (R2 = 0.903, PR2 = 0.821, p < 0.05 [50]. The workflow describing SWAT model development and simulations using SWAT based inbuilt K-factors and the later phase where K-factor modification is performed using the additional soil properties of soil surface moisture and soil bulk density are shown in Figure 2.    Figure 2. Methodology applied for the generation of a modified K-factor for annual estimates of soil erodibility and soil loss in watersheds, sub basins and HRUs.   The section “2.3. Kmlr Modification Approaches” is reworked to show only the methods part as suggested by the Reviewer.  2.3. Kmlr Modification Approaches  2.3.1. High-Resolution Satellite Data in Kmlr  High-resolution satellite data enhances the spatial and temporal accuracy of soil property estimation, providing essential information on land cover, vegetation indices, soil moisture, and topographic features that are crucial for calculating the K-factor. The soil surface moisture estimates which are vital for calculating the K-factor could be generated from satellite data with higher resolution, providing detailed information on soil moisture profiling at a fine resolution. The steps used in this modification process are as follows:  Replace STATSGO soil data with satellite-derived soil moisture data. Process the satellite-derived soil moisture data for integrating into GIS with spatial compatibility.   Integrate the satellite-derived soil moisture data into ArcGIS (which operates on GIS principles) and superimpose it for the study area boundary. Analyze the integrated data and generate spatially changing soil moisture data, AWC for the study area. Incorporate AWC data into the SWAT model to estimate the K-factors and SY.  2.3.2. Dynamic C-factors in Kmlr In the context of the Kmlr model, high-resolution satellite imagery enables the accurate estimation of the crop and cover management factor, which varies annually depending on land cover and vegetation dynamics. In the Kmlr model, the USLE K-factor is modified by incorporating dynamic factors inbuilt in the hydrological model such as soil surface moisture, bulk density, and permeability, which change seasonally or annually across a watershed. Hence, Kmlr model is modified with the aid of dynamic C-factors representing real land cover changes and satellite derived spatially changing soil moisture data. The steps used in this modification process are as follows:  Acquire high-resolution satellite imagery of enhanced vegetation index (EVI), fraction of photosynthetically active radiation (SR), and leaf area index (LAI), to develop the dynamic C-factor, Cdynamic functionality for the study area. Process EVI, SR, and LAI data for integrating into GIS with spatial compatibility.   Integrate EVI, SR, LAI, and the developed AWC data into ArcGIS and superimpose it for the study area boundary. Calculate Cdynamic for the HRUs and sub basins of the study area. Modify the Kmlr model by incorporating the Cdynamic estimates.  Incorporate the modified Kmlr model estimates into the SWAT model to estimate the SY. 2.3.3. Downscaled Satellite Data in Kmlr The use of downscaled data and data products are crucial for adapting coarse global or regional climate and land-use models to local conditions, especially in heterogeneous landscapes with varied soil types and land management practices [54-55]. By integrating these downscaled datasets with SWAT model outputs, the Kmlr model can more accurately capture the temporal and spatial variations in soil properties, land cover, and topography. This leads to a more realistic and adaptive K-factor calculation that can respond to environmental changes, such as changing precipitation patterns or vegetation cover, thus improving the reliability of sediment yield predictions. The steps used in this modification process are as follows:  Obtain downscaled satellite imagery of soil moisture from Soil Moisture Active Passive (SMAP). Refine spatial scales of SMAP data using the tool GeoDa and resampling methods.  Integrate the refined SMAP data into ArcGIS and superimpose it for the study area boundary. Incorporate SMAP data generated in Step 3 and Cdynamic estimates together to modify the Kmlr model.  Incorporate the modified Kmlr model estimates into the SWAT model to estimate the SY. 2.4. Sediment Yield Predictions  Sediment yield predictions are crucial for understanding soil erosion dynamics, assessing water quality, and managing soil health. Accurate predictions help in developing effective soil conservation strategies and managing the impacts of erosion on agricultural productivity. The study uses SWAT (Soil and Water Assessment Tool) for predicting the sediment yield responses in the watershed, sub basins and HRUs from 2001 to 2011. The sediment yield will be simulated in the SWAT model for the three spatial units of the basin including watershed, sub basins and HRUs under the five developed Kmlr scenarios listed in the section 2.2 and 2.3. The sediment yield in ton/ha will be calculated based on KUSLE, Kmlr, Kmlr-c, Kmlr-sat, and Kmlr-dsat and will be validated against the ground truth estimates.      Early results or findings are moved to the section “3. Results” as “3.1. Kmlr Model Modification Results” as suggested by the Reviewer.      