Wade T. Crow’s research while affiliated with Agricultural Research Service and other places

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Publications (15)


From theory to hydrological practice: Leveraging CYGNSS data over seven years for advanced soil moisture monitoring including those for text and data mining, AI training, and similar technologies
  • Article

November 2024

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133 Reads

Remote Sensing of Environment

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Nguyen

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Soil moisture (SM) is a key variable in hydrometeorology and climate systems. With the growing interest in capturing fine-scale SM variability for effective hydroclimate applications, spaceborne L-band bistatic radar systems using Global Navigation Satellite System-Reflectometry (GNSS-R) technology hold great potential to meet the demand for high spatiotemporal resolution SM data. Although primarily designed for tropical cyclone monitoring purposes, the first GNSS-R satellite constellation-Cyclone Global Navigation Satellite System (CYGNSS) mission, has demonstrated the benefits of reliably monitoring diurnal SM dynamics through its initial stage of seven-year data record, thanks to its high revisit frequency at sub-daily intervals. Nevertheless, knowledge of SM retrieval from CYGNSS, particularly linked with its distinctive features, remains poorly understood , while numerous existing uncertainties and open issues can restrict its effective SM retrieval and practical applications in the next operating stages. Unlike other review papers, this work aims to bridge this knowledge gap in CYGNSS SM retrieval by highlighting noteworthy design properties based on analyses of its real-world data, while providing a synthesis of recent advances in eliminating external uncertainty factors and improving SM inversion methods. Despite its potential, CYGNSS SM retrieval faces both general and particular challenges arising from common issues in retrieval algorithms for conventional GNSS-R satellites and unique data limitations tied to its technical design. Scientific debates over the contributions of coherent and incoherent components in total CYGNSS signals and accurate partitioning of these two parts are defined as the key algorithm-related challenges to resolve, along with correcting attenuation effects of vegetation and surface roughness. The data-related challenges involve variations in CYGNSS's spatial footprint, temporal frequency, and signal penetration depth across different land surface conditions, inadequate consideration of CYGNSS incidence angle change, excessive dependence on a reference SM dataset for inversion model calibration/training or validation, and computational demands for processing rapid multi-sampling CYGNSS data retrieval. Future research pathways highlight leveraging cutting-edge machine learning/deep learning algorithms to enhance CYGNSS SM data quantity and quality and better interpret its complex interactions with other hydroclimate variables. Assimilating CYGNSS SM data streams into physical models to improve the prediction of related variables and climate extremes also presents a promising prospect.


Fig. 1. (a) Geographical location, (b) land use composition, and (c) distribution of hydrologic soil groups of the study area (adapted from Lee, 2018 [32])) Note: hydrologic soil groups (HSGs) are classified based on their infiltration rates as follows: Type A -well-drained soils with a water infiltration rate of 7.6-11.4 mm h 1 ; Type B -moderately well-drained soils with a rate of 3.8-7.6 mm h 1 ; Type C -moderately poorly-drained soils with a rate of 1.3-3.8 mm h 1 ; and Type D -poorly-drained soils with a rate of 0-1.3 mm h 1 [33]. The distribution of HSGs within the TCW is as follows: HSG-A, HSG-B, HSG-C, and HSG-D account for 0.3 %, 55.8 %, 2.2 %, and 41.7 % of the watershed, respectively.
Fig. 2. Daily simulated and observed streamflow, watershed-level RS-ET, and RS-LAI from 2010 to 2014: PAR #3 (a, g, and m), #4 (b, h, and n), #9 (c, i, and o), #12 (d, j, and p) #13 (e, k, and q) #14 (f, l, and r). The extended illustration is included in Fig. S3 of the Supplementary Material.
Fig. 4. Median KGE values across sub-watersheds: (a) ET for calibration, (b) ET for validation, (c) LAI for calibration (d) LAI for validation. A KGE threshold of 0.5 is denoted by the horizontal red line. Detailed KGE values for individual sub-watersheds are found in Tables S4 and S5 of the Supplementary Material for ET and LAI, respectively.
Fig. 5. The sub-watershed level KGE values for the PAR#4, PAR#13, and PAR#14: ET (a, b, and c) and LAI (d, e, and f). Note: The figure was created by ArcMap 10.7 program (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources).
Eight cultivation crop configurations in this study.

