Remote Sensing-based Model Research Articles

Hydrodynamics- and remote sensing-based model for estimating the effects of cohesive sediment transport on lagoon siltation in Southwestern Taiwan

This study investigates localized siltation in the Cigu Lagoon, Southwestern Taiwan, using an integrated approach of hydrodynamic modeling and remote sensing. In regions where in situ data is scarce, remote sensing provides critical complementary data inputs for our sediment model. We employed a multilayered mud sediment model, incorporating initial suspended sediment concentration (SSC) data derived from Landsat imagery, to identify the morphological changes taking place in the lagoon. Over the past few decades, sandbar migration and sedimentation have led to a significant shrinkage of the Cigu Lagoon, which is now at risk of disappearing if a full understanding of the underlying factors is not reached. The loss of the lagoon would have severe implications for the local ecosystem and habitat, as well as for the fishermen who rely on the lagoon for their livelihoods. Our results showed that sedimentation in the Cigu Lagoon is a compounded consequence of the action of the tidal cycle and of waves. Throughout the simulation period, the SSC in the Cigu Lagoon ranged from 1 g m −3 to 50 g m −3. The annual siltation rate of the lagoon due to cohesive sediment transport was 0.82 cm. The simulation results showed that the siltation mainly occurred during the winter, with the dominant factor being the frequent strong waves at this time of year. This study suggests that a management plan for the Cigu Lagoon must be devised and implemented, and that remote sensing and hydrodynamic modeling are valuable tools in communicating about the complex processes involved in a sedimentary system and informing relevant decision-making at the stage of management.

Predicting Urban Trees’ Functional Trait Responses to Heat Using Reflectance Spectroscopy

Plant traits are often measured in the field or laboratory to characterize stress responses. However, direct measurements are not always cost effective for broader sampling efforts, whereas indirect approaches such as reflectance spectroscopy could offer efficient and scalable alternatives. Here, we used field spectroscopy to assess whether (1) existing vegetation indices could predict leaf trait responses to heat stress, or if (2) partial least squares regression (PLSR) spectral models could quantify these trait responses. On several warm, sunny days, we measured leaf trait responses indicative of photosynthetic mechanisms, plant water status, and morphology, including electron transport rate (ETR), photochemical quenching (qP), leaf water potential (Ψleaf), and specific leaf area (SLA) in 51 urban trees from nine species. Concurrent measures of hyperspectral leaf reflectance from the same individuals were used to calculate vegetation indices for correlation with trait responses. We found that vegetation indices predicted only SLA robustly (R2 = 0.55), while PLSR predicted all leaf trait responses of interest with modest success (R2 = 0.36 to 0.58). Using spectral band subsets corresponding to commercially available drone-mounted hyperspectral cameras, as well as those selected for use in common multispectral satellite missions, we were able to estimate ETR, qP, and SLA with reasonable accuracy, highlighting the potential for large-scale prediction of these parameters. Overall, reflectance spectroscopy and PLSR can identify wavelengths and wavelength ranges that are important for remote sensing-based modeling of important functional trait responses of trees to heat stress over broad ranges.

A machine learning model for estimating the temperature of small rivers using satellite-based spatial data

The influence of anthropogenic activity and land cover alteration on stream temperatures has major ecological implications, such as limiting fish survival. While these ecological impacts have been extensively studied at varying spatial scales for major rivers, our understanding of the range and complexity of this relationship across climates and geographies for smaller rivers (less than ∼60 m wide) remains limited. This is in part because although such rivers comprise a vast majority (∼97%) of total stream length, most smaller rivers lack routine temperature observations despite extensive gage networks. While existing gage networks in many regions are sufficient to support satellite remote sensing-based modeling of small stream temperatures, most large regions including the contiguous United States do not have modeled data products at high spatial and temporal resolution for small rivers.In this study, we developed a statistical model, TempEst (“temperature estimation”), to estimate monthly average water temperatures for smaller rivers and evaluated the model with rivers ranging from 3 m to 1000 m (1 km) in width, using land cover, topography, and satellite-based land surface temperature. The rivers chosen for development of the model are representative of a range of urban and rural land uses and cover eight US Environmental Protection Agency Level I ecoregions. The model was calibrated and validated using observed stream temperatures from the United States Geological Survey. TempEst is able to predict monthly average temperature for streams of any size with an overall median RMSE of about 1.5 °C and bias of 0%. Model accuracy has a consistent trend with training gauge network density, with median RMSE increasing to about 2.0 °C in more sparsely-gauged regions. As a simple demonstration, we used TempEst to estimate thermal suitability conditions for cutthroat trout in Rocky Mountain streams in the United States. The developed model, available as open-source R (model) and Google Earth Engine (data retrieval) scripts, will facilitate the study of stream temperature behaviors at higher resolutions than previously available across the contiguous United States and can be easily adapted to support global river systems.