3.1. Kmlr Model Modification Results   3.1.1. Kmlr-sat Modeling using High-Resolution Satellite Data  The traditionally used STATSGO soil data generated AWC modeled estimates with a spatial resolution of 1 km x 1 km and provided a constant soil moisture variable of AWC for the entire simulation period [51-52]. This task replaced the STATSGO dataset with spatially changing soil moisture data derived from the remotely sensed satellite imagery of AWC based on the surface reflectance dataset of MODIS with a spatial resolution of 250 m x 250 m. The task aims to investigate the enhancement in the spatial representation of K-factor outcomes with one finer spatially resolute satellite derived data. The analysis was conducted using Python programming, ArcGIS, and the Google Earth Engine Code Editor. The remotely sensed environmental data of AWC were obtained for each pixel of the derived images for the watershed. These datasets were extracted for the Fish River watershed from the LP DAAC database for the period 2001–2011. AWC refers to the soil moisture percentage in the surface soil layer and has a resolution of 250 m. The model developed is referred to as Kmlr-sat (Kmlr + MODIS based AWC data).           (3)  3.1.2. Kmlr-c Modeling using Dynamic C-factors To refine the K-factor, the modified K-factor model, Kmlr-c introduces an additional dynamic factor, Cdynamic (Kmlr + MODIS based AWC data + MODIS based Cdynamic equation).        (4) where Cdynamic is the crop and cover management factor [53]. The developed C-factor model implements the satellite remotely-sensed data of enhanced vegetation index (EVI), fraction of photosynthetically active radiation (SR), leaf area index (LAI), ratio of the area of hydrologic response unit to the total watershed area (A), slope gradient (S). a and b are parameters that determine the shape of the exponential curve of C and EVI. A value of 1.5 and 1 was assigned for a and b, respectively and the coefficients presented in Eq. (3) were employed which yielded the best fit for the C-factor model, Cdynamic for the study areas (R2 = 0.68, PR2 = 0.51, p < 0.05 [53]. The modified Kmlr equation is as follows:        (5) In this study, we utilized the remotely sensed satellite imagery of EVI, LAI, SR, and AWC from MODIS. The task aims to investigate the enhancement in the spatial representation of K-factor outcomes with a collection of four finer spatially resolute satellite derived data. The EVI, AWC, LAI, and SR values were obtained for each pixel of the derived images for the watershed from the LP DAAC database for the period 2001–2011. The EVI data (product ID: MOD13Q1) had a spatial resolution of 250 m, while the LAI and SR data product (MOD15A2H) had a resolution of 500 m. LAI is defined as the ratio of half the total surface area of leaves to the unit ground area, while SR represents the fraction of photosynthetically active radiation absorbed by green vegetation, as identified in the MODIS data product subset.  3.1.3. Kmlr-dsat Modeling using Downscaled Satellite Data In this study, the downscaled soil moisture data obtained from the satellite product Soil Moisture Active Passive (SMAP) were refined to finer spatial scales using the spatial data analysis software, GeoDa. SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture Version 3 (SPL3SMP_E) product, spanning from 2001 to 2011, were utilized in this task [56]. The study uses the inverse distance weighted method and nearest neighbor resampling method to improve the raw SMAP data’s spatial resolution of 9 km to 1 km [57-58]. GeoDa's strength lies in spatial analysis, and it can be useful for understanding spatial patterns after resampling. The model developed is referred to as Kmlr-dsat (Kmlr + SMAP based downscaled AWC data + SMAP and MODIS based Cdynamic equation).        (6)    3) The formulae are also confusing and need to be properly formatted. LaTeX would work much better than word in this case (it's obvious the authors didn't use LaTeX since formulae appear poorly formatted, which is typical of MS Word Equation). Response: The comments from the Reviewer are well received, acknowledged and revised accordingly. The formulae are properly formatted using LaTeX Overleaf tool as suggested by the Reviewer and is added to the submission.   4) Therefore, the section should have: materials (what data were used, what their characteristics are); methods (each step in the modified KLMR method) Response: The comments from the Reviewer are well received, acknowledged and revised accordingly. The materials are listed first followed by methods as suggested by the Reviewer. A section “2.1. Datasets for the Study Area” and a table (Table 1) showing the image details, DEMs used, their spectral characteristics, resolutions and other aspects are added to the manuscript. The section “2.3. Kmlr Modification Approaches” is reworked to include each step in the Kmlr modification process.   