Modeling the Leaf Area Index of Inner Mongolia Grassland Based on Machine Learning Regression Algorithms Incorporating Empirical Knowledge

Shen, Biao; Ding, Lei; Ma, Leichao; Li, Zhenwang; Pulatov, Alim; Kulenbekov, Zheenbek E.; Chen, Jiquan; Mambetova, Saltanat; Hou, Lulu; Xu, Donghui; Wang, Xu; Xin, Xiaoping

doi:10.3390/rs14174196

Cited by 10 publications

(6 citation statements)

References 48 publications

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“…The slope gradient has a negligible effect on the FVC of forest and grassland in such areas. Shen [63] and De Castilho [64] also reported that topography has little influence on vegetation parameters, and biomass estimation results in regions with low topographic relief. Above 3000 m, the topographic undulation becomes more prominent with increasing altitude, resulting in reduced soil and water conservation capacity and a significant increase in the importance of slope gradient on the FVC of forests and grassland [61].…”

Section: Influence Of Topographic Features On Fvc Retrievalmentioning

confidence: 98%

The Retrieval of Forest and Grass Fractional Vegetation Coverage in Mountain Regions Based on Spatio-Temporal Transfer Learning

Huang,

Zhou,

et al. 2023

Remote Sensing

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The vegetation cover of forests and grasslands in mountain regions plays a crucial role in regulating climate at both regional and global scales. Thus, it is necessary to develop accurate methods for estimating and monitoring fractional vegetation cover (FVC) in mountain areas. However, the complex topographic and climate factors pose significant challenges to accurately estimating the FVC of mountain forests and grassland. Existing remote sensing products, FVC retrieval methods, and FVC samples may fail to meet the required accuracy standards. In this study, we propose a method based on spatio-temporal transfer learning for the retrieval of FVC in mountain forests and grasslands, using the mountain region of Huzhu County, Qinghai Province, as the study area. The method combines simulated FVC samples, Sentinel-2 images, and mountain topographic factor data to pre-train LSTM and 1DCNN models and subsequently transfer the models to HJ-2A/B remote sensing images. The results of the study indicated the following: (1) The FVC samples generated by the proposed method (R2 = 0.7536, RMSE = 0.0596) are more accurate than those generated by the dichotomy method (R2 = 0.4997, RMSE = 0.1060) based on validation with ground truth data. (2) The LSTM model performed better than the 1DCNN model: the average R2 of the two models was 0.9275 and 0.8955; the average RMSE was 0.0653 and 0.0735. (3) Topographic features have a significant impact on FVC retrieval results, particularly in relatively high-altitude mountain regions (DEM > 3000 m) or non-growing seasons (May and October). Therefore, the proposed method has better potential in FVC fine spatio-temporal retrieval of high-resolution mountainous remote sensing images.

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Section: Influence Of Topographic Features On Fvc Retrievalmentioning

confidence: 98%

The Retrieval of Forest and Grass Fractional Vegetation Coverage in Mountain Regions Based on Spatio-Temporal Transfer Learning

Huang,

Zhou,

et al. 2023

Remote Sensing

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“…RF was chosen and used in this study because it is fast, insensitive to overfitting, and effective in handling data multicollinearity and dimensionality [99]. RF is renowned for being more accurate and outperforming other regression algorithms [100][101][102][103][104]. Above all, it offers various importance matrices in its computation, which provide valuable insights into the effects of each predictor variable on the response variable [105].…”

Section: Spatial Analysismentioning

confidence: 99%

Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform

et al. 2023

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Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m−2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m−2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m−2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m−2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m−2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m−2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.

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“…The normalized difference phenology index (NDPI) was chosen to establish the relationship between LAI among different grassland types; we generated LAI reference maps in Google Earth Engine with a spatial resolution of 30 m, which we then upscaled to a spatial resolution of 500 m. The NDPI design differs from the NDVI in that it incorporates the shortwave-infrared (SWIR) band [30]. Our other study also found that NDPI contributed the most to LAI estimation among multiple factors, including vegetation indices, climate factors, soil factors, and topography factors [31]. For each grassland type, 75% of the sampling points were taken for modeling and 25% for validation, respectively, to determine the inversion model to generate LAI reference maps.…”

Section: Leaf Area Index Reference Mapsmentioning

confidence: 99%

Comparative Verification of Leaf Area Index Products for Different Grassland Types in Inner Mongolia, China

Shen,

Guo,

et al. 2023

Remote Sensing

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Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R2 = 0.26, RMSE = 0.41 m2/m2) and typical steppe (R2 = 0.32, RMSE = 0.38 m2/m2); the GEOV2 LAI performed better in desert steppe (R2 = 0.39, RMSE = 0.30 m2/m2). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R2 = 0.94), MODIS LAI (R2 = 0.92), and GLOBMAP LAI (R2 = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan.

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Modeling the Leaf Area Index of Inner Mongolia Grassland Based on Machine Learning Regression Algorithms Incorporating Empirical Knowledge

Cited by 10 publications

References 48 publications

The Retrieval of Forest and Grass Fractional Vegetation Coverage in Mountain Regions Based on Spatio-Temporal Transfer Learning

The Retrieval of Forest and Grass Fractional Vegetation Coverage in Mountain Regions Based on Spatio-Temporal Transfer Learning

Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform

Comparative Verification of Leaf Area Index Products for Different Grassland Types in Inner Mongolia, China

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