Moderate Resolution Imaging Spectroradiometer Imagery Research Articles

Monitoring vegetation cover trends in the steppe region of western Algeria using MODIS imagery

The steppe region of Chehaima (Tiaret Province, northwestern Algeria) covers an area of 2,202 km², representing 10.94% of the total area of Tiaret. This study identified the ecological state of the area using a spatial theme from a 24-year synchronized moderate-resolution imaging spectroradiometer (MODIS) satellite image campaign (2000–2023). The adopted method consists of analyzing spectral indices (normalized difference vegetative index, low surface temperature (LST), and soil moisture index) from MODIS sensors (MOD13Q1), making it possible to evaluate the spatiotemporal dynamics of the vegetation in the study area and to identify regions degraded by several biotic and abiotic factors. The results show a vegetation cover rate of intense fluctuations in unfavorable conditions over 24 years. The percentage of plant growth recorded does not exceed 32%. The abundance of LST vegetation decreases when the LST exceeds 35°C, with a soil that has a water deficit. The De Martonne aridity index (<em>I</em>) classifies the study area as a lower semiarid bioclimatic zone. These indicators made it possible to determine the spatiotemporal dynamics of vegetation, which modifies the plant cover, leading to the loss of native species such as alfa <em>Stipa tenacissima</em> L. Defensive measures are necessary over large areas of the study area to allow successful protection of steppe rangelands through a sustainable conservation strategy, preserving endemic species, stabilizing dunes, and adopting the steppe rotation system.

Three-dimensional reconstruction of aerosols based on spectral radiance matching

This paper evaluates a three-dimensional scene construction algorithm that assigns CALIPSO-observed profiles to receiving pixels in MODIS imagery through spectral radiance matching (SRM), so that a limited number of vertically resolved aerosol observations can be expanded into a regionally continuous three-dimensional aerosol structure. The SRM algorithm was tested using moderate resolution imaging spectro-radiometer (MODIS) and cloud-aerosol lidar and infrared pathfinder satellite observation (CALIPSO) data for the period of April 10–25, 2015. First, the spectral radiance channel and the surface reflectance channel were selected for combination tests to select the best channel combination 01-04-07-26 for the application of SRM algorithm in the field of aerosol three-dimensional reconstruction. The average aerosol matching rate for this channel combination can reach 90.57%, 81.64%, 78.6%, 74.12%, and 65.96% at extended distances of 5, 20, 30, 50, and 100 km, respectively. In addition, the combination of the average correct match rate and the reconstructed aerosol structure revealed that the most suitable extension distance is up to 100 km. Then, for the reconstruction profile validation, lidar sites under four weather conditions (clean sky, aerosol loading, thin clouds, and thick clouds) were selected for validation. The comparison results reveal that the average relative errors are about 30%, 60%, 30%–60%, and 70% for these four conditions, respectively. Overall, the average relative error of the reconstructed profiles within 100 km of the orbital track of CALIPSO (except under thick cloud conditions) is less than 60%, which is within a reasonable error envelop. There are also some limitations that can be improved later by adding more constraints: 1) it is difficult to reconstruct some short-time, small-scale aerosol or cloud features; 2) it is difficult to reconstruct some hierarchical structures under thick clouds due to signal attenuation caused by thick clouds; 3) there is an underestimation of the attenuated backscatter coefficient under clouds. Finally, for aerosol optical depth (AOD), the SRM algorithm largely underestimates AOD. The reconstructed AOD is in good agreement with aerosol robotic network (AERONET), with R2=0.8231 after removal of outliers.

Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery

Abstract Climate change poses an urgent global challenge in water resource management, with drought emerging as a pervasive threat worldwide. Over the past two decades, Iraq has faced severe droughts, categorized into meteorological, agricultural, hydrological, and socioeconomic types. Agricultural drought, marked by prolonged soil moisture deficits due to insufficient rainfall, often leads to crop failures. This comprehensive study delves into the impact of drought on Iraq’s vegetation cover from 2000 to 2022, utilizing advanced tools like remote sensing (RS) and geographic information systems. The normalized difference vegetation index (NDVI) facilitated the creation of multitemporal drought maps. Employing Landsat satellite imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, the study revealed 2000, 2008, 2009, 2010, 2012, and 2022 as the most drought-prone years. In specific years such as 2000, 2008, 2010, and 2009, Landsat imagery showcased the lowest vegetation cover percentages (3.356, 4.984, 5.112, and 5.409%, respectively), while MODIS data indicated the lowest percentages in 2008, 2000, 2009, 2012, and 2022 (11.122, 11.260, 12.580, 13.026, and 14.445%, respectively). This study underscores the pivotal role of RS, particularly NDVI, as a valuable tool for agricultural drought early warning systems. The findings highlight the effectiveness of NDVI as a simple and cost-efficient index for monitoring changes in vegetation conditions and assessing the impact of droughts on agriculture.

Rapid, Landscape-Scale Assessment of Cyclonic Impacts on Mangrove Forests Using MODIS Imagery

Cyclones are a key disturbance in mangrove ecosystems, but it is challenging to assess post-storm impacts over large areas, along with the recovery of these systems at broad temporal scales. Given the high frequency of these events in the Sundarbans region, prompt and consistent assessment of vegetation conditions is an important research need. Several studies have assessed the impact of an extreme cyclone event in 2007 (Sidr); however, there is little agreement between the extent and severity of the disturbance footprint of the cyclone, and very few studies attempted to assess vegetation recovery. We used a MODIS (Moderate Resolution Imaging Spectroradiometer) time series (2001–2010) to calculate monthly plant productivity anomalies in Google Earth Engine. We summarized dry season anomalies to assess post-storm vegetation change and evaluate the recovery time. Approximately 2100 km2, primarily on the east side, were impacted by Sidr. The number of damaged pixels was reduced by 55% the following dry season (2008) and 93% in the dry season of 2009, indicating a near-full recovery 26 months after the event. Our results provide an additional line of evidence to provide a rapid assessment of the post-storm vegetation damage. The simple framework used can provide a comprehensive view of the extent of the damage, including lag effects on vegetation, in just a matter of months after the event.

Monitoring of sea surface temperature, chlorophyll, and turbidity in Tunisian waters from 2005 to 2020 using MODIS imagery and the Google Earth Engine

Time series ocean color data product facilitates the study of spatio-temporal variability of various physical parameters of water, which allows the monitoring of water quality, interannual variability in phytoplankton biomass, and seasonal representation at different latitudes.Its role in marine biogeochemistry, water quality distribution, and climate fluctuation is very crucial., However, the lack of in situ measurement induces poor management and knowledge of the dynamics of Tunisian coastal waters. Therefore, this study aims to develop a workflow to monitor Tunisian waters based on long-term spatial observations of sea surface temperature, chlorophyll, and turbidity observations.Long-term sea surface observations were automatically obtained by processing the daily Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua data via the Google Earth Engine (GEE) platform from 2005 to 2020. The recorded average monthly and yearly trends are validated by point-based measurements from the Gulf of Gabes and a qualitative analysis based on the bibliographic synthesis of offshore measurement campaigns in Tunisian waters. The hottest years were 2006, 2007, and 2017 while the coldest ones (12–28 °C) were 2011, 2012, and 2016. The highest chlorophyll content (10 and 5 μg L-1) was observed in 2006, 2007, 2011, 2012, 2015, 2016, and 2019. In addition, Turbidity peaks ranging between 11 nephelometric turbidity units (NTU) and 5 NTU were identified during December and January of 2005 2008, and 2011. Moreover, seasonal cyclicity and high correlations between estimated parameters were observed. Overall, combining the Google Earth Engine tool with daily MODIS data was effective for the routine monitoring of water quality parameters that is fast, accurate, and important for Tunisian coast management.