2.1.Datasets for the Study Area Table 1. Materials and datasets employed for the watershed modeling of the study area. Dataset Sources Data Type Spatial Resolution Temporal Resolution Details Digital Elevation Models Web GIS Raster 30 m Yearly, 2001-2011 Supplies elevation data for analyzing terrain and delineating watersheds Land Cover United States Geological Survey (USGS) Raster 30 m Yearly, 2001-2011 Comprises 8 land use types, which are utilized to model the impacts of land cover on hydrology Soil United States Department of Agriculture (USDA) Raster 60 m Yearly, 2001-2011 Contains data on 5 soil types with hydrological properties, crucial for understanding soil-water interactions Meteorology U.S. National Weather Service Gridded Data 1 km Yearly, 2001-2011 Provides data on temperature, precipitation, wind speed, solar radiation, and relative humidity, which is used in Arc SWAT’s weather generator Hydrology United States Geological Survey (USGS) Gridded Data 1 km Monthly, 2001-2011 Offers surface runoff and sediment concentration data from hydrological stations (Silver Hill and Loxley River) Crop Management Factor Remote Sensed MODIS Data Raster 250 m Yearly, 2001-2011 Supplies crop management data that reflects land use and agricultural practices influencing hydrological processes     In this study, the watershed modeling was performed using the process based hydrological model, Soil and Water Assessment Tool (SWAT). The SWAT model was employed to delineate the study area of Fish River watershed with an area of 783 sq.km. The model inputs include Digital Elevation Models (DEM), land use land cover data, soil data, and meterological data (Table 1). The model classified the delineated watershed into 7 sub basins and 36 hydrological response units (HRU). Each HRU is a specific combination of a particular land use land cover, a particular soil type, and a particular slope gradient. Then the meterological data is fed into the model and the model is calibrated and validated for the spatial units of sub basins and HRUs and temporal scales of years and months. All the simulations from the SWAT model produced outputs in three different spatial scales including watershed level, sub basin level and HRU level. The study also employs MODIS based yearly data of land use land cover and undertakes a supervised classification of the land use land cover data to capture the temporal land cover changes which has implications on soil erosion rates and soil health in the watershed. The land cover dynamics are reflected through the crop and cover management factors, C-factors used in the study. Figure 1 shows the Fish River watershed with land cover dynamics spatially spread across the watershed. The SWAT model outputs predict the sediment yield (SY) for the watershed, sub basins, HRUs, and reaches for Fish River watershed between 2001 and 2011.       Figure 1: Map showing the annual land cover classification and the respective C-factor (Cc) values for Fish River watershed.    2.2. Kmlr Modeling The KUSLE for each type of soil with corresponding land cover is obtained from the SWAT model using the equation            (1) [44] where M is the particle size parameter, OM is the organic matter content of the soil (%), csoilstr is the soil structure code used in soil classification, cperm is the profile permeability class. The KUSLE is referred to as as the soil loss rate for every unit of soil erosivity in a specific soil as surveyed or observed on a unit plot [45].    The overestimation of the soil loss estimates by the USLE, RUSLE, and extensions of USLE such as MUSLE, USLE-M, and dUSLE resulted in novel approaches to dynamically and realistically assess K-factors, especially in catchments [46-48]. This study introduces a modified K-factor pedotransfer function uisng multiple linear regression modeling (Kmlr) integrating dynamic remotely sensed data on soil surface moisture and land cover to enhance K-factor accuracy for diverse soil health management applications. The dynamic functionality of the K-factor in the study was developed using the topographic factor (LSUSLE), C-factor, and soil properties of soil surface moisture (AWC in %), bulk density (BD in g/cm3), and permeability (Psoil in mm/h). While the original USLE is designed for bare soil, many applications of erosion modeling in more practical, real-world scenarios require accounting for vegetation to more accurately predict erosion rates in catchments or landscapes that aren't bare fallow. The study area contains an assortment of land use land covers as indicated below which justifies the need to use a crop management factor in the Kmlr equation to find the soil erodibility factor (Figure 1).    The topographic factor, LSUSLE and C-factor were calculated using the equation [49]. The values of the variables, such as AWC, BD, and Psoil were obtained from the model outputs of watershed delineation identified for the corresponding HRUs. The slope, S, was calculated from DEMs of the watershed. After categorizing the watershed into HRUs, the HRU map along with S and DEM, and the flow direction map were used to calculate HRU wise estimates of slope and longest flow length. The calculated longest flow length in each HRU was taken as slope length, L for the corresponding HRUs in the watersheds. The following operations were executed in GeoHMS-Terrain processing Tool as well as the Raster calculator in GIS. Thus, the variables of L and S used in the calculation of topographic factors were obtained from the watershed delineation results spatially joined to the corresponding HRUs from 2001 to 2011 in the watershed. The