Remote Sensing Satellite Data Research Articles

Monitoring of Low Chl-a Concentration in Hulun Lake Based on Fusion of Remote Sensing Satellite and Ground Observation Data

China’s northern Hulun Lake is a significant body of water internationally. The issue of eutrophication has gained prominence in recent years. The achievement of precise chlorophyll-a (Chl-a) monitoring is crucial for safeguarding Hulun Lake’s ecosystem. The machine learning-based remote sensing inversion method has been shown to be effective in capturing the intricate relationship between independent and dependent variables; however, it lacks a priori knowledge and is limited by the quality of remote sensing data sources. The relationship between independent and dependent variables can be more accurately simulated with the use of suitable auxiliary variables. Therefore, three machine learning models—random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost)—were established in this study using meteorological observation parameters as auxiliary variables combined with Sentinel-2 satellite image remote sensing band combinations as independent variables and measured Chl-a data as dependent variables. The estimation effects before and after the fusion of meteorological ground observation data were compared, and the best model was used to estimate the spatial–temporal variation trend of Chl-a in the regional water body. The results show that (1) the addition of meteorological parameters as auxiliary variables improved the precision of the three machine models; the decision coefficient (R2) rose by 7.25%, 5.71%, and 7.20%, respectively, to 0.76, 0.66, and 0.73. (2) The concentration of Chl-a in the lake region was projected from June to October 2019 to October 2021 using the RF optimal estimating model of meteorological fusion. The northeast, southwest, and south of the lake were where the comparatively high concentration values of Chl-a were located, whereas the lake’s center had a generally low concentration of the substance. Chromatically, Chl-a typically peaked in August after initially increasing and then declining. (3) The three rivers that feed into the river have varying levels of water pollution, with chemical oxygen demand (COD) and total nitrogen (TN) pollution being the most severe. This is what primarily caused the higher levels of Chl-a in the northeast, southwest, and south. This study is crucial for the preservation and restoration of Hulun Lake’s natural ecosystem and offers some technical support for the monitoring of the lake’s concentration of Chl-a.

Research on Monitoring and Application of Ecological Restoration Engineering in Open Pit Backfilling Mines Based on Satellite Remote Sensing Data

Abstract. The restoration and management of mining ecological environment is an important component of China's ecological civilization construction. The implementation of restoration projects is to avoid the serious impact caused by the continuous deterioration of mining ecological structure, and is a key way to maintain regional ecological security and energy and mineral security. Therefore, efficient and rapid monitoring and evaluation of the effectiveness of mining ecological restoration is an essential and important link. With the rapid development of remote sensing technology, remote sensing monitoring has become an important means to objectively, quickly, and accurately obtaining changes in the mining environment. It can provide timely and long-term monitoring of mining and backfilling conditions in mining areas. This article uses the multi temporal stereo images of the Resource 3 satellite over four years to monitor the mining and backfilling situation of the Zhungeer open-pit coal mine area in Inner Mongolia. By reconstructing the Digital Surface Model (DSM) of the open-pit mining area, the changes and thresholds of the multi temporal DSM data are statistically analyzed to extract the mining and backfilling areas, and the earthwork volume is calculated. The results showed that from 2013 to 2016, the main large-scale mining faces in the study area of Zhungeer open-pit mining area had a total mining operation of about 563.5 million cubic meters, and a total backfilling operation of about 604.29 million cubic meters; During the period from 2016 to 2018, the main large-scale mining faces in the region had a total of approximately 721.71 million cubic meters of mining operations and a total of approximately 805.42 million cubic meters of backfilling operations. The overall operational intensity increased from 2016 to 2018. The research results show that based on satellite image data, it is convenient and efficient to obtain DSM and corresponding changes of multiple time periods in mining areas. Monitoring results can provide important support for regional environmental governance and protection, as well as safety production in mining areas.

