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Remote Sensing of Primary Production

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 18233

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Dr. Xiaobin Guan

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1. School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
2. Department of Geography and Planning, University of Toronto, No. 100 St. George St., Toronto, ON M5S 3G3, Canada
Interests: remote sensing; urban vegetation; vegetation index; spatial-temporal reconstruction; ecosystem carbon cycle; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Xing Li

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Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Korea
Interests: solar‐induced chlorophyll fluorescence; terrestrial carbon cycle; remote sensing of vegetation
Special Issues, Collections and Topics in MDPI journals

Dr. Zhaoying Zhang

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International Institute for Earth System Sciences, Nanjing University, Nanjing 210023, China
Interests: solar induced fluorescence; gross primary production; carbon cycle remote sensing

Dr. Xinyao Xie

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Guest Editor

Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Interests: vegetation productivity; mountainous areas; surface topography; process models

Special Issue Information

Dear Colleagues,

Primary productivity, which refers to the creation of new organic matter by plant communities through the process of photosynthesis, is the fundamental determinant of both the structure and functioning of terrestrial biomes. The accurate estimation of primary productivity offers opportunities to better understand vegetation dynamics, patterns of biodiversity, potential agricultural yield, and global climatic changes.

Remote sensing observations from different platforms (drone, airborne, and spaceborne) provide substantial information for vegetation structure and physiological traits monitoring at various spatial resolutions, and remote-sensing-based methods become the mainstem to obtain large-scale primary productivity. The traditional techniques mainly include vegetation-index-based methods, light use efficiency models, and process-based models, which have been extensively applied in various ecosystems. In addition, in recent years, the remote sensing of solar-induced chlorophyll fluorescence (SIF) is becoming a rapidly advancing front in terrestrial primary productivity estimates, and machine learning techniques also achieved successful applications in this field. Although significant progress has been made, there are also uncertainties in terrestrial primary productivity estimation, especially over mountainous areas. Therefore, the development of state-of-the-art strategies and innovation-driven techniques is encouraged to integrate observations from space–air–ground platforms with models to achieve improved estimates of primary productivity.

As a result, this Special Issue aims to present the latest advances on remote sensing estimation and applications of ecosystem primary productivity. Topics of interest include, but are not limited to:

Applications of new-generation and high-resolution remote sensing data in primary productivity;
Remote sensing retrieval of vegetation structure (e.g., leaf area index, clumping index, height) and physiological parameters (e.g., leaf chlorophyll content, maximum photosynthetic carboxylation rate);
Advancing terrestrial ecosystem models for vegetation productivity and biomass;
Machine learning for long-term and high-resolution primary productivity products;
Remote sensing of solar-induced fluorescence and vegetation photosynthesis;
Retrieval of biophysical parameters, primary productivity, and biomass in mountain ecosystems;
Interactions between ecosystem productivity and climate change at regional or global scales;
The variation and response of primary productivity in typical ecosystems and zones;
Vegetation mapping and classification, vegetation growth, phenology, and recovery monitoring;
Novel vegetation remote sensing data processing methods: reconstruction, temporal filtering, downscaling, and data fusion.

Dr. Xiaobin Guan
Dr. Xing Li
Dr. Zhaoying Zhang
Dr. Xinyao Xie
Guest Editors

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Keywords

remote sensing
primary productivity
terrestrial ecosystem carbon cycle
vegetation index
solar-induced chlorophyll fluorescence
light use efficiency models
process-based models
machine learning
climate change

Published Papers (10 papers)

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Research

17 pages, 42067 KiB

Open AccessArticle

How Do Driving Factors Affect Vegetation Coverage Change in the Shaanxi Region of the Qinling Mountains?

by Shuoyao Wang, Meiling Gao, Zhenhong Li, Jingjing Ma and Jianbing Peng

Remote Sens. 2024, 16(1), 160; https://doi.org/10.3390/rs16010160 - 30 Dec 2023

Viewed by 948

Abstract

Understanding the effects of natural and human disturbance factors on fractional vegetation coverage (FVC) is significant in the promotion of ecological and environmental protection. However, most of the relevant studies neglect to consider differences in the effect of driving factors on areas with different vegetation change characteristics. In this paper, we have combined Theil-Sen median trend analysis and Mann-Kendall testing to identify degraded and restored areas. Differences in the impact of various factors on FVC in terms of degradation, restoration, and the whole region were distinguished quantitatively using the geodetector model. Additionally, the constraint line approach was used to detect the influence thresholds of factors on FVC. The results are shown as below: (1) FVC showed an overall improving trend, and vegetation restoration and degradation areas accounted for 69.2% and 22.0%, respectively. (2) The two dominant factors affecting FVC were Digital Elevation Model (DEM) and temperature for both degraded and restored regions. However, the explanatory power of precipitation was noticeably different between regions. (3) Most natural factors had a “convex” constraint effect on FVC, which gradually weakened with an increase in the variable below the threshold and vice versa. Human disturbance factors negatively constrained FVC, and the constraint effect increased with increased human activity. This study can help decision-makers optimize specific implementation policies relating to ecological restoration and sustainable development. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Production)

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25 pages, 6033 KiB

Open AccessArticle

Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images

by Jorge Celis, Xiangming Xiao, Paul M. White, Jr., Osvaldo M. R. Cabral and Helber C. Freitas

Remote Sens. 2024, 16(1), 46; https://doi.org/10.3390/rs16010046 - 21 Dec 2023

Viewed by 1044

Abstract

Sugarcane croplands account for ~70% of global sugar production and ~60% of global ethanol production. Monitoring and predicting gross primary production (GPP) and transpiration (T) in these fields is crucial to improve crop yield estimation and management. While moderate-spatial-resolution (MSR, hundreds of meters) satellite images have been employed in several models to estimate GPP and T, the potential of high-spatial-resolution (HSR, tens of meters) imagery has been considered in only a few publications, and it is underexplored in sugarcane fields. Our study evaluated the efficacy of MSR and HSR satellite images in predicting daily GPP and T for sugarcane plantations at two sites equipped with eddy flux towers: Louisiana, USA (subtropical climate) and Sao Paulo, Brazil (tropical climate). We employed the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) with C4 photosynthesis pathway, integrating vegetation index data derived from satellite images and on-ground weather data, to calculate daily GPP and T. The seasonal dynamics of vegetation indices from both MSR images (MODIS sensor, 500 m) and HSR images (Landsat, 30 m; Sentinel-2, 10 m) tracked well with the GPP seasonality from the EC flux towers. The enhanced vegetation index (EVI) from the HSR images had a stronger correlation with the tower-based GPP. Our findings underscored the potential of HSR imagery for estimating GPP and T in smaller sugarcane plantations. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Production)

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18 pages, 16131 KiB

Open AccessArticle

Sensitivities of Vegetation Gross Primary Production to Precipitation Frequency in the Northern Hemisphere from 1982 to 2015

by Shouye Xue and Guocan Wu

Remote Sens. 2024, 16(1), 21; https://doi.org/10.3390/rs16010021 - 20 Dec 2023

Viewed by 1052

Abstract

Vegetation of the Northern Hemisphere plays a vital role in global ecosystems and the carbon cycle. Variations in precipitation profoundly affect vegetation productivity, plant growth, and species communities. Precipitation frequency directly controls soil moisture availability, which has an impact on the vegetation carbon sink. However, it is unclear how precipitation frequency affects the vegetation productivity of different land cover types in different seasons. In this study, the sensitivities of the gross primary production (GPP) of six vegetation types (forest, cropland, grassland, shrubland, tundra and barren land) in response to the frequency of five categories of precipitation (trace: 0.1–5 mm/day, small: 5–10 mm/day, moderate: 10–15 mm/day, heavy: 15–20 mm/day, and very heavy: >20 mm/day) were analyzed based on the XGBoost model. The results showed that, between 1982 and 2015, precipitation frequency declined in most land cover types but increased significantly in the pan-Arctic. Differences in the sensitivity to precipitation frequency were observed between seasons and precipitation categories in northern latitudes. The GPP values of forest and barren land vegetation were less sensitive to precipitation frequency than grassland, shrubland and tundra. This may be related to different vegetation community structures and underlying surfaces and gradually increasing drought resistance capability. The sensitivity to precipitation frequency declined for moderate and heavy precipitation in cropland, but it increased in winter. As the frequency of trace precipitation diminishes in winter, the sensitivity of each vegetation type reduces by an average of 0.03%/decade. Conversely, the sensitivities to small and moderate rain increase by 0.01%/decade and 0.02%/decade, respectively, for ecosystems such as cultivated land, forests, and shrubs. However, shrubs and tundra exhibit distinct behaviors, where shifts in precipitation frequency align directly with trends in sensitivity. These results show that the frequency of precipitation significantly affects vegetation productivity and has different sensitivities, and vegetation shows different feedback mechanisms in the face of environmental changes. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Production)

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22 pages, 8413 KiB

Open AccessArticle

Spatial–Temporal Variation Characteristics and Driving Factors of Net Primary Production in the Yellow River Basin over Multiple Time Scales

by Ziqi Lin, Yangyang Liu, Zhongming Wen, Xu Chen, Peidong Han, Cheng Zheng, Hongbin Yao, Zijun Wang and Haijing Shi

Remote Sens. 2023, 15(22), 5273; https://doi.org/10.3390/rs15225273 - 7 Nov 2023

Cited by 1 | Viewed by 1125

Abstract

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m² in the YRB and increased at rates of 4.64 g C/m²/a¹ (R² = 0.86, p < 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Production)

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17 pages, 4867 KiB

Open AccessArticle

Non-Ignorable Differences in NIRv-Based Estimations of Gross Primary Productivity Considering Land Cover Change and Discrepancies in Multisource Products

by Jiaxin Jin, Weiye Hou, Longhao Wang, Songhan Wang, Ying Wang, Qiuan Zhu, Xiuqin Fang and Liliang Ren

Remote Sens. 2023, 15(19), 4693; https://doi.org/10.3390/rs15194693 - 25 Sep 2023

Viewed by 861

Abstract

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm⁻²yr⁻¹ across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Production)

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23 pages, 4720 KiB

Open AccessArticle

Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems

by Yulong Lv, Hong Chi, Peichen Shi, Duan Huang, Jialiang Gan, Yifan Li, Xinyi Gao, Yifei Han, Cun Chang, Jun Wan and Feng Ling

Remote Sens. 2023, 15(16), 4002; https://doi.org/10.3390/rs15164002 - 12 Aug 2023

Viewed by 1185

Abstract

The maximum light use efficiency (LUE) (ε₀) is a key essential parameter of the LUE model, and its accurate estimation is crucial for quantifying gross primary production (GPP) and better understanding the global carbon budget. Currently, a comprehensive understanding of the potential of seasonal variations of ε₀ in GPP estimation across different plant functional types (PFTs) is still lacking. In this study, we used a phenology-based strategy for the estimation of ε₀ to find the optimal photosynthetic responses of the parameter in different phenological stages. The start and end of growing season (SOS and EOS) from time series vegetation indices and the camera-derived greenness index were extracted across seven PFT flux sites using the methods of the hybrid generalized additive model (HGAM) and double logistic function (DLF). Optimal extractions of SOS and EOS were evaluated, and the ε₀ was estimated from flux site observations during the optimal phenological stages with the light response equation. Coupled with other obligatory parameters of the LUE model, phenology-based GPP (GPP_phe-based) was estimated over 21 site-years and compared with vegetation photosynthesis model (VPM)-based GPP (GPP_VPM) and eddy covariance-measured GPP (GPP_EC). Generally, GPP_phe-based basically tracked both the seasonal dynamics and inter-annual variation of GPP_EC well, especially at forest, cropland, and wetland flux sites. The R² between GPP_phe-based and GPP_EC was stable between 0.85 and 0.95 in forest ecosystems, between 0.75 and 0.85 in cropland ecosystems, and around 0.9 in wetland ecosystems. Furthermore, we found that GPP_phe-based was significantly improved compared to GPP_VPM in cropland, grassland, and wetland ecosystems, implying that phenology-based ε₀ is more appropriate in the GPP estimation of herbaceous plants. In addition, we found that GPP_phe-based was significantly improved over GPP_VPM in cropland, grassland, and wetland ecosystems, and the R² between GPP_phe-based and GPP_EC was improved by up to 0.11 in cropland ecosystems and 0.05 in wetland ecosystems compared to GPP_VPM, and RMSE was reduced by up to 5.90 and 2.11 g C m⁻² 8 day⁻¹, respectively, implying that phenology-based ε₀ in herbaceous plants is more appropriate for GPP estimation. This work highlights the potential of phenology-based ε₀ in understanding the seasonal variation of vegetation photosynthesis and production. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Production)