soil properties of BD and Psoil were obtained from the soil attribute characterization module (.sol) of the SWAT model. They were calculated by the spatial join of the soil map (soil type) and HRU map (HRU ID) in the SWAT model. Thus, the developed model of K-factor, Kmlr serves as a dynamic and realistic improvement of the KUSLE equation in terms of capturing the HRU wise as well as annual variations in soil erodibility and is given here:        (2) [50]   Both KUSLE and Kmlr are expressed as ton acre hour per acre feet ton inch in U.S. customary units (metric ton. hectare.hour per hectare.megajoule.millimeter in S.I. units). The coefficients presented in Eq. (2) were employed and the developed K-factor model was calibrated with the historical soil erodibility factor estimates, yielding substantial performance improvements in the study areas (R2 = 0.903, PR2 = 0.821, p < 0.05 [50]. The workflow describing SWAT model development and simulations using SWAT based inbuilt K-factors and the later phase where K-factor modification is performed using the additional soil properties of soil surface moisture and soil bulk density are shown in Figure 2.    Figure 2. Methodology applied for the generation of a modified K-factor for annual estimates of soil erodibility and soil loss in watersheds, sub basins and HRUs. 2.3. Kmlr Modification Approaches  2.3.1. High-Resolution Satellite Data in Kmlr  High-resolution satellite data enhances the spatial and temporal accuracy of soil property estimation, providing essential information on land cover, vegetation indices, soil moisture, and topographic features that are crucial for calculating the K-factor. The soil surface moisture estimates which are vital for calculating the K-factor could be generated from satellite data with higher resolution, providing detailed information on soil moisture profiling at a fine resolution. The steps used in this modification process are as follows:  Replace STATSGO soil data with satellite-derived soil moisture data. Process the satellite-derived soil moisture data for integrating into GIS with spatial compatibility.   Integrate the satellite-derived soil moisture data into ArcGIS (which operates on GIS principles) and superimpose it for the study area boundary. Analyze the integrated data and generate spatially changing soil moisture data, AWC for the study area. Incorporate AWC data into the SWAT model to estimate the K-factors and SY.  2.3.2. Dynamic C-factors in Kmlr In the context of the Kmlr model, high-resolution satellite imagery enables the accurate estimation of the crop and cover management factor, which varies annually depending on land cover and vegetation dynamics. In the Kmlr model, the USLE K-factor is modified by incorporating dynamic factors inbuilt in the hydrological model such as soil surface moisture, bulk density, and permeability, which change seasonally or annually across a watershed. Hence, Kmlr model is modified with the aid of dynamic C-factors representing real land cover changes and satellite derived spatially changing soil moisture data. The steps used in this modification process are as follows:  Acquire high-resolution satellite imagery of enhanced vegetation index (EVI), fraction of photosynthetically active radiation (SR), and leaf area index (LAI), to develop the dynamic C-factor, Cdynamic functionality for the study area. Process EVI, SR, and LAI data for integrating into GIS with spatial compatibility.   Integrate EVI, SR, LAI, and the developed AWC data into ArcGIS and superimpose it for the study area boundary. Calculate Cdynamic for the HRUs and sub basins of the study area. Modify the Kmlr model by incorporating the Cdynamic estimates.  Incorporate the modified Kmlr model estimates into the SWAT model to estimate the SY. 2.3.3. Downscaled Satellite Data in Kmlr The use of downscaled data and data products are crucial for adapting coarse global or regional climate and land-use models to local conditions, especially in heterogeneous landscapes with varied soil types and land management practices [54-55]. By integrating these downscaled datasets with SWAT model outputs, the Kmlr model can more accurately capture the temporal and spatial variations in soil properties, land cover, and topography. This leads to a more realistic and adaptive K-factor calculation that can respond to environmental changes, such as changing precipitation patterns or vegetation cover, thus improving the reliability of sediment yield predictions. The steps used in this modification process are as follows:  Obtain downscaled satellite imagery of soil moisture from Soil Moisture Active Passive (SMAP). Refine spatial scales of SMAP data using the tool GeoDa and resampling methods. Integrate the refined SMAP data into ArcGIS and superimpose it for the study area boundary. Incorporate SMAP data generated in Step 3 and Cdynamic estimates together to modify the Kmlr model.  Incorporate the modified Kmlr model estimates into the SWAT model to estimate the SY.     5) The results section is ok, but because the previous section was so confusing, it is hard to understand some of the steps taken to achieve these results. Response: The comments from the Reviewer are well received, acknowledged and revised accordingly.  