High rainfall event identification using remote sensing satellite data integrated with a hybrid deep learning framework

High rainfall event identification using remote sensing satellite data integrated with a hybrid deep learning framework

Remotely-sensed phenoclusters of Wisconsin’s forests, shrublands, and grasslands for biodiversity applications

Heterogeneous vegetation supports higher species richness than homogenous vegetation, which is why efficiently identifying heterogenous vegetation can be useful for biodiversity conservation. Satellite remote-sensing data provide an opportunity to generate vegetation heterogeneity metrics and to explore the phenology of vegetation patterns. Phenoclusters are vegetation types with similar phenological characteristics, and valuable for capturing vegetation habitat heterogeneity patterns. Our goal was to map phenoclusters for Wisconsin, USA, at 10-m spatial resolution based on land surface phenology metrics from EVI (Enhanced Vegetation Index) Sentinel-2 data. We characterized each phenocluster based on landcover composition and structure, phenology timing, and environmental factors, and compared them to bird species richness. We also calculated the diversity of phenoclusters at multiple spatial extents. We identified 14 phenoclusters in Wisconsin, each with distinct landcover composition and structure, and unique phenological characteristics. Our remotely-sensed phenoclusters effectively captured environmental gradients, with elevation and temperature emerging as the most important driving variables. Furthermore, the phenoclusters successfully captured bird biodiversity patterns, especially richness of forest and grassland specialist. Our results identified phenological patterns among Wisconsin’s forests, shrublands, and grasslands, capturing phenological timing both among and within the same tree species. Phenoclusters are a valuable tool for capturing vegetation habitat heterogeneity, phenology diversity and biodiversity patterns, as well as climate change effects.

Tracking Carbon Dioxide with Lagrangian Transport Simulations: Case Study of Canadian Forest Fires in May 2021

The large amounts of greenhouse gases, such as carbon dioxide, produced by severe forest fires not only seriously affect the ecosystems in the area where the fires occur but also cause a greenhouse effect that has a profound impact on the natural environment in other parts of the world. Numerical simulations of greenhouse gas transport processes are often affected by uncertainties in the location and timing of the emission sources and local meteorological conditions, and it is difficult to obtain accurate and credible predictions by combining remote sensing satellite data with given meteorological forecasts or reanalyses. To study the regional transport processes and impacts of greenhouse gases produced by sudden large-scale forest fires, this study applies the Lagrangian particle dispersion model Massive-Parallel Trajectory Calculations (MPTRAC) to conduct forward simulations of the CO2 transport process of greenhouse gases emitted from forest fires in the central region of Saskatchewan, Canada, during the period of 17 May to 25 May 2021. The simulation results are validated with the Orbiting Carbon Observatory-2 Goddard Earth Observing System (OCO-2 GEOS) Level 3 daily gridded CO2 product over the study area. In order to leverage the high computational costs of the numerical simulations of the model, we implement the forward simulations on the Tianhe-2 supercomputer platform and the JUWELS HPC system, which greatly improves the computational efficiency through parallel computation and makes near-real-time predictions of atmospheric transport processes feasible.

The Inversion of Three-Dimensional Ocean Temperature and Salinity Fields for the Assimilation of Satellite Observations

The lack of dimensionality of ocean observations makes it difficult to utilize multi-scale data assimilation to correct model errors with limited observations. Since satellite observations can provide high-resolution and time-continuous sea surface information, this study utilizes sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) anomalies to invert the vertical temperature and salinity fields and thus realize multi-scale data assimilation in the three-dimensional space. We propose a temperature and salinity inversion network (TSI-Net) for reconstructing the mapping of the sea surface’s spatial distribution features to vertical structural features to obtain pseudo-observed fields. In this study, measured satellite remote-sensing data and temperature and salinity profiles are used to correct the model errors in the waters around the China Sea. The sensitivity analysis shows that the multi-component inversion can better fit the temperature field relationship, with a correlation coefficient of about 0.87. The results of the assimilation experiments show that the analytical field obtained by assimilating the pseudo-observed field is more consistent with the target field in terms of the spatial distribution characteristics.

Crop Type Classification using Sentinel 2A-Derived Normalized Difference Red Edge Index (NDRE) and Machine Learning Approach

Abstract Satellite remote sensing (RS) enables the extraction of vital information on land cover and crop type. Land cover and crop type classification using RS data and machine learning (ML) techniques have garnered considerable attention in the scientific community. This study aimed to enhance the field of remote sensing research using high-resolution satellite imagery and the ML approach. To reach this goal, machine learning techniques were used to demonstrate whether they could accurately classify different types of crops in farming areas using the Normalized Difference Red Edge Index (NDRE) from Sentinel 2A. Five ML classifiers, namely Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) were implemented using Python programming on Google Colaboratory. The target crop classes included cereals, fallow, forage, fruits, grassland-pasture, legumes, maize, sugar beet, onion-garlic, sunflower, and watermelon-melon. The classification models exhibited strong performance, as evidenced by their robust overall accuracy (OA). The RF model outperformed, with an OA rate of 95% and a Kappa score of 92%. It was followed by DT, KNN, SVM, and MLP with an OA of 88%, 87%, 85%, and 82%, respectively. This study confirmed the potential enhancement of the application of high spatial resolution satellite RS data and ML for crop type classification, especially in regions that have received less attention in previous studies.