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20 pages, 3882 KiB

Open AccessArticle

Quantitative Analysis of the Contributions of Climatic and Anthropogenic Factors to the Variation in Net Primary Productivity, China

by Shouhai Shi, Luping Zhu, Zhaohui Luo and Hua Qiu

Remote Sens. 2023, 15(3), 789; https://doi.org/10.3390/rs15030789 - 30 Jan 2023

Cited by 10 | Viewed by 1844

Abstract

Accurate quantification of the contributions of climatic and anthropogenic factors to the variation in NPP is critical for elucidating the relevant driving mechanisms. In this study, the spatiotemporal variation in net primary productivity (NPP) in China during 2000–2020, the interactive effects of climatic and anthropogenic factors on NPP and the optimal characteristics of driving forces were explored. Our results indicate that NPP had obvious spatial differentiation, an overall increasing trend was identified and this trend will continue in the future for more than half of the pixels. Land use and Land cover and precipitation were the main factors regulating NPP variation at both the national scale and the sub-region scale, except in southwest China, which was dominated by altitude and temperature. Moreover, an interactive effect between each pair of factors was observed and the effect of any pair of driving factors was greater than that of any single factor, manifested as either bivariate enhancement or nonlinear enhancement. Furthermore, the responses and optimal characteristics of NPP concerning driving forces were diverse. The findings provide a critical understanding of the impacts of driving forces on NPP and could help to create optimal conditions for vegetation growth to mitigate and adapt to climate changes. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Production)

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18 pages, 4449 KiB

Open AccessArticle

Global Leaf Chlorophyll Content Dataset (GLCC) from 2003–2012 to 2018–2020 Derived from MERIS and OLCI Satellite Data: Algorithm and Validation

by Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen and Qi Sun

Remote Sens. 2023, 15(3), 700; https://doi.org/10.3390/rs15030700 - 25 Jan 2023

Cited by 4 | Viewed by 1958

Abstract

Leaf chlorophyll content (LCC) is a prominent plant physiological trait and a proxy for leaf photosynthetic capacity. The acquisition of LCC data over large spatial and temporal scales facilitates vegetation growth monitoring and terrestrial carbon cycle modeling. In this study, a global 500 m LCC weekly dataset (GLCC) was produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physical radiative transfer modeling approach that considers the influence of canopy structure and soil background. Firstly, five look-up-tables (LUTs) were generated using PROSPECT-D+4-Scale and PROSAIL-D models for woody and non-woody plants. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. The one LUT applicable to non-woody vegetation type includes 25 sub-LUTs corresponding to five kinds of canopy structures and five kinds of soil backgrounds. The final retrieval was considered the aggregation of the LCC inversion results of all sub-LUTs for each plant function type (PFT). Then, the GLCC dataset was generated and validated using field measurements, yielding an overall accuracy of R² = 0.41 and RMSE = 8.94 μg cm⁻². Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset. OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003–2012 to 2018–2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity to analyze variations in vegetation dynamics. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Production)

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20 pages, 6115 KiB

Open AccessArticle

How Well Can Matching High Spatial Resolution Landsat Data with Flux Tower Footprints Improve Estimates of Vegetation Gross Primary Production

by Xiaojuan Huang, Shangrong Lin, Xiangqian Li, Mingguo Ma, Chaoyang Wu and Wenping Yuan

Remote Sens. 2022, 14(23), 6062; https://doi.org/10.3390/rs14236062 - 29 Nov 2022

Cited by 2 | Viewed by 2049

Abstract

Eddy-covariance (EC) measurements are widely used to optimize the terrestrial vegetation gross primary productivity (GPP) model because they provide standardized and high-quality flux data within their footprint areas. However, the extent of flux data taken from a tower site within the EC footprint, represented by the satellite-based grid cell between Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), and the performance of the model derived from the Normalized Difference Vegetation Index (NDVI) within the EC footprint at different spatial resolutions (e.g., Landsat and MODIS) remain unclear. Here, we first calculated the Landsat-footprint NDVI and MODIS-footprint NDVI and assessed their spatial representativeness at 78 FLUXNET sites at 30 m and 500 m scale, respectively. We then optimized the parameters of the revised Eddy Covariance-Light Use Efficiency (EC-LUE) model using NDVI within the EC-tower footprints that were calculated from the Landsat and MODIS sensor. Finally, we evaluated the performance of the optimized model at 30 m and 500 m scale. Our results showed that matching Landsat data with the flux tower footprint was able to improve the performance of the revised EC-LUE model by 18% for savannas, 14% for croplands, 9% for wetlands. The outperformance of the Landsat-footprint NDVI in driving model relied on the spatial heterogeneity of the flux sites. Our study assessed the advantages of remote sensing data with high spatial resolution in simulating GPP, especially for areas with high heterogeneity of landscapes. This could facilitate a more accurate estimation of global ecosystem carbon sink and a better understanding of plant productivity and carbon climate feedbacks. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Production)

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18 pages, 4278 KiB

Open AccessArticle

Exploring the Individualized Effect of Climatic Drivers on MODIS Net Primary Productivity through an Explainable Machine Learning Framework

by Luyi Li, Zhenzhong Zeng, Guo Zhang, Kai Duan, Bingjun Liu and Xitian Cai

Remote Sens. 2022, 14(17), 4401; https://doi.org/10.3390/rs14174401 - 4 Sep 2022

Cited by 8 | Viewed by 4571

Abstract

Along with the development of remote sensing technology, the spatial–temporal variability of vegetation productivity has been well observed. However, the drivers controlling the variation in vegetation under various climate gradients remain poorly understood. Identifying and quantifying the independent effects of driving factors on a natural process is challenging. In this study, we adopted a potent machine learning (ML) model and an ML interpretation technique with high fidelity to disentangle the effects of climatic variables on the long-term averaged net primary productivity (NPP) across the Amazon rainforests. Specifically, the eXtreme Gradient Boosting (XGBoost) model was employed to model the Moderate-resolution Imaging Spectroradiometer (MODIS) NPP data, and the Shapley addictive explanation (SHAP) method was introduced to account for nonlinear relationships between variables identified by the model. Results showed that the dominant driver of NPP across the Amazon forests varied in different regions, with temperature dominating the most considerable portion of the ecoregion with a high importance score. In addition, light augmentation, increased CO₂ concentration, and decreased precipitation positively contributed to Amazonia NPP. The wind speed for most vegetated areas was under the optimum, which benefits NPP, while sustained high wind speed would bring substantial NPP loss. We also found a non-monotonic response of Amazonia NPP to VPD and attributed this relationship to the moisture load in Amazon forests. Our application of the explainable machine learning framework to identify the underlying physical mechanism behind NPP could be a reference for identifying relationships between components in natural processes. Full article

(This article belongs to the Special Issue Remote Sensing of Primary Production)

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