The section “3. Results” is reworked into the following sub sections for clarity and organization as suggested by the Reviewer.  “3.1. Kmlr Model Modification Results”,  “3.1.1. Kmlr-sat Modeling using High-Resolution Satellite Data”,  “3.1.2. Kmlr-c Modeling using Dynamic C-factors”,  “3.1.3. Kmlr-dsat Modeling using Downscaled Satellite Data”,  “3.2. Results of Descriptive Statistics of Kmlr Model”,  “3.3. Results of Descriptive Statistics of Modified Kmlr Models”   6) The discussion, on the other hand, is very short and merely complements the findings under results, and so are the conclusions. Response: The comments from the Reviewer are well received, acknowledged and revised accordingly.    The section “4. Discussions” is reworked and expanded into the following sub sections for clarity and organization and to complement the findings in the section “3. Results” as suggested by the Reviewer.  “4.1. Kmlr Versus KUSLE”,  “4.2. KUSLE and Kmlr Effects on Sediment Yield Predictions”,  “4.3. Spatial Effects of K-Factors on Sediment Yields”,  “4.4. Validation of Sediment Yield Predictions: KUSLE Versus Kmlr-c”,  “4.5. Sediment Yield Predictions and Soil Loss Representation”,  “4.6. Categorization of Erosive Hotspots”   The section “5. Conclusions” is expanded to include the main outcomes of the study as suggested by the Reviewer.    5. Conclusions Soil erosion remains a critical challenge for soil health and agricultural productivity, with the K-factor commonly used to assess soil erodibility in models like the Universal Soil Loss Equation. However, traditional methods often lack spatiotemporal accuracy, particularly when accounting for dynamic factors like soil moisture and land cover variations. This study presents a modified K-factor pedotransfer function (Kmlr) that integrates dynamic remotely sensed data of land use land cover, to improve the accuracy of K-factor estimates for diverse soil health management applications. The Kmlr model, incorporating high-resolution MODIS based soil surface moisture (Kmlr-sat), dynamic crop and cover management factors, Cdynamic (Kmlr-c), and downscaled soil surface moisture data from SMAP (Kmlr-dsat), is tested across spatial and temporal scales within the Fish River watershed in Alabama, a coastal region with complex soil-water interactions.  Results show that the Kmlr model, augmented by dynamic soil moisture and land cover data, significantly enhances spatial accuracy in estimating soil erodibility. The Kmlr values are generally lower than the USLE K-factor, suggesting that the modified approach reflects less erodibility in certain areas, likely due to additional dynamic factors considered in the model. Sediment yield predictions using the modified Kmlr-c model demonstrated the strongest correlation with observed sediment yield (R² = 0.980), outperforming the USLE model (R² = 0.911). This suggests that the modified Kmlr-c model, which integrates high-resolution satellite data for factors like soil surface mositure, solar radiation, leaf area index, and enhanced vegetation index, provides a more accurate representation of sediment dynamics. While both models showed strong relationships with observed sediment yield, the modified Kmlr-c model had a slightly better calibration for the specific landscape conditions of the watershed, offering more accurate predictions. The analysis of sediment yield across 36 HRUs further highlights the advantages of the modified Kmlr model in predicting soil loss. Areas with high land use variations, such as wetlands, agriculture, and forested regions, exhibited significant differences in soil loss between the modified and USLE-based models. These findings underscore the need for more tailored soil conservation strategies in regions with high erosion risk. The categorization of erosive hotspots in the watershed reveals that most areas fall under the "Low" K-factor category, with moderate soil loss. However, critical areas identified as "High" or "Medium" risk, although smaller in size, represent regions that require immediate attention for erosion control and land management interventions. The results demonstrate the potential of the modified K-factor model to enhance soil erosion predictions, guiding more effective watershed management strategies. This research highlights the importance of refining traditional K-factor models using dynamic remotely sensed data and tailored approaches like Kmlr-c. The study outcomes signifies that with a union of dynamic crop and cover management estimates and high resolution satellite data of soil surface moisture, the sediment yield and soil loss predictions could be improved, thus enhancing soil health assessments and contributing to better management practices for soil conservation and ecosystem resilience.     My suggestion is that the article should benefit from a major rewrite where each step is adequately described and the discussion section is enhanced - especially showing in which aspects the new model is better.  Response: The comments from the Reviewer are well received, acknowledged and revised accordingly. The manuscript is rewritten to adequately describe the study goals, and the discussion section is enhanced highlighting the aspects where the new model is better.     