Multi-scale remote sensing retrieval model for land surface emissivity and its impact on land surface temperature estimation

ABSTRACT As a fundamental parameter of the surface radiation process, land surface emissivity (LSE) is the most direct factor affecting the land surface temperature (LST) retrieval accuracy. Based on the measured data from 2017 to 2023, the characteristics of LSE changes were analysed, different timescale LSE retrieval models were developed, and the impact of LSE on pixel scale observational LST acquisition was evaluated by using unmanned aerial vehicle (UAV) remote sensing images. The results show that with changes in atmospheric environmental conditions, the LSE changes are relatively less pronounced at 3.99 µm and 4.02 µm in the mid-infrared band and at 10.8 µm and 11.8 µm in the thermal infrared band than in the other bands. At the timescale above day, the LSE is significant correlation with the normalized differential vegetation index (NDVI). Vegetation is the main parameter that dominates the LSE change under heterogeneous underlying surfaces. On a daily timescale, LSE exhibits typical diurnal variation characteristics, and the amplitude of diurnal variation is influenced mainly by shallow soil water and heat conditions. The developed LSE model based on Fengyun-4A (FY-4A) satellite remote-sensing data can effectively reproduce the daily variation characteristics of LST and avoid the ‘jumping’ phenomenon that may occur in the NDVI threshold method. The root-mean-square error (RMSE) between the retrieved LST and in situ data is reduced to 3.78°C, and the retrieval accuracy is better than that of the published LST product. When using UAVs to obtain pixel-scale LSTs, dry bare soil is more sensitive to LSE. When the LSE increases from 0.95 to 1.0, the average LST obtained by the UAV deviates by approximately 1.0°C. This study preliminarily provides some spatiotemporal variation patterns of LSE and lays a foundation for the later-modified LST retrieval algorithms.

Medium-term exposure to size-fractioned particulate matter and asthma exacerbations in China: A longitudinal study of asthmatics with poor medication adherence

BackgroundStudies have shown that short- and long-term exposure to particulate matter (PM) can increase the risk of asthma morbidity and mortality. However, the effect of medium-term exposure remains unknown. We aim to examine the effect of medium-term exposure to size-fractioned PM on asthma exacerbations among asthmatics with poor medication adherence. MethodsWe conducted a longitudinal study in China based on the National Mobile Asthma Management System Project that specifically and routinely followed asthma exacerbations in asthmatics with poor medication adherence from April 2017 to May 2019. High-resolution satellite remote-sensing data were used to estimate each participant's medium-term exposure (on average 90 days) to size-fractioned PM (PM1, PM2.5, and PM10) based on the residential address and the date of the follow-up when asthma exacerbations (e.g., hospitalizations and emergency room visits) occurred or the end of the follow-up. The Cox proportional hazards model was employed to examine the hazard ratio of asthma exacerbations associated with each PM after controlling for sex, age, BMI, education level, geographic region, and temperature. ResultsModelling results revealed nonlinear exposure-response associations of asthma exacerbations with medium-term exposure to PM1, PM2.5, and PM10. Specifically, for emergency room visits, we found an increased hazard ratio for PM1 above 22.8 µg/m3 (1.060, 95 % CI: 1.025–1.096, per 1 µg/m3 increase), PM2.5 above 38.2 µg/m3 (1.032, 95 % CI: 1.010–1.054), and PM10 above 78.6 µg/m3 (1.019, 95 % CI: 1.006–1.032). For hospitalizations, we also found an increased hazard ratio for PM1 above 20.3 µg/m3 (1.055, 95 % CI: 1.001–1.111) and PM2.5 above 39.2 µg/m3 (1.038, 95 % CI: 1.003–1.074). Furthermore, the effects of PM were greater for a longer exposure window (90–180 days) and among participants with a high BMI. ConclusionThis study suggests that medium-term exposure to PM is associated with an increased risk of asthma exacerbations in asthmatics with poor medication adherence, with a higher risk from smaller PM.

Soil Erosion Hazard Zonation in Bandu Sub-Watershed, India

Abstract Soil hazard zonation in watershed is quite significant to take the necessary actions for soil conservation. The present study attempts to identify soil hazard zones for proper soil conservation and prepare a treatment plan for the Bandu sub-watershed of India through morphometric analysis techniques by giving rank based on priority. The result of the prioritization of eighteen micro-watersheds is entirely satisfactory due to the use of fourteen morphometric parameters. The study quantifies the potential soil loss and identifies the soil eroded zone of the sub-watershed using the universal soil loss equation method and the entire region is categorized into three soil hazard zones with varying degrees. Indian Remote Sensing Satellite data have been used to conduct the whole study. The micro-watershed prioritization has been estimated by applying the composite morphometric value. The micro-watershed having 5.625 composite value ranks first for prioritization (most vulnerable with maximum soil erosion) and having 15.875 composite value ranks last for prioritization (least vulnerable with minimum soil erosion). The result also shows that the soil loss ranges from 0-30 tonne/hectare/year with an average soil loss of 0-10 tonne/hectare/year in maximum areas of the sub-watershed. The soil loss map shows that along the Bandu and in some agricultural fields, the central part of the region is susceptible to soil erosion. The scientific approach of this research could be more effective in maintaining sustainable rural planning. The study can be used as a reference work for determining soil hazard zones in any tropical watershed with high soil loss risk.

Ecosystem Service Value Assessment of the Yellow River Delta Based on Satellite Remote Sensing Data

The Yellow River Delta (YRD) stands as a globally significant wetland, playing a pivotal role in sustaining regional ecosystem stability and offering crucial ecosystem services to humanity. However, anthropogenic activities, particularly resource development, unavoidably disrupt the ecosystem, leading to the degradation of these vital services. Utilizing satellite remote sensing data, the InVEST model, and energy analysis, this study introduces the concept of ‘emergy’ as an ‘intermediate variable’ to investigate the spatiotemporal changes in the ecosystem service value of the YRD. Five distinct types of ecosystem services are selected for quantitative assessment and analysis of the YRD’s spatiotemporal evolution from 1990 to 2020. Results indicate a 63.7% decline in the total value of ecosystem services from 1990 to 2010, followed by a 16.5% increase from 2010 to 2020. The study also unveils spatial shifts in high- and low-value areas of ecosystem services and attributes these changes to rapid urbanization and alterations in land use and cover. The assessment of ecosystem service values concretizes the intangible ecosystem service functions of natural resources. This lays the foundation for establishing a mechanism that combines positive incentives and reverse pressure to achieve the economic valuation of ecosystem service.

An inter-comparison of approaches and frameworks to quantify irrigation from satellite data

Abstract. This study provides the first inter-comparison of different state-of-the-art approaches and frameworks that share a commonality in their utilization of satellite remote-sensing data to quantify irrigation at a regional scale. The compared approaches vary in their reliance on either soil moisture or evapotranspiration data or their joint utilization of both. The two compared frameworks either extract irrigation information from residuals between satellite observations and rainfed hydrological models in a baseline framework or use soil water balance modeling in a soil-moisture-based inversion framework. The inter-comparison is conducted over the lower Ebro catchment in Spain where observed irrigation amounts are available for benchmarking. Our results showed that within the baseline framework, the joint approach using both soil moisture and evapotranspiration (ET) remote-sensing data only differed by +37 mm from the irrigation benchmark (922 mm) during the main irrigation season over 2 years and by +47 and −208 mm for approaches relying solely on soil moisture and ET, respectively. A comparison of the different frameworks showed that the main advantage of the more complex baseline framework was the consistency between soil moisture and ET components within the hydrological model, which made it unlikely that either one ended up representing all irrigation water use. However, the simplicity of the soil-moisture-based inversion framework, coupled with its direct conversion of soil moisture changes into actual water volumes, effectively addresses the key challenges inherent in the baseline framework, which are associated with uncertainties related to an unknown remote-sensing observation depth and the static depth of the soil layers in a conceptual model. The performance of the baseline framework came closest to the irrigation benchmark and was able to account for the precipitation input, which resulted in more plausible temporal distributions of irrigation than what was expected from the benchmark observations.