Dust detection and susceptibility mapping by aiding satellite imagery time series and integration of ensemble machine learning with evolutionary algorithms

To mitigate the impact of dust on human health and the environment, it is crucial to create a model and map that identifies the areas susceptible to dust. The present study focused on identifying dust occurrences in the Bushehr province of Iran between 2002 and 2022 using moderate-resolution imaging spectroradiometer (MODIS) imagery. Subsequently, an ensemble machine learning model was improved to prepare a dust susceptibility map (DSM). The study employed differential evolution (DE), genetic algorithm (GA), and flower pollination algorithm (FPA) - three evolutionary algorithms - to enhance the random forest (RF) ensemble model. A spatial database was created for modeling, including 519 dust occurrence points (extracted from MODIS imagery) and 15 factors affecting dust (Slope, bulk density, aspect, clay, altitude, sand, rainfall, lithology, soil order, distance to river, soil texture, normalized difference vegetation index (NDVI), soil water content, land cover, and wind speed). By utilizing the differential evolution (DE) algorithm, we determined the significance of these factors in impacting dust occurrences. The results indicated that altitude, wind speed, and land cover were the most influential factors, while the distance to the river, bulk density, and soil texture had less impact on dust occurrence. Data were preprocessed using multicollinearity analysis and the frequency ratio (FR) approach. For this research, three RF-based meta-heuristic optimization algorithms, namely RF-FPA, RF-GA, and RF-DE, were created for DSM. The effectiveness prediction of the constructed models by indexes of root-mean-square-error (RMSE), the area under the receiver operating characteristic (AUC-ROC), and coefficient of determination (R2) from best to worst were RF-DE (RMSE = 0.131, AUC-ROC = 0.988, and R2 = 0.93), RF-GA (RMSE = 0.141, AUC-ROC = 0.986, and R2 = 0.919), RF-FPA (RMSE = 0.157, AUC-ROC = 0.981, and R2 = 0.9), and RF (RMSE = 0.173, AUC-ROC = 0.964, and R2 = 0.878). The results showed that combining evolutionary algorithms with an RF model improves the accuracy of dust susceptibility modeling.

Arctic sea ice drift fields extraction based on feature tracking to MODIS imagery

Moderate-resolution optical imagery holds great potential in deriving Arctic sea ice drift fields because of its higher resolution than microwave radiometers and scatterometers, as well as its larger swath widths than most other optical and synthetic aperture radar (SAR) images. However, the application of such imagery is hindered by cloud influences and a lack of texture. In this study, we propose a method of deriving Arctic sea ice drift fields based on applying feature tracking to Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. To enhance the quality of the feature tracking step, a bundle of digital image processing techniques is first introduced, including histogram equalization which is based on the Cumulative Distribution Function (CDF) of the sea ice area, and Laplacian filtering which enhances image texture. Various MODIS bands and A-KAZE parameter settings are subsequently compared to balance the quality of sea ice drifting fields and calculation efficiency. Three pairs of MODIS images observed in different zones of the Arctic Ocean are selected to evaluate the performance of the proposed method. International Arctic Buoy Programme (IABP) buoy data are employed for validating the derived drift vectors with MODIS imagery. The results show that our proposed method effectively increases the number of vectors and their coverage rates of the sea ice drift fields extracted with MODIS images. The coverage rates of sea ice drift fields in three regions increase from 4.8%, 2.3%, and 2.5% to 56.5%, 23.5%, and 53.0% compared to using the A-KAZE algorithm directly, respectively. The MAEs of the derived sea ice motion vectors are 707 m/d in speed and 6.4° in direction, superior to the sea ice drift products based on the Advanced Very High Resolution Radiometer (AVHRR) imagery. The proposed method enables MODIS and other medium-resolution optical data to be utilized in deriving Arctic sea ice drift fields, which is of great significance to the long-term and large-scale Arctic environment, climate, and oceanography research in the future.

Določevanje fenoloških faz izbranih drevesnih vrst s časovnimi vrstami posnetkov MODIS

This study investigates the usefulness of MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery for determining the start, end, and length of the growing season of selected deciduous tree species. Vegetation indices derived from satellite imagery provide consistent observations in a similar temporal sequence and are useful for determining phenological phases. Using time series of NDVI (Normalised Difference Vegetation Index) vegetation index from MODIS imagery, phenological patterns were detected at several points in Slovenia and different approaches to determine seasonal phases were compared. In addition, the derived seasonal phases with field phenological and meteorological data were also compared. It has been found that the success of determining phenological phases from satellite imagery depends on many factors: the spatial resolution of the satellite data, the smoothing method for the time series data, the method for determining phenological parameters, and the field data used for comparison. The results of the study show that phenological phases determined by using MODIS data with a resolution of 250 m best match the phenological data maintained by the Slovenian Forestry Institute using the mean seasonal values method.

Satellite‐Synoptic Monitoring of Dominant Dust Entering Western Iran

Dust storm in Iran’s western regions has been one of its major environmental problems in recent years, which has not only turned into a yearly phenomenon but is also expanding. This study investigated two events of dominant dust in southwestern Iran using moderate resolution imaging spectroradiometer imagery, Reanalysis Datasets (meteorological fields and atmospheric compositions), in both hot (July 2, 2008) and cold (February 18, 2017) seasons. After radiometric correction and calculation of brightness temperature as well as the reflective and thermal behavior of dust, the research results showed that the detection of dominant dust entering was 0.645 μm (visible red) and 0.858 μm (near‐infrared) in the reflective ranges and 3.959, 8.55, 11.03, and 12.02 µm in the thermal ranges. Synoptically, the lower values for mean sea level pressure from the east Mediterranean along Syria and Iraq to the southwest and Central Asia facilitate a convergence condition in the lower troposphere that induces strong northwesterlies, Shamal winds, over the Middle East toward the Persian Gulf, forming a more expansive aerosol hotspot over southwest Iran. However, on a cold day, high dust events in Arabia and south Iran are related to the ongoing high pressure, which is accompanied by a subtropical jet, following anticyclonic circulation toward southwestern Iran.

Mapping Forest Aboveground Biomass with MODIS and Fengyun-3C VIRR Imageries in Yunnan Province, Southwest China Using Linear Regression, K-Nearest Neighbor and Random Forest

The aboveground biomass (AGB) of a forest is an important indicator of the forest’s terrestrial carbon storage and its relation to climate change. Due to the advantage of extensive spatial coverage and low cost, coarse-resolution remote sensing data is the main data source for wall-to-wall mapping of forest AGB at the regional scale. Despite this, improving the accuracy and efficiency of forest AGB estimation is a major challenge. In this study, two optical imageries, Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m imagery and Fengyun-3C Visible and Infrared Radiometer (FY-3C VIRR) 1000 m imagery, were used and compared for forest AGB estimation in Yunnan Province, southwest China. One parametric approach, multiple linear regression (MLR), and two nonparametric approaches, k-nearest neighbor (KNN) and random forest (RF), were applied for the two imagery datasets, respectively. We evaluated the performance of the combination of remote sensing data and modeling approaches by comparing the accuracies and also explored the potential of FY-3C imagery data in forest AGB estimation at the regional scale as it was used for this purpose for the first time. We found that the machine learning models KNN and RF provided better results than MLR. From the three approaches for both MODIS and FY-3C imagery, RF performed best with R2 values of 0.84 and 0.81 and RMSE of 23.18 and 23.43, respectively. Estimation of forest AGB based on MODIS was marginally better than the estimation based on FY-3C. FY-3C imagery could therefore be an additional optical remote sensing data source of coarse spatial resolution, comparable to MODIS data which has been widely used for regional forest AGB estimation. Indices related to forest canopy moisture levels from both types of imagery were sensitive to forest AGB. The RF model and MODIS imagery were then applied to map the spatial variation of forest AGB of Yunnan Province. As a result of our study, we determined that Yunnan Province has a total forest AGB of 2123.22 Mt, with a mean value of 58.05 t/ha for forestland in 2016.

Spectrally simplified approach for leveraging legacy geostationary oceanic observations.

The use of multispectral geostationary satellites to study aquatic ecosystems improves the temporal frequency of observations and mitigates cloud obstruction, but no operational capability presently exists for the coastal and inland waters of the United States. The Advanced Baseline Imager (ABI) on the current iteration of the Geostationary Operational Environmental Satellites, termed the R Series (GOES-R), however, provides sub-hourly imagery and the opportunity to overcome this deficit and to leverage a large repository of existing GOES-R aquatic observations. The fulfillment of this opportunity is assessed herein using a spectrally simplified, two-channel aquatic algorithm consistent with ABI wave bands to estimate the diffuse attenuation coefficient for photosynthetically available radiation, Kd(PAR). First, an in situ ABI dataset was synthesized using a globally representative dataset of above- and in-water radiometric data products. Values of Kd(PAR) were estimated by fitting the ratio of the shortest and longest visible wave bands from the in situ ABI dataset to coincident, in situKd(PAR) data products. The algorithm was evaluated based on an iterative cross-validation analysis in which 80% of the dataset was randomly partitioned for fitting and the remaining 20% was used for validation. The iteration producing the median coefficient of determination (R2) value (0.88) resulted in a root mean square difference of 0.319m-1, or 8.5% of the range in the validation dataset. Second, coincident mid-day images of central and southern California from ABI and from the Moderate Resolution Imaging Spectroradiometer (MODIS) were compared using Google Earth Engine (GEE). GEE default ABI reflectance values were adjusted based on a near infrared signal. Matchups between the ABI and MODIS imagery indicated similar spatial variability (R2=0.60) between ABI adjusted blue-to-red reflectance ratio values and MODIS default diffuse attenuation coefficient for spectral downward irradiance at 490 nm, Kd(490), values. This work demonstrates that if an operational capability to provide ABI aquatic data products was realized, the spectral configuration of ABI would potentially support a sub-hourly, visible aquatic data product that is applicable to water-mass tracing and physical oceanography research.

FABIAN: A daily product of Fractional Austral-summer Blue Ice over ANtarctica during 2000–2021 based on MODIS imagery using Google Earth Engine

Antarctic blue ice areas are exposed due to erosion and sublimation of snow. At the same time, surface melt can form surface types that are spectrally similar to blue ice, especially at low elevations. These are termed melt-induced blue ice areas. Both types of blue ice are sensitive indicators of climate change. Satellite remote sensing is a powerful technique to retrieve the spatial extent of blue ice areas and their variation in time. Yet, existing satellite-derived blue ice area products are either mono-temporal for the entire Antarctic ice sheet, or multi-temporal for a limited area. Here, we present FABIAN, a product of blue ice fraction over Antarctica, derived from the moderate resolution imaging spectroradiometer (MODIS) archive covering the period 2000–2021. A spectral mixture analysis (SMA) in Google Earth Engine, based on a careful selection of endmember spectra, accurately reconstructs the reflectance observed by MODIS in blue ice areas. Based on a validation with contemporaneous Sentinel-2 images, FABIAN has a root mean square error in blue ice fraction of approximately 10% ∼ 20% in wind-induced blue ice areas, and 20% ∼ 30% in melt-induced blue ice areas across six selected test sites in the coastal East Antarctic ice sheet. FABIAN is challenged in regions with shallow melt streams and lakes, since their spectral profiles are similar to those from blue ice areas in MODIS bands. For further analyses and applications, FABIAN holds the potential for (1) deriving annual blue ice area maps, (2) distinguishing between wind-and melt-induced blue ice types, (3) evaluating and correcting (regional) climate models, and (4) analyzing temporal variations in blue ice abundance and exposure.

DSWEmod — The Production of High‐Frequency Surface Water Map Composites from Daily MODIS Images

AbstractOptical satellite imagery is commonly used for monitoring surface water dynamics, but clouds and cloud shadows present challenges in assembling complete water time series. To test whether the daily revisit rate of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery can reduce cloud obstruction and improve high‐frequency surface water mapping, we compared map results derived from Landsat (30‐m) and MODIS (250‐m) data across the state of California for 2003–2019. We adapted the Dynamic Surface Water Extent (DSWE) model in Google Earth Engine to generate surface water map composites from MODIS imagery every 5, 10, 15, and 30 days, and compared products to monthly Landsat‐based DSWE maps. Results for DSWEmod (DSWE MODIS) in California suggest that more than 5% data loss (cloud obstruction, etc.) was present in only 2% of the 15‐day time series, as compared to 32% of the monthly Landsat DSWE time series. The five‐day DSWEmod composites averaged 8.4% obscuration in the winter months. Area estimates derived from cloud‐filtered MODIS and Landsat monthly products have the highest linear correlations compared to streamgage discharge records, suggesting that monthly scale analyses best explain the relationship between surface water area and general streamflow dynamics. Shorter‐interval DSWEmod products have lower correlations but utility for understanding the timing of surface water peaks and past flood events.

A Cascaded Spectral–Spatial CNN Model for Super-Resolution River Mapping With MODIS Imagery

Rivers are important elements of the earth’s ecosystem, and their spatial distribution information is critical for the study of hydrological and biogeochemical processes. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery has been widely used for river mapping due to its high temporal resolution and long-term observation records, which are essential to capture the rapid fluctuation of rivers. However, when the conventional hard classification methods are used, the accuracy of the river maps produced from MODIS data (and especially for those rivers with narrow widths) is often limited because mixed pixels are common in MODIS imagery, due to coarse spatial resolution. In this article, a cascaded spectral–spatial information combined deep convolutional neural network (CNN) model for super-resolution river mapping (DeepRivSRM) is proposed to produce Landsat-like fine-resolution river maps from MODIS images. In DeepRivSRM, a CNN made up of a spectral unmixing module and a super-resolution mapping (SRM) module is introduced to handle the spectral and spatial information simultaneously. Moreover, for training the DeepRivSRM model, an adaptive cross-entropy loss function incorporating the fraction information of the rivers is designed to improve the performance of the DeepRivSRM model for small rivers. The proposed method was evaluated with MODIS images from three test sites and was compared with hard classification, a conventional SRM method, and a CNN-based SRM method. The results show that DeepRivSRM can generate more accurate river maps by effectively learning the subpixel-scale spatial–spectral information in MODIS imagery.

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

Analysis of long-term (2002–2020) trends and peak events in total suspended solids concentrations in the Chesapeake Bay using MODIS imagery

Water quality monitoring programs have been widely implemented worldwide to monitor and assess water quality and to understand its trends. However, water quality analysis based on point-source field observations is difficult to perform at large spatial and temporal scales. In this paper, a fully automated Google Earth Engine (GEE) application algorithm was developed to estimate the total suspended solids (TSS) concentration in the Chesapeake Bay based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra imagery. Combining long-term archived satellite data (2002–2020) with field observations, the concentrations and spatiotemporal patterns of TSS in the bay water were evaluated. Time series analysis showed a statistically significant decreasing trend in TSS concentration between 2002 and 2020, suggesting that the sediment concentration in the bay has gradually been decreasing over the last two decades. The decreasing trend was observed in 49 out of 60 segments of the bay, implying that substantial progress has been made toward attaining the Chesapeake Bay water quality standards. Based on the monthly TSS analysis, 12 major peak events of TSS were identified in the Chesapeake Bay, which coincided with extreme winter blizzards and summer hurricane events. The GEE application and the results presented herein complement the existing monitoring program in attaining the water quality standards of the bay.

Assessment of FSDAF Accuracy on Cotton Yield Estimation Using Different MODIS Products and Landsat Based on the Mixed Degree Index with Different Surroundings

Research on fusion modeling of high spatial and temporal resolution images typically uses MODIS products at 500 m and 250 m resolution with Landsat images at 30 m, but the effect on results of the date of reference images and the ‘mixed pixels’ nature of moderate-resolution imaging spectroradiometer (MODIS) images are not often considered. In this study, we evaluated those effects using the flexible spatiotemporal data fusion model (FSDAF) to generate fusion images with both high spatial resolution and frequent coverage over three cotton field plots in the San Joaquin Valley of California, USA. Landsat images of different dates (day-of-year (DOY) 174, 206, and 254, representing early, middle, and end stages of the growing season, respectively) were used as reference images in fusion with two MODIS products (MOD09GA and MOD13Q1) to produce new time-series fusion images with improved temporal sampling over that provided by Landsat alone. The impact on the accuracy of yield estimation of the different Landsat reference dates, as well as the degree of mixing of the two MODIS products, were evaluated. A mixed degree index (MDI) was constructed to evaluate the accuracy and time-series fusion results of the different cotton plots, after which the different yield estimation models were compared. The results show the following: (1) there is a strong correlation (above 0.6) between cotton yield and both the Normalized Difference Vegetation Index (NDVI) from Landsat (NDVIL30) and NDVI from the fusion of Landsat with MOD13Q1 (NDVIF250). (2) Use of a mid-season Landsat image as reference for the fusion of MODIS imagery provides a better yield estimation, 14.73% and 17.26% higher than reference images from early or late in the season, respectively. (3) The accuracy of the yield estimation model of the three plots is different and relates to the MDI of the plots and the types of surrounding crops. These results can be used as a reference for data fusion for vegetation monitoring using remote sensing at the field scale.

Quantifying the Response of German Forests to Drought Events via Satellite Imagery

Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.

From Standard Weather Stations to Virtual Micro-Meteorological Towers in Ungauged Sites: Modeling Tool for Surface Energy Fluxes, Evapotranspiration, Soil Temperature, and Soil Moisture Estimations

One of the benefits of training a process-based, land surface model is the capacity to use it in ungauged sites as a complement to standard weather stations for predicting energy fluxes, evapotranspiration, and surface and root-zone soil temperature and moisture. In this study, dynamic (i.e., time-evolving) vegetation parameters were derived from remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) imagery and coupled with a physics-based land surface model (tin-based Real-time Integrated Basin Simulator (tRIBS)) at four eddy covariance (EC) sites in south-central U.S. to test the predictability of micro-meteorological, soil-related, and energy flux-related variables. One cropland and one grassland EC site in northern Oklahoma, USA, were used to tune the model with respect to energy fluxes, soil temperature, and moisture. Calibrated model parameters, mostly related to the soil, were then transferred to two other EC sites in Oklahoma with similar soil and vegetation types. New dynamic vegetation parameter time series were updated according to MODIS imagery at each site. Overall, the tRIBS model captured both seasonal and diurnal cycles of the energy partitioning and soil temperatures across all four stations, as indicated by the model assessment metrics, although large uncertainties appeared in the prediction of ground heat flux, surface, and root-zone soil moisture at some stations. The transferability of previously calibrated model parameters and the use of MODIS to derive dynamic vegetation parameters enabled rapid yet reasonable predictions. The model was proven to be a convenient complement to standard weather stations particularly for sites where eddy covariance or similar equipment is not available.

Automated estimation of daily surface water fraction from MODIS and Landsat images using Gaussian process regression

ABSTRACT Satellite remote sensing has been widely used to monitor surface water, but its application in observing rapid inundation changes remains challenging. Observations relying on only one sensor could hardly achieve both high temporal and high spatial resolutions. High spatial resolution images are not frequent enough to capture the fast-changing inundation, while high temporal resolution images do not provide sufficient detail on spatial variations. To address this resolution compromise for rapid response, here we propose a new automated method to estimate daily sub-pixel water cover, the surface water fraction, from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images. First, water indices are derived from MODIS and Landsat images, and the Landsat derived water indices are used to generate a binary water map. We then aggregate the Landsat water map to the coarser MODIS resolution, making a surface water fraction map. Second, the relationship between the MODIS derived water indices and the Landsat derived surface water fraction is fitted using a Gaussian Process Regression (GPR) model. Lastly, the fitted GPR model is applied to new MODIS imagery to estimate surface water fraction. The experiment results showed that the proposed method could provide water fraction with Root Mean Squared Errors of less than 7%. By mapping fractional water cover automatically and regularly, the proposed method could facilitate daily emergency responses and long-term analyses.