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report (New Reviewer)

Comments and Suggestions for Authors

Whilst the authors have considerably enhanced sections 4 and 5, it still needs to be properly formatted. There are two empty tables which may be just a mistake but may also contain something relevant that ended up being deleted by accident.

Also, in my opinion, the conclusion is still missing some elements. Please make sure the conclusion fully summarizes the findings in 4, and also the abstract should point out to those findings. Now that section 4 has been increased, other article sections should be adapted accordingly.

Author Response

Response to Reviewer’s Comments on Land Manuscript -3435148

The authors are grateful for the Reviewers and Editor for their insightful comments and recommendations. Following the suggestions, we have revised the manuscript and a point-by-point response to the comments is prepared as listed below.

Comments and Suggestions for Authors – Reviewer 2

Whilst the authors have considerably enhanced sections 4 and 5, it still needs to be properly formatted. There are two empty tables which may be just a mistake but may also contain something relevant that ended up being deleted by accident.

Response: The comments from the Reviewer are well received, acknowledged, and revised accordingly. The authors thank the reviewer for the suggestion. The materials and methods are formatted properly. Authors have checked the manuscript and made sure that there are no empty tables in the manuscript.

Also, in my opinion, the conclusion is still missing some elements. Please make sure the conclusion fully summarizes the findings in 4, and also the abstract should point out to those findings. Now that section 4 has been increased, other article sections should be adapted accordingly.

Response: The comments from the Reviewer are well received, acknowledged, and revised accordingly. The authors thank the reviewer for the suggestion. This is an important comment. Following your suggestions, we have revised the abstract and conclusions and copied below.