Monitoring Maritime Ship Characteristics Using Satellite Remote Sensing Data from Different Sensors

Monitoring Maritime Ship Characteristics Using Satellite Remote Sensing Data from Different Sensors

Analysis of Atmospheric Elements in Near Space Based on Meteorological-Rocket Soundings over the East China Sea

As an important means of in situ detection in near space, meteorological rockets can provide a high-precision distribution analysis of atmospheric elements. However, there are currently few studies on the principles of meteorological-rocket detection and the application of rocket-sounding data. The purpose of this paper is to fill this gap by providing a detailed introduction to the detection principle of a meteorological rocket launched in the East China Sea in November 2022. Moreover, empirical models, satellite data, and reanalysis data were selected for comparison and verification with the rocket-sounding data. Furthermore, the accuracy of these widely used datasets was studied based on the rocket-sounding data in the near space over the East China Sea. Additionally, gravity-wave power–frequency spectra were extracted using the maximum entropy method from both the rocket-sounding data and the remote-sensing data. Furthermore, the relationship between gravity waves and Kelvin–Helmholtz instability (KHI) was investigated by analyzing the gravity-wave energy and the Richardson number. The research findings indicate that among the remote-sensing data describing the atmospheric environment over the launch site, the COSMIC occultation data is more accurate compared with the SABER data. The wind-field distribution derived from rocket detection is consistent with the Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis data, while also providing a more detailed description of the wind field. The main wavelengths of gravity waves extracted from rocket-sounding data are consistently smaller than those obtained from satellite remote-sensing data, indicating that rocket sounding is capable of capturing more intricate structures of gravity waves. The good correspondence between the peaks of gravity-wave energy and the regions where KHI occurs indicates that there is a strong interaction between gravity waves and KHI in the middle atmosphere.

Optimization of Multi-Subband Parallel and Signal Reconstruction for Remote Sensing Satellite Data Transmission

Optimization of Multi-Subband Parallel and Signal Reconstruction for Remote Sensing Satellite Data Transmission

A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data

The accurate mapping of crop types is crucial for ensuring food security. Remote Sensing (RS) satellite data have emerged as a promising tool in this field, offering broad spatial coverage and high temporal frequency. However, there is still a growing need for accurate crop type classification methods using RS data due to the high intra- and inter-class variability of crops. In this vein, the current study proposed a novel Parallel-Cascaded ensemble structure (Pa-PCA-Ca) with seven target classes in Google Earth Engine (GEE). The Pa section consisted of five parallel branches, each generating Probability Maps (PMs) for different target classes using multi-temporal Sentinel-1/2 and Landsat-8/9 satellite images, along with Machine Learning (ML) models. The PMs exhibited high correlation within each target class, necessitating the use of the most relevant information to reduce the input dimensionality in the Ca part. Thereby, Principal Component Analysis (PCA) was employed to extract the top uncorrelated components. These components were then utilized in the Ca structure, and the final classification was performed using another ML model referred to as the Meta-model. The Pa-PCA-Ca model was evaluated using in-situ data collected from extensive field surveys in the northwest part of Iran. The results demonstrated the superior performance of the proposed structure, achieving an Overall Accuracy (OA) of 96.25% and a Kappa coefficient of 0.955. The incorporation of PCA led to an OA improvement of over 6%. Furthermore, the proposed model significantly outperformed conventional classification approaches, which simply stack RS data sources and feed them to a single ML model, resulting in a 10% increase in OA.

Characterization of the Fels Landslide (Alaska) Using Combined Terrestrial, Aerial, and Satellite Remote Sensing Data

The characterization of landslides located in remote areas poses significant challenges due to the costs of reaching the sites and the lack of reliable subsurface data to constrain geological interpretations. In this paper, the advantages of combining field and remote sensing techniques to investigate the deformation and stability of rock slopes are demonstrated. The characterization of the Fels landslide, a large, slowly deforming rock slope in central Alaska, is described. Historical aerial imagery is used to highlight the relationship between glacier retreat and developing instability. Airborne laser scanning (ALS) and Structure-from-Motion (SfM) datasets are used to investigate the structural geological setting of the landslide, revealing a good agreement between structural discontinuities at the outcrop and slope scales. The magnitude, plunge, and direction of slope surface displacements and their changes over time are studied using a multi-temporal synthetic aperture radar speckle-tracking (SAR ST) dataset. The analyses show an increase in displacement rates (i.e., an acceleration of the movement) between 2010 and 2020. Significant spatial variations of displacement direction and plunge are noted and correlated with the morphology of the failure surface reconstructed using the vector inclination method (VIM). In particular, steeper displacement vectors were reconstructed in the upper slope, compared to the central part, thus suggesting a change in basal surface morphology, which is largely controlled by rock mass foliation. Through this analytical approach, the Fels landslide is shown to be a slow-moving, compound rockslide, the displacement of which is controlled by structural geological features and promoted by glacier retreat.

Association between exposure to outdoor artificial light at night and the risk of preterm birth.

This study aimed to investigate the association between outdoor artificial light at night (ALAN) exposure during pregnancy and the risk of preterm birth (PTB). A retrospective case-control study was conducted, and data were collected from pregnant women residing in Beijing, China. The level of ALAN exposure during pregnancy was estimated using remote sensing satellite data. Propensity score matching was utilized to match the control group. Logistic and multivariate linear regression were used to analyze the association between ALAN and the risk of PTB. The odds ratio (OR) and partial regression coefficient (β) with 95% confidence interval (CI) were utilized to assess the association. A total of 2,850 pregnant women were enrolled in this study. ALAN (nW/cm2/sr) exposure was higher in the PTB group than in the control group during first trimester (mean ± standard deviation: 25.30 ± 17.91 vs. 17.56 ± 14.74, p < 0.001) and second trimester (27.07 ± 18.10 vs. 21.93 ± 16.08, p < 0.001). A negative association was found between ALAN exposure and gestation day in the first (β = -0.151, 95%CI: -0.217 to -0.085, p < 0.001) and second trimesters (β = -0.077, 95%CI: -0.139 to -0.015, p = 0.015). ALAN was identified as a risk factor for PTB during the first trimester (OR = 1.032, 95%CI: 1.025-1.040, p < 0.001) and the second trimester (OR = 1.018, 95%CI: 1.011-1.025, p < 0.001), while no significant association was observed in the third trimester. Our study suggesting that exposure to outdoor ALAN, especially during first and second trimester, was associated with the risk of PTB. These findings highlight the potential impact of ALAN on pregnancy health and offer new insights into the risk of PTB.

Earth observation satellite data-based assessment of wetland dynamics in the Kashmir Himalaya.

Earth observation (EO) technology offers enormous opportunities to assess the magnitude and patterns of spatial variability in wetlands over time. This study aims to assessthe spatial and temporalchanges in the wetlands of the Kashmir valley using multiple remote sensing satellite data products, Geographic Information System (GIS), and field observations. Moreover, role of major factors operating at different time scales including regional geology, climate, and human activities in driving the wetland change is presented. The dynamics of the wetlands are illustrated in the occurrence, seasonality, and recurrence of surface water, land cover transitions and loss patterns particularly for the period from 1984 to 2021. Constituting about 3% (495 Km2) of the total area, substantial and variable patterns of seasonal and annual changes are exhibited by the wetlands. The main transitions of the water surface reveal that 2% of the area has changed from permanent to seasonal; 8% is lost; 15% is new seasonal; 0.12% is permanently lost; and 0.3% is new permanent. About 22% of the area reveals increase in the intensity of water surface occurrence, whereas 44% shows no change, and 34% exhibits decrease. Bathymetric analysis suggests that the average depth of the wetlands ranges between 0.6 and 16.6m. In general, alpine wetlands are relatively deeper and mostly static in their structure whereas those in the floodplain are shallow, fragmented, and showing signs of depletion during the assessment period. The results of this assessment will inform the policy on conservation and sustainability of wetlands in the Kashmir Himalaya.

A novel approach for retrieving GPP of evergreen forest regions of India using random forest regression

Gross Primary Productivity (GPP) is a crucial variable of global carbon cycle for determining the ecosystem’s health. Various methods are devised to quantify GPP and upscale it in both time and space. The most common methods are physical model and eddy covariance-based estimation, which are very restricted to surrounding area of study, only. The alternative methods are empirical (e.g., LUE, CASA, SCARF, and MODIS) and Machine learning (ML) models that employ remote sensing satellite data and geographical factors. However, for using ML models, ground-based measurements of GPP is a very important factor, which is not available in most places. We propose an alternative and effective way of estimating the GPP using the ML model and data from various flux sites around the globe for a particular plant functional type (PFT). In the present study, RF is used as ML model, which is trained on global GPP data from evergreen forest and implemented in Indian region. The key findings indicated that ML-based GPP is highly accurate and hence, we generated 20 years of time series GPP dataset (2001–2020). We validated with ground-based flux tower observations during 2016–2018 for three sites (very limited datasets) in India and compared them with MODIS GPP. The coefficient of determination (R2) value of the ML-based model was 0.84 with root mean square error (RMSE) of 1.45 gC m−2 Day−1 and mean absolute error (MAE) of 0.838 gC m−2 Day−1. The proposed approach is highly accurate and far better than the MODIS-based GPP. Therefore, it can be further extended to other forest types for a holistic assessment of the carbon cycle of a region.