Estimating distributed autumn irrigation water use in a large irrigation district by combining machine learning with water balance models

Autumn irrigation (AI) after crop harvesting is vital for agricultural production in cold arid regions, typically requiring more water than regular irrigation during crop growth phases. Understanding distributed AI water use (AIWU) is key to optimizing irrigation strategies and refining agro-hydrological simulations. However, field-scale monitoring of irrigation water use is challenging, particularly in large districts, and quantification methods for distributed AIWU have seldom been investigated. Given this, we proposed a remote sensing-based model to estimate AIWU from water balance analysis, with key components derived through machine learning algorithms. Specifically, to address the unique challenge of short water ponding duration during AI, we selected MODIS as the primary data source, supplemented with Sentinel-2 data to enrich field-level details. A Sentinel-2 empirical bathymetry model, informed by in-situ measurements, served as a foundation for constructing a MODIS bathymetry model using random forest. This MODIS bathymetry model was essential for determining water ponding depths, which allowed for accurate estimation of infiltration rates when applied to the MODIS-based water index time series. By integrating these estimates into the water balance framework, we achieved a more precise estimate of AIWU. The proposed model was applied to Hetao Irrigation District (HID), the largest irrigation district in the arid region of China. The estimated steady infiltration rate and canal efficiency align with similar studies. The results indicate that the average AIWU per unit AI area in the HID remains stable during 2010–2020 with substantial spatial variability at the pixel level, providing new insights into the local hydrological cycle. The AIWU of HID has been decreasing by 19 million m3 per year during this period mainly due to the reduction in AI area, with a more prominent trend from 2010 to 2017. Our study deepens insights into the spatial–temporal dynamics of AIWU in arid regions, contributing to better irrigation management.

Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant’s nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards.

Inside-out: Synergising leaf biochemical traits with stomatal-regulated water fluxes to enhance transpiration modelling during abiotic stress.

As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant-environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r2 = 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal-based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing-based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields.

A Comparison of Different Machine Learning Methods to Reconstruct Daily Evapotranspiration Time Series Estimated by Thermal–Infrared Remote Sensing

Remote sensing-based models usually have difficulty in generating spatio-temporally continuous terrestrial evapotranspiration (ET) due to cloud cover and model failures. To overcome this problem, machine learning methods have been widely used to reconstruct ET. Therefore, studies comparing and evaluating the accuracy and effectiveness of reconstruction among different machine learning methods at the basin scale are necessary. In this study, four popular machine learning methods, including deep forest (DF), deep neural network (DNN), random forest (RF) and extreme gradient boosting (XGB), were used to reconstruct the ET product, addressing gaps resulting from cloud cover and model failure. The ET reconstructed by the four methods was evaluated and compared for Heihe River Basin. The results showed that the four methods performed well for Heihe River Basin, but the RF method was particularly robust. It not only performed well compared with ground measurements (R = 0.73) but also demonstrated the ability to fully reconstruct gaps generated by the TSEB model across the entire basin. Validation based on ground measurements showed that the DNN and XGB models performed well (R > 0.70). However, some gaps still existed in the desert after reconstruction using the DNN and XGB models, especially for the XGB model. The DF model filled these gaps throughout the basin, but this model had lower consistency compared with ground measurements (R = 0.66) and yielded many low values. The results of this study suggest that machine learning methods have considerable potential in the reconstruction of ET at the basin scale.

Assessing the potential of remote sensing-based models to predict old-growth forests on large spatiotemporal scales

Old-growth forests provide a broad range of ecosystem services. However, due to poor knowledge of their spatiotemporal distribution, implementing conservation and restoration strategies is challenging. The goal of this study is to compare the predictive ability of socioecological factors and different sources of remotely sensed data that determine the spatiotemporal scales at which forest maturity attributes can be predicted.We evaluated various remotely sensed data that cover a broad range of spatial (from local to global) and temporal (from current to decades) extents, from Airborne Laser Scanning (ALS), aerial multispectral and stereo-imagery, Sentinel-1, Sentinel-2 and Landsat data. Using random forests, remotely sensed data were related to a forest maturity index available in 688 forest plots across four ranges of the French Alps. Each model also includes socioecological predictors related to topography, socioeconomy, pedology and climatology.We found that the different remotely sensed data provide information on the main forest structural characteristics as defined by ALS, except for Landsat, which has a too coarse resolution, and Sentinel-1, which responds differently to vegetation structure. The predictions were quite similar considering aerial remotely sensed data, on the one hand, and satellite remotely sensed data, on the other hand. Socioecological variables are the most important predictors compared to the remote sensing metrics.In conclusion, our results indicate that a wide range of remotely sensed data can be used to study old-growth forests beyond the use of ALS and despite different abilities to predict forest structure. Accounting for socioecological predictors is indispensable to avoid a significant loss of predictive accuracy. Remotely sensed data can allow for predictions to be made at different spatiotemporal resolutions and extents. This study paves the way to large-scale monitoring of forest maturity, as well as for retrospective analyses which will show to what extent predicted maturity change at different dates.

Assessment of Regression Models for Surface Water Quality Modeling via Remote Sensing of a Water Body in the Mexican Highlands

Remote sensing plays a crucial role in modeling surface water quality parameters (WQPs), which aids spatial and temporal variation assessment. However, existing models are often developed independently, leading to uncertainty regarding their applicability. This study focused on two primary objectives. First, it aimed to evaluate different models for chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) in a surface water body, the J. A. Alzate dam, in the Mexican highland region (R2 ≥ 0.78 and RMSE ≤ 16.1 mg/L). The models were estimated using multivariate regressions, with a focus on identifying dilution and dragging effects in inter-annual flow rate estimations, including runoff from precipitation and municipal discharges. Second, the study sought to analyze the potential scope of application for these models in other water bodies by comparing mean WQP values. Several models exhibited similarities, with minimal differences in mean values (ranging from −9.5 to 0.57 mg/L) for TSS, TN, and TP. These findings suggest that certain water bodies may be compatible enough to warrant the exploration of joint modeling in future research endeavors. By addressing these objectives, this research contributes to a better understanding of the suitability of remote sensing-based models for characterizing surface water quality, both within specific locations and across different water bodies.

Evaluation of transpiration in different almond production systems with two-source energy balance models from UAV thermal and multispectral imagery

AbstractA growing number of intensive irrigated production systems of the almond crop have been established in recent years. However, there is little information regarding the crop water requirements. Remote sensing-based models such as the two-source energy balance (TSEB) have proven to be reliable ways to accurately estimate actual crop evapotranspiration. However, few efforts have been made to validate the transpiration with sap flow measurements in woody row crops with different production systems and water status. In this study, the TSEB Priestley-Taylor (TSEB-PT) and contextual approach (TSEB-2T) models were assessed to estimate canopy transpiration. In addition, the effect of applying a basic clumping index for heterogeneous randomly placed clumped canopies and a rectangular hedgerow clumping index on the TSEB transpiration estimation was assessed. The TSEB inputs were obtained from high resolution multispectral and thermal imagery using an unmanned aerial vehicle. The leaf area index (LAI), stem water potential (Ψstem) and fractional intercepted photosynthetically active radiation (fIPAR) were also measured. Significant differences were observed in transpiration between production systems and irrigation treatments. The combined use of the TSEB-2T with the C&N-R transmittance model gave the best transpiration estimations for all production systems and irrigation treatments. The use of in situ PAR transmittance in the TSEB-2T model significantly improved the root mean squared error. Thus, the better agreement observed with the TSEB when using the C&N-R model and in situ PAR transmittance highlights the importance of improving radiative transfer models for shortwave canopy transmittance, especially in woody row crops.

Forestry feedbacks in Malezales’ degradation: Insights from past to plan future policy-driven forestry expansion over wetlands in Argentina

The degradation of wetlands is a worrying phenomenon, given its ecosystemic importance due to the provision of environmental services. In Argentina, the process of wetland degradation is fast and almost unknown due to the lack of control and monitoring. Malezales grasslands are wetland ecosystems, indeed poorly studied and endemic of Argentina's northeast. This study aimed to understand the impacts of policy-driven forestry expansion in wetlands, searching for tools and patterns to plan sustainable afforestation. Geographic information systems and remote sensing-based models were used to produce and analyze historical data of the forestry expansion and malezales degradation on inceptsols located in northeastern Corrientes, Argentina. The results showed that the region experienced extensive forestry expansion over the last 20 years. Pinus spp. plantations expanded on the most degraded malezales, which suggests some sustainability in the expansion process. However, the remaining malezales present current worrying levels of degradation. The intensification of the degradation process is imminent because livestock pressure on malezales has been intensified after the loss of areas due to the forestry increasing. We present a preliminary zoning of areas for conservation or forestry expansion in malezales. We found that 40.2% (47571 ha) of the current area of malezales could be used for forestry expansion, while 59.81% (about 70793 ha, 41.56% of the original ecosystem) is of priority for conservation or protection actions. A complete zoning of malezales is recommended, and policies to foment the correct management of malezales and forestry should therefore be implemented.

Improved quantification of cover crop biomass and ecosystem services through remote sensing-based model–data fusion

Cover crops have long been seen as an effective management practice to increase soil organic carbon (SOC) and reduce nitrogen (N) leaching. However, there are large uncertainties in quantifying these ecosystem services using either observation (e.g. field measurement, remote sensing data) or process-based modeling. In this study, we developed and implemented a model–data fusion (MDF) framework to improve the quantification of cover crop benefits in SOC accrual and N retention in central Illinois by integrating process-based modeling and remotely-sensed observations. Specifically, we first constrained and validated the process-based agroecosystem model, ecosys, using observations of cover crop aboveground biomass derived from satellite-based spectral signals, which is highly consistent with field measurements. Then, we compared the simulated cover crop benefits in SOC accrual and N leaching reduction with and without the constraints of remotely-sensed cover crop aboveground biomass. When benchmarked with remote sensing-based observations, the constrained simulations all show significant improvements in quantifying cover crop aboveground biomass C compared with the unconstrained ones, with R 2 increasing from 0.60 to 0.87, and root mean square error (RMSE) and absolute bias decreasing by 64% and 97%, respectively. On all study sites, the constrained simulations of aboveground biomass C and N at termination are 29% and 35% lower than the unconstrained ones on average. Correspondingly, the averages of simulated SOC accrual and N retention net benefits are 31% and 23% lower than the unconstrained simulations, respectively. Our results show that the MDF framework with remotely-sensed biomass constraints effectively reduced the uncertainties in cover crop biomass simulations, which further constrained the quantification of cover crop-induced ecosystem services in increasing SOC and reducing N leaching.

The effect of pixel heterogeneity on surface heat and water vapor flux estimated by the remote sensing-based model coupled with deep learning

Accurate quantification of sensible (H) and latent (LE) heat fluxes are desperately crucial for agricultural irrigation strategy, drought monitoring, water resource management, and climate change. Regional-scale H and LE were derived generally from satellite remote sensing data using relevant modeling methods. However, one of the challenges of modeling methods is to deal with sub-pixel heterogeneity, because the model usually assumes homogeneous conditions within a pixel. In this study, a hybrid model, which coupled deep neural network (DNN) and the remote sensing-based two-source energy balance (TSEB) model, was developed to estimate H and LE at multiple spatial resolutions to further quantify the influence of pixel size on the modeled fluxes. First, the DNN-based land surface temperature (LST) estimation model was developed and validated. LST at multiple spatial resolutions (i.e., 1, 5, 10, 30, 100, and 1000 m) were generated by the DNN method using multi-source satellite data including the GaoFen-2 (GF-2, a Chinese high-resolution optical remote-sensing satellite), Sentinel-2, and Landsat-8 images, respectively. Then, the hybrid DNN-TSEB model was used to estimate H and LE at multi-pixel sizes. Finally, modeled H and LE at 1 ∼ 1000 m were validated by eddy covariance (EC) and optical-microwave scintillometer (OMS) flux observation. Results revealed that the DNN-based LST model was a potential approach to generate LST at multiple pixel sizes. The hybrid DNN-TSEB model can produce more accurate H and LE when different land cover types were discriminated over heterogeneous farmland (e.g., the average bias for H and LE at a spatial resolution of 1 m were −9 and −6 W/m−2, respectively). Besides, the deviation of modeled fluxes from the observations increased as the spatial resolution became coarser, particularly the pixel size was greater than 30 m (average bias for H and LE were −34 and 30 W m−2, respectively). However, the magnitude of bias of fluxes estimates remained unchanged when the pixel sizes of satellite data were between 100 and 1000 m. The more important finding was that the hybrid model was a promising approach, which can obtain accurate estimates of H and LE at a high spatial resolution. In addition, the performance of the model based on GF-2 data was better than that based on Sentinel-2 and Landsat-8 data. The results suggest that pixel heterogeneity should be considered when estimating H and LE over heterogeneous landscapes with various surface types.

Towards monitoring stem growth phenology from space with high resolution satellite data

Radial stem growth is a key ecosystem process resulting in long-term carbon sequestration. Despite recognition of its importance to global carbon cycling, high uncertainties remain regarding how radial growth phenology (e.g., the onset, mid, and cessation of radial growth) will be affected by climate change. In this study, we evaluated to what extent high spatially (3 × 3 m) and temporally (up to daily) resolved satellite imagery from PlanetScope can be used to monitor stem growth phenology. For this, we made use of detailed stem growth phenological observations of six common European tree species measured by automated point dendrometers at 14 distinct sites across Switzerland between 2017 and 2021. These growth phenological observations were then linked through multiple regression modeling with metrics extracted from spectral index time series. Our results show that the remote sensing-based models enable monitoring the onset (root mean squared deviation (RMSD) ranges from 5.96 to 27.04 days) and mid-stages of stem growth (RMSD ranges from 10.20 to 36.34 days) with reasonable accuracy as opposed to the cessation of stem growth that showed low accuracy (RMSD ranges from 16.02 to 153.63 days). The accuracy of the remote sensing-based prediction models and their optimal suite of predictors varied across species. The latter has important implications for the remote sensing of stem growth phenology in mixed forests, suggesting that it is important for satellite sensors to resolve individual tree crowns. Overall, our results suggest the need for novel spectral indices that capture the spectral components of mechanistic linkages between stem growth and canopy properties that go beyond the mere detection of leaf phenology. When employing such spectral indices, remote sensing could make it possible to detect not only shifts in leaf phenology caused by climate change but also those in stem growth on a broad spatial scale.

Estimating evapotranspiration and yield of wheat and maize croplands through a remote sensing-based model

Estimating evapotranspiration and yield of wheat and maize croplands through a remote sensing-based model

Development of the grass LAI and CCC remote sensing-based models and their transferability using sentinel-2 data in heterogeneous grasslands

ABSTRACT Estimation of biophysical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC) at high spatiotemporal resolution is important for managing natural and heterogeneous environments. However, accurate estimation of biophysical variables particularly over heterogeneous environments remains a challenge. The objective of the study was to develop locally parameterized grass LAI and CCC empirical models using the Sentinel-2 variables combined with the Stepwise multiple linear regression (SMLR) and Random forest (RF) at the Golden Gate Highlands National Park (GGHNP) and Marakele National Park (MNP) in South Africa. Results showed that in MNP, SMLR yielded better LAI estimation with root mean squared error (RMSE) of 0.67 m2.m−2 and mean adjusted error (MAE) of 0.54, explaining 48% of LAI variability, when bands and indices are combined. In contrast, RF gave better CCC estimation i.e. RMSE and MAE of 17.08 µg.cm−2 and 13.18 respectively, explaining about 40% of CCC variability with Sentinel-2 bands only. In GGHNP, the RF models provided the best estimates of both LAI and CCC compared to SMLR models. Furthermore, the CCC and LAI estimation models of GGHNP showed improved model accuracies when 50% and 75% of the MNP field samples were transferred to the GGHNP models. In contrast, the CCC and LAI estimation models of MNP showed a decline in model performance across all scenarios where the GGHNP field samples were transferred to the MNP models. These findings have significant implications for the development of locally parameterized types of models that can provide improved and consistent site-specific accurate estimates of grass biophysical parameters over heterogeneous environments.

Remote Sensing-Based Estimates of Changes in Stored Groundwater at Local Scales: Case Study for Two Groundwater Subbasins in California’s Central Valley

Sustainable groundwater management requires high-quality and low-latency estimates of changes in groundwater storage (∆Sgw). However, estimates of ∆Sgw produced using traditional methods, including groundwater models and well-based measurements, typically lag years behind the present because collecting the required on-the-ground data is a time consuming, expensive, and labor-intensive task. Satellite remote sensing measurements provide potential pathways to overcome these limitations by quantifying ∆Sgw through closing the water balance. However, the range of spatial scales over which ∆Sgw can be accurately estimated using remote sensing products remains unclear. To bridge this knowledge gap, this study quantified ∆Sgw for the period of 2002 through to 2021 using the water balance method and multiple remote sensing products in two subbasins (~2700 km2–3500 km2) within California’s Central Valley: (1) the Kaweah–Tule Subbasin, a region where the pumping of groundwater to support agriculture has resulted in decades of decline in head levels, resulting in land subsidence, damage to infrastructure, and contamination of drinking water and (2) the Butte Subbasin, which receives considerably more rainfall and surface water and has not experienced precipitous drops in groundwater. The remote sensing datasets which we utilized included multiple sources for key hydrologic components in the study area: precipitation, evapotranspiration, and soil moisture. To assess the fidelity of the remote sensing-based model, we compared estimates of ∆Sgw to alternative estimates of ∆Sgw derived from independent sources of data: groundwater wells as well as a widely used groundwater flow model. The results showed strong agreement in the Kaweah–Tule Subbasin in long-term ∆Sgw trends and shorter-term trends during droughts, and modest agreement in the Butte Subbasin with remote sensing datasets suggesting more seasonal variability than validation datasets. Importantly, our analysis shows that the timely availability of remote sensing data can potentially enable ∆Sgw estimates at sub-annual latencies, which is timelier than estimates derived through alternate methods, and thus can support adaptive management and decision making. The models developed herein can aid in assessing aquifer dynamics, and can guide the development of sustainable groundwater management practices at spatial scales relevant for management and decision making.

Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass

Accurately characterizing carbon stock is vital for reporting carbon emissions from forest ecosystems. We studied the estimation of biomass using Sentinel-2 remote sensing data in moist temperate forests in the Galies region of Abbottabad Pakistan. Above-ground biomass (AGB), estimated from 60 field plots, was correlated with vegetation indices obtained from Sentinel-2 image-to-map AGB using regression models. Furthermore, additional explanatory variables were also associated with AGB in the geo-statistical technique, and kriging interpolation was used to predict AGB. The results illustrate that the atmospherically resistant vegetation index (ARVI) is the best index (R2 =0.67) for estimating AGB. In spectral reflectance, Band 1(Coastal Aerosol 443 nm) performs better than other bands. Multiple linear regression models calibrated with ARVI, NNIR and NDVI yielded better results (R2 = 0.46) with the lowest RMSE (48.53) and MAE (38.42) and were therefore considered better for biomass estimation. On the other hand, in the geo-statistical technique, distance to settlements, ARVI and annual precipitation were significantly correlated with biomass compared to others. In the stepwise regression method, the forward selection resulted in a very significant value (less than 0.000) for ARVI. Therefore, it can be considered best for prediction and used to interpolate AGB through kriging. Compared to the geo-statistical technique, the remote sensing-based models performed relatively well. Regarding potential sites for REDD+ implementation, temporal analysis of Landsat images showed a decrease in forest area from 8896.23 ha in 1988 to 7692.03 ha in 2018. Therefore, this study concludes that the state-of-the-art open-source sensor, the Sentinel-2 data, has significant potential for forest biomass and carbon stock estimation and can be used for robust regional AGB estimation with acceptable accuracy and frequent availability.

Evaluation of time-series Sentinel-2 images for early estimation of rice yields in south-west of Iran

PurposeIn the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as rice creates serious worldwide food insecurity problems. The main purpose of this study was to compare the potential of time-series Landsat-8 and Sentinel-2 data to predict rice yield several weeks before harvest on a regional scale.Design/methodology/approachTo this end, the sum of normalized difference vegetation index (NDVI)-based models created the best agreement with actual yield data at the golden time window of six weeks before harvest when rice grains were in milky and mature growth stages. The application of nine other vegetation indicators was also investigated in the golden time window in comparison to NDVI.FindingsThe findings of this study demonstrate the viability of identifying locations with poor and superior performance in terms of production management approaches through a rapid and economical solution for early rice grain yield assessment. Results indicated that while some of those, such as enhanced vegetation index (EVI) and optimized soil adjusted vegetation index, were able to estimate rice yield with high accuracy, NDVI is still the best indicator to predict rice yield before harvest. However, experiments can be conducted in different regions in future studies to evaluate the generalizability of the approach.Originality/valueTo achieve this objective, the authors considered the following purposes: using Sentinel-2 time-series data, determining the appropriate growth stage for estimating rice yield and evaluating different vegetation indices for estimating rice yield.

Remote sensing of dissolved CO2 concentrations in meso-eutrophic lakes using Sentinel-3 imagery

Satellite observation can significantly reduce the uncertainties in CO2 emission estimations compared to insufficient field data. However, big challenges remain in developing remote sensing-based models for mapping concentrations of dissolved carbon dioxide (cCO2) in lakes at regional or global scales. We developed a cCO2 estimation model using Sentinel-3-derived lake environmental variables and near-synchronous field cCO2 data from 16 lakes in the middle and lower reaches of the Yangtze and Huai River (ML_YHR) basins in Eastern China (N = 248). Stepwise quadratic polynomial regressions of several combinations of chlorophyll-a (Chl-a), water temperature (Tw), Secchi disk depth (ZSD), and photosynthetic active radiation (PAR)-related variables were tested and validated to select the best approach. The final model showed high performance in calibration and validation (R2 > 0.72, RMSE < 6.35 μmol L−1, MAPE < 30.31%). The model sensitivity analysis, based on Monte Carlo simulations, showed the model's estimated bias as <25% based on uncertainties of all input variables. Spatial and temporal dynamics of dissolved CO2 concentrations in 113 lakes (≥ 10 km2) in the ML_YHR basins were mapped from 2016 to 2021 using the Sentinel-3 data. The result showed that CO2 concentrations were low in the summer and autumn but high in the winter and spring with dramatic variations (e.g., mean coefficient of variation: 52.59%). The annual mean CO2 concentrations of lakes revealed that about 28% of the lakes acted as weak atmospheric CO2 sinks (14.96 ± 1.13 μmol L−1) while the rest were sources (19.22 ± 2.02 μmol L−1), compared with a mean concentration of CO2 atmospheric equilibrium (16.29 μmol L−1). CO2 concentrations decreased with increasing eutrophication and decreasing lake size (p < 0.05). This study advances current knowledge about CO2 emissions from emerging meso-eutrophic lakes and shows how satellite remote sensing can expand the spatiotemporal coverage of lake CO2.