"Abstract: Soil erosion is a critical factor impacting soil health and agricultural productivity, with soil erodibility often quantified using the K-factor in erosion models like the Universal Soil Loss Equation (USLE). Traditional K-factor estimation lacks spatiotemporal precision, particularly under varying soil moisture and land cover conditions. This study introduces modified K-factor pedotransfer functions (Kmlr) integrating dynamic remotely sensed data of land use land cover to enhance K-factor accuracy for diverse soil health management applications. The Kmlr functions from multiple approaches including dynamic crop and cover management factor (Cdynamic), high resolution satellite data, and downscaled remotely-sensed data are evaluated across spatial and temporal scales within the Fish River watershed in Alabama, a coastal watershed with significant soil-water interactions. The results highlighted that the Kmlr model provides more accurate sediment yield (SY) predictions, particularly in agricultural areas where traditional models overestimate erosion by upto 59.23 ton/ha. SY analysis across the 36 hydrological response units (HRUs) in the watershed shows that the Kmlr model captures more accurate soil loss estimates, especially in regions with varying land use. The modified K-factor model (Kmlr-c) using Cdynamic and high-resolution soil surface moisture data outperforms the traditional USLE K-factors in predicting SY, with a strong correlation to observed SY data (R² = 0.980 versus R² = 0.911). The total sediment yield predicted by Kmlr-c (525.11 ton/ha) was notably lower than that of USLE-based estimates (828.62 ton/ha), highlighting the overestimation in conventional models. The identification of erosive hotspots revealed that 6003 ha of land is at high erosion risk (K-factor > 0.25), with an average soil loss of 24.2 ton/ha. The categorization of erosive hotspots highlights critical areas at high risk for erosion, underscoring the need for targeted soil conservation practices. This research underscores the improvement of remotely sensed data-based models and perfects them for the application of soil erodibility assessments thus promoting the development of such models.
 
Keywords: K-factor; C-factor; remote sensing; soil surface moisture; land cover

Conclusions
Soil erosion remains a critical challenge for soil health and agricultural productivity, with the K-factor commonly used to assess soil erodibility in models like the Universal Soil Loss Equation. However, traditional methods often lack spatiotemporal accuracy, particularly when accounting for dynamic factors like soil moisture and land cover variations. This study presents a modified K-factor pedotransfer function (Kmlr) that integrates dynamic remotely sensed data of land use land cover, to improve the accuracy of K-factor estimates for diverse soil health management applications. The Kmlr model, incorporating high-resolution MODIS based soil surface moisture (Kmlr-sat), dynamic crop and cover management factors, Cdynamic (Kmlr-c), and downscaled soil surface moisture data from SMAP (Kmlr-dsat), is tested across spatial and temporal scales within the Fish River watershed in Alabama, a coastal region with complex soil-water interactions.
Results show that the Kmlr model, augmented by dynamic soil moisture and land cover data, significantly enhances spatial accuracy in estimating soil erodibility. The Kmlr values are generally lower than the USLE K-factor, suggesting that the modified approach reflects less erodibility in certain areas, likely due to additional dynamic factors considered in the model. Traditional USLE-based models overestimated sediment yield by up to 59.23 ton/ha in high-risk areas, while the total predicted sediment yield in the watershed was 828.62 ton/ha for USLE-based estimates, compared to 525.11 ton/ha using Kmlr-c. Sediment yield predictions using the modified Kmlr-c model demonstrated the strongest correlation with observed sediment yield (R² = 0.980), outperforming the USLE model (R² = 0.911). This suggests that the modified Kmlr-c model, which integrates high-resolution satellite data for factors like soil surface moisture, solar radiation, leaf area index, and enhanced vegetation index, provides a more accurate representation of sediment dynamics. While both models showed strong relationships with observed sediment yield, the modified Kmlr-c model had a slightly better calibration for the specific landscape conditions of the watershed, offering more accurate predictions. The analysis of sediment yield across 36 HRUs further highlights the advantages of the modified Kmlr model in predicting soil loss. Areas with high land use variations, such as wetlands, agriculture, and forested regions, exhibited significant differences in soil loss between the modified and USLE-based models. These findings underscore the need for more tailored soil conservation strategies in regions with high erosion risk.
The categorization of erosive hotspots in the watershed reveals that most areas fall under the "Low" K-factor category, with moderate soil loss. However, critical areas identified as "High" or "Medium" risk, although smaller in size, represent regions that require immediate attention for erosion control and land management interventions. Erosive hotspot identification revealed that 6003 ha of land (7.7% of the watershed) is at high erosion risk (K > 0.25), with an average soil loss of 24.2 ton/ha. Conversely, 63,000+ ha was classified as low-risk areas, demonstrating the ability of the modified model to accurately categorize erosion-prone regions. The improved model also accounts for spatial variations in soil properties, particularly in agricultural and wetland regions, where USLE-based methods failed to capture dynamic soil moisture influences. The results demonstrate the potential of the modified K-factor model to enhance soil erosion predictions, guiding more effective watershed management strategies. This research highlights the importance of refining traditional K-factor models using dynamic remotely sensed data and tailored approaches like Kmlr-c. The study outcomes signify that with a union of dynamic crop and cover management estimates and high-resolution satellite data of soil surface moisture, the sediment yield and soil loss predictions could be improved, thus enhancing soil health assessments and contributing to better management practices for soil conservation and ecosystem resilience."