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Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data
  • Article
  • Full-text available

May 2024

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114 Reads

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1 Citation

Heliyon

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

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A brief history of the thermal IR-based Two-Source Energy Balance (TSEB) model – diagnosing evapotranspiration from plant to global scales

March 2024

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382 Reads

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8 Citations

Agricultural and Forest Meteorology

Thermal infrared (TIR) remote sensing of the land-surface temperature (LST) provides an invaluable diagnostic of surface fluxes and vegetation state, from plant and sub-field scales up to regional and global coverage. However, without proper consideration of the nuances of the remotely sensed LST signal, TIR imaging can give poor results for estimating sensible and latent heating. For example, sensor view angle, atmospheric impacts, and differential coupling of soil and canopy sub-pixel elements with the overlying atmosphere can affect the use of satellite-based LST retrievals in land-surface modeling systems. A concerted effort to address the value and perceived shortcomings of TIR-based modeling culminated in the Workshop on Thermal Remote Sensing of the Energy and Water Balance, held in La Londe les Maures, France in September of 1993. One of the outcomes of this workshop was the Two-Source Energy Balance (TSEB) model, which has fueled research and applications over a range of spatial scales. In this paper we provide some historical context for the development of TSEB and TSEB-based multi-scale modeling systems (ALEXI/DisALEXI) aimed at providing physically based, diagnostic estimates of latent heating (evapotranspiration, or ET, in mass units) and other surface energy fluxes. Applications for TSEB-based ET retrievals are discussed: in drought monitoring and yield estimation, water and forest management, and data assimilation into – and assessment of – prognostic modeling systems. New research focuses on augmenting temporal sampling afforded in the thermal bands by integrating cloud-tolerant, microwave-based LST information, as well as evaluating the capabilities of TSEB for separating ET estimates into evaporation and transpiration components. While the TSEB has demonstrated promise in supplying water use and water stress information down to sub-field scales, improved operational capabilities may be best realized in conjunction with ensemble modeling systems such as OpenET, which can effectively combine strengths of multiple ET retrieval approaches.


True global error maps for SMAP, SMOS, and ASCAT soil moisture data based on machine learning and triple collocation analysis

December 2023

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313 Reads

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16 Citations

Remote Sensing of Environment

Quantifying the accuracy of the satellite-based soil moisture (SM) data is important for a number of key applications , such as: combining satellite-based SM products for long-term SM analyses, assimilating SM data into land surface models, and providing quality flags to mask bad quality SM data. A range of statistical methods have been proposed to estimate error statistics for large-scale SM datasets including the: instrumental variable (IV) method, triple collocation analysis (TCA), and quadruple collocation analysis (QCDA). While requiring only two input products, the IV method also imposes an additional assumption that one input product possesses serially uncorrelated errors-thus limiting its scope compared to TC. Likewise, QCDA requires four independent SM data products that are difficult to obtain and may not always be available for analysis. Nonetheless, TCA-based methods still cannot provide truly global error maps for satellite SM products due to the limited number of independent SM products and difficulties with baseline TCA assumptions. Moreover, temporal sampling requirements for TCA are often impractical because of low SM retrieval skill in forested and arid areas-as well as in regions prone to radio frequency interference. Here, we seek to fill significant spatial gaps in TCA results using machine learning (ML) and therefore provide spatially complete error maps for the satellite-based SM data products derived from the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) systems. Furthermore, we use SHapley Additive exPlanations (SHAP) values, a model-agnostic technique for interpreting ML models, to examine the impact of various environmental conditions on the quality of satellite-based SM retrievals. Globally, and across all three products, 72.0% of missing error information in a TCA-based analysis, due to either the lack of valid data or the inability of TCA to provide reliable results, can be reconstructed from the ensemble prediction mean of the ML models. Overall, we found that 22.7% (a.m.) and 34.2% (p.m.) of the Earth'sSM dynamics (between 60 • S to 60 • N) have not been investigated properly across all three satellite missions.


FIGURE 1
Cross-cutting concepts to transform agricultural research

August 2023

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169 Reads

Agriculture is an important link to many issues that challenge society today, including adaptation to and mitigation of climate change, food security, and communicable and non-communicable diseases in animals and humans. Transformation of agriculture and food systems has become a priority for a range of federal agencies and global organizations. It is imperative that food and agricultural researchers effectively harness the global convergence of priorities to overcome research “silos” through deep and sustained systemic change. Herein, we identify intersections in federal and global initiatives encompassing climate adaptation and mitigation; human health and nutrition; animal health and welfare; food safety and security; and equity and inclusion. Many agencies and organizations share these priorities, but efforts to address them remain uncoordinated and opportunities for collaboration untapped. Based on the interconnectedness of the identified priority areas, we present a research framework to catalyze agricultural transformation, beginning with the research enterprise. We propose that transformation in agricultural research should incorporate (1) innovation, (2) integration, (3) implementation, and (4) evaluation. This framework provides approaches for food and agricultural research to contribute to sustainable, flexible, and coordinated transformation in the agricultural sector.


A Bayesian machine learning method to explain the error characteristics of global-scale soil moisture products

August 2023

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495 Reads

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15 Citations

Remote Sensing of Environment

Estimating accurate surface soil moisture (SM) dynamics from space, and knowing the error characteristics of these estimates, is of great importance for the application of satellite-based SM data throughout many Earth Science/Environmental Engineering disciplines. Here, we introduce the Bayesian inference approach to analyze the error characteristics of widely used passive and active microwave satellite-derived SM data sets, at different overpass times, acquired from the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) missions. In particular, we apply Bayesian hierarchical modeling (BHM) and triple collocation analysis (TCA) to investigate the relative importance of different environmental factors and human activities on the accuracy of satellite-based data. To start, we compare the BHM-based sensitivity analysis method to the classic multiple regression models using a frequentist approach, which includes complete pooling and no-pooling models that have been widely used for sensitivity analysis in the field of remote sensing and demonstrate the BHM's adaptability and great potential for providing insight into sensitivity analysis that can be used by various remote sensing research communities. Next, we conduct an uncertainty analysis on BHM's model parameters using a full range of uncertainties to assess the association of various environmental factors with the accuracy of satellite-derived SM data. We focus on investigating human-induced error sources such as disturbed surface soil layers caused by irrigation activities on microwave satellite systems, naturally introduced error sources such as vegetation and soil organic matter, and errors related to the disregard of SM retrieval algorithmic assumptions-such as the thermal equilibrium passive microwave systems. Based on the BHM-based sensitivity analysis, we find that assessments of SM data quality with a single variable should be avoided, since numerous other factors simultaneously influence their quality. As such, this provides a useful framework for applying Bayesian theory to the investigation of the error characteristics of satellite-based SM data and other time-varying geophysical variables.


IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

July 2023

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96 Reads

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5 Citations

Journal of Hydrometeorology

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.


Multivariate Calibration of the SWAT Model Using Remotely Sensed Datasets

May 2023

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310 Reads

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12 Citations

Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling.


A reduced latency regional gap-filling method for SMAP using random forest regression

January 2023

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176 Reads

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4 Citations

iScience

The soil moisture active/passive (SMAP) mission represents a significant advance in measuring soil moisture from satellites. However, its large spatial-temporal data gaps limit the use of its values in near-real-time (NRT) applications. Considering this, the study uses NRT operational metadata (precipitation and skin temperature), together with some surface parameterization information, to feed into a random forest model to retrieve the missing values of the SMAP L3 soil moisture product. This practice was tested in filling the missing points for both SMAP descending (6:00 AM) and ascending orbits (6:00 PM) in a crop-dominated area from 2015 to 2019. The trained models with optimized hyper-parameters show the goodness of fit (R2 ≥ 0.86), and their resulting gap-filled estimates were compared against a range of competing products with in situ and triple collocation validation. This gap-filling scheme driven by low-latency data sources is first attempted to enhance NRT spatiotemporal support for SMAP L3 soil moisture.



Citations (10)


... It plays a significant role in global energy balance and water distribution [2,3]. ET is influenced by a variety of climatic factors, such as wind speed, solar radiation, temperature, and humidity [4]. ...

Reference:

Enhancing Evapotranspiration Estimation: A Bibliometric and Systematic Review of Hybrid Neural Networks in Water Resource Management
A brief history of the thermal IR-based Two-Source Energy Balance (TSEB) model – diagnosing evapotranspiration from plant to global scales

Agricultural and Forest Meteorology

... These techniques aim to capture the random errors of three datasets from different measurement systems without requiring a fixed truth reference dataset, under the assumption that these three datasets are independent and linearly related to the true product. This method has been widely employed in the hydrological sector for error characterization, particularly for SM products (Gruber et al., 2016;Chen et al., 2018;Kim et al., 2023a;Kim et al., 2023b). Despite its extensive use in characterizing errors in different microwave satellite SM datasets, only a few previous studies have initially investigated its application in evaluating CYGNSS-derived SM using different sets of triple SM products. ...

True global error maps for SMAP, SMOS, and ASCAT soil moisture data based on machine learning and triple collocation analysis
  • Citing Article
  • December 2023

Remote Sensing of Environment

... Xu et al. (2024) pointed out that this approach significantly improved the classification performance by utilizing prior information and measuring uncertainty in each prediction. Kim et al. (2023) developed a Bayesian machine learning to analyze error characteristics in global soil moisture data, which allowed them to evaluate the impact of environmental factors and human activities on the data quality of soil moisture. These studies highlighted the considerable potential of Bayesian technique in active learning. ...

A Bayesian machine learning method to explain the error characteristics of global-scale soil moisture products
  • Citing Article
  • August 2023

Remote Sensing of Environment

... While these regions are generally geopolitically ungauged or have a sparing network if in-situ data for both precipitation and SM, the lack of SM gauges is much more severe than that of rain gauges. Additionally, in data-rich regions like the CONUS and Europe, dense system of rain gauges can provide better evaluation for downscaled high-resolution SM products (Crow et al., 2022;Reichle et al., 2023). Past studies leveraged the inherent relationship between SM and rainfall to indirectly evaluate satellite SM. ...

IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product
  • Citing Article
  • July 2023

Journal of Hydrometeorology

... To reduce the computational cost for model calibration, a regionalization approach was implemented for the calibration of streamflow and instream total nitrogen. The SWAT calibration was done in two parts following the approach described in previous studies (Dangol et al., 2023;Feng et al., 2018). First, a coarse SWAT model was developed using the thresholds (i.e., % area of each subbasin) of 5% for land cover, 10% for soil class, and 10% for single slope class during HRU delineation, resulting in 5,129 HRUs. ...

Multivariate Calibration of the SWAT Model Using Remotely Sensed Datasets

... For soil moisture, most recent gap-filling studies focus on the applicability of different multivariate machine learning (ML) methods in small to medium-sized study regions. Random forest (RF) has been the dominant algorithm (Nadeem et al., 2023;Liu et al., 2020c;Nadeem et al., 2023;Wang et al., 2023;Mao et al., 2019;Bessenbacher et al., 2022), which was found to outperform other covariate supported approaches such as Neural Networks (NN), XGBoost, Support Vector Machines (SVM), or Multivariate Linear Regression (Liu et al., 2023;Sun and Xu, 2021;Tong et al., 2021;Almendra-Martín et al., 70 2021). To predict missing soil moisture, these studies use physically related variables such as air or land surface temperature, precipitation, soil type, topography, land cover, and vegetation properties. ...

A reduced latency regional gap-filling method for SMAP using random forest regression

iScience

... Moreover, there is a considerable influence (inversely correlated) of the topography with the streamflow signatures, with the increase in the altitude and slope lowering the runoff ratio, baseflow index, slope of the FDC, and mean streamflow, making this group of catchments to probably be an exporter of water (i.e., leaky catchments). This is in accordance with previous research on leaky catchments in Brazil (Schwamback et al., 2022), highlighting the importance of considering an open water balance on inferences about streamflow (Gordon et al., 2022). Finally, this group is one of the most impacted by human disturbance. ...

Can We Use the Water Budget to Infer Upland Catchment Behavior? The Role of Data Set Error Estimation and Interbasin Groundwater Flow

... This approach, characterized by its simplicity and accessibility, contrasts with the intricate methodologies employed by other researchers in optimizing and fusing reanalysis data. These methodologies encompass sophisticated techniques, including artificial neural networks [59], wavelet transform methods [60], genetic algorithms [61,62], and machine learning [63,64]. However, it is noteworthy that the efficacy of the aforementioned optimization is constrained to situations akin to CMADS, where certain metrics exhibit suboptimal performance relative to others. ...

Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau

... The lower frequency (L-band) of SMAP can overcome some of those limitations and has been shown to produce accurate estimates of soil moisture in forests (e.g., [24]). In addition, there have been numerous studies that have evaluated the potential for using SMAP soil moisture observations to monitor drought characteristics (e.g., [12,35]). The SMAP data products that we used in this study include the SPL3SMP_E Enhanced Level-3 soil moisture and the SPL2SMAP_S Level-2 product, which combines SMAP and Sentinel-1 measurements to derive soil moisture at 3 and 1 km. ...

Investigating the Efficacy of the SMAP Downscaled Soil Moisture Product for Drought Monitoring Based on Information Theory

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

... NDWI has been widely used to detect water bodies from space for multiple applications including wetland monitoring [25], canopy estimation [26], and coastline changes [27]. It is true that NDWI may overestimate the presence of water due to existing cloud cover at the time the imagery is captured. ...

Estimating Corn Canopy Water Content From Normalized Difference Water Index (NDWI): An Optimized NDWI-Based Scheme and Its Feasibility for Retrieving Corn VWC
  • Citing Article
  • October 2021

IEEE Transactions on Geoscience and Remote Sensing