Author Response File: Author Response.pdf

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Comment on land-3435148

 

This paper is one of a number of papers that misuse the USLE model.

Often, the USLE model is shown as A = RKLSCP where A is average annual soil loss, R is the average annual value of the product of storm kinetic energy and the maximum 30-min intensity, K is the soil erodibility factor, L is the slope length factor,  S is the slope gradient factor, C is the crop and crop management factor, and P is the soil protection factor. Central to the mathematical operation of USLE models is the concept of the unit plot, a bare fallow area 22.1 m long on a 9 % slope, where the average annual soil loss (A1) is given by the product of R and K as L=S=C=P = 1 on the unit plot. The fundamental mathematical model is

A = A1 LSCP. This model is based on statistical models common in evaluating agricultural field experiments where a “control” condition is used to provide the baseline condition determined by the soil and climate at a location. In the USLE, K focuses on the soil effect on erosion in that “control” situation. By definition, K focuses on erosion from bare fallow areas and so is not influenced by land cover contrary to what is considered in this paper. Given that, as noted by Kinnell and Risse (1998), erosion is given by the product of runoff and sediment concentration, K is influenced by runoff so that Ke, the value of K for an erosive event when erosivity is given by EI30, can be estimated using Ke = KUM QR where KUM is the average annual soil loss from the unit plot divided by the average annual product of EI30 and the runoff ratio for the unit plot. However, RUSLE2 has a mechanism to determine Ke on a daily basis at a given location from climate data. It deals with variations in soil moisture during the year empirically. The USLE-M requires runoff data input and has a structure that implies that event erodibility associated with the EI30 index varies with the runoff ratio. The inclusion of a vegetative cover factor in Eq. 2 is not appropriate to the application of USLE-based technology in GIS modelling of erosion in catchments.

Recommendation: Reject on scientific grounds.

Foster, G.R., Yoder, D., C., Weesis, G.A., Mc Cool, D.K., 2013. Science documentation user’s guide version 2 RUSLE2. USDA–Agricultural Research Service, Washington, DC (available at http://www. ars. usda. gov/sp2UserFiles/Place/60600505/RUSLE/RUSLE2_Science_Doc. pdf).

 

Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K., Yoder, D.C., 1997. Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE).U.S. Department of Agriculture Agricultural Handbook. No. 703. US Govt.Printing Office, Washington, DC. 404 pp.

Kinnell, P.I.A., 2019. A review of the science and logic associated with approach used in the universal soil loss equation family of models. Soil systems, 3, 62.

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study introduces modified K-factor pedotransfer functions integrating dynamic remotely sensed data of land use land cover to enhance K-factor accuracy for diverse soil health management applications. It is an intriguing topic. However, the article lacks organization and requires substantial improvements. Additionally, the language used in the study needs to be refined. The main areas that need attention are，

 

-Regarding the research significance part, the statement "This research underscores the potential of remote sensing for refining soil health assessments and informs management strategies to mitigate soil degradation and enhance ecosystem resilience." It is not precise enough. The important significance of this research should be improving and perfecting the application of models, promoting the development of models.

 

-The introduction logic is a bit unclear, the third paragraph discusses the development of technology, the fourth paragraph is about the model theory, the fifth paragraph is about the application of technology. It is suggested to clarify the model theory before explaining the application and development of technology, and finally highlight the purpose and significance of this study.

 

-From Figure 2, it can be seen that the error itself is very large, even exceeding the data itself. How can we better explain the results? Similarly, in Figure 3, R2 is very small. Does it have a fitting significance? Figure 4(a) suggests not using 3D graphs, as the results are not clear enough.

 

-Suggest writing the results and discussion separately.

 

-Conclusion need to be shortened.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf