Normalized Difference Vegetation Index Method Research Articles

Monitoring of toxic cyanobacterial blooms in Lalla Takerkoust reservoir by satellite imagery and microcystin transfer to surrounding farms

Cyanobacterial harmful algal blooms (CyanoHABs) threaten public health and freshwater ecosystems worldwide. In this study, our main goal was to explore the dynamics of cyanobacterial blooms and how microcystins (MCs) move from the Lalla Takerkoust reservoir to the nearby farms. We used Landsat imagery, molecular analysis, collecting and analyzing physicochemical data, and assessing toxins using HPLC. Our investigation identified two cyanobacterial species responsible for the blooms: Microcystis sp. and Synechococcus sp. Our Microcystis strain produced three MC variants (MC-RR, MC-YR, and MC-LR), with MC-RR exhibiting the highest concentrations in dissolved and intracellular toxins. In contrast, our Synechococcus strain did not produce any detectable toxins. To validate our Normalized Difference Vegetation Index (NDVI) results, we utilized limnological data, including algal cell counts, and quantified MCs in freeze-dried Microcystis bloom samples collected from the reservoir. Our study revealed patterns and trends in cyanobacterial proliferation in the reservoir over 30 years and presented a historical map of the area of cyanobacterial infestation using the NDVI method. The study found that MC-LR accumulates near the water surface due to the buoyancy of Microcystis. The maximum concentration of MC-LR in the reservoir water was 160 µg L−1. In contrast, 4 km downstream of the reservoir, the concentration decreased by a factor of 5.39 to 29.63 µgL−1, indicating a decrease in MC-LR concentration with increasing distance from the bloom source. Similarly, the MC-YR concentration decreased by a factor of 2.98 for the same distance. Interestingly, the MC distribution varied with depth, with MC-LR dominating at the water surface and MC-YR at the reservoir outlet at a water depth of 10 m. Our findings highlight the impact of nutrient concentrations, environmental factors, and transfer processes on bloom dynamics and MC distribution. We emphasize the need for effective management strategies to minimize toxin transfer and ensure public health and safety.

Spatio-temporal changes of rice cropping system in Anhui Province from 2010 to 2020

Monitoring changes in rice production is of significant importance for addressing food security concerns in China. However, obtaining long-term, high-resolution rice planting maps is challenging due to the influence of cloud cover and temporal resolution limitations. This study, conducted on the Google Earth Engine (GEE) platform, utilizes Landsat and Sentinel data and employs the difference in normalized difference vegetation index method (dNDVI) to extract the distribution in rice cultivation systems in Anhui Province over the past decade. The results indicate that, between 2010 and 2020, the area of single cropping rice cultivation in Anhui Province increased by 12513.612 km², while the area of double cropping rice cultivation decreased by 13527.892 km². The rice replanting index declined by 31%. With a notable shift observed in 2015, where a substantial area of double cropping rice was replaced by single cropping rice cultivation. Consequently, over the past decade, there has been a gradual decrease in the rice cultivation area in Anhui Province, accompanied by a significant shift from double to single cropping cultivation, and a continuous decline in the replanting index. This research provides vital clues for a deeper understanding of the rice planting pattern, offering scientific support for the sustainability of food production.

Application of Sentinel-2A Images for Land Cover Classification Using NDVI in Jember Regency

The advancement of remote sensing technology has led to the development of sophisticated image processing methods that yield highly accurate land cover classification information, minimizing misinterpretations. The Normalized Difference Vegetation Index (NDVI) is a widely utilized method in remote sensing for measuring green vegetation. A significant portion of the Jember Regency area is covered by vegetation. This study aimed to identify various land cover types in the Jember Regency area, quantify the area for each classification, and establish the NDVI value ranges for each type of cover. Sentinel-2 was employed as the primary data source, and the NDVI method was utilized for land cover classification in the Jember Regency. The region exhibited diverse land cover types. Data from Sentinel-2A captured in June and October 2019 were chosen due to their accessibility, open-source nature, and adequate spectral, spatial, and temporal resolution. The classification in this study encompassed five classes: water bodies, settlements, dry fields, irrigated paddy fields, and forests. Error analysis was conducted using a confusion matrix with the Overall and Kappa algorithms. The accuracy results for June indicated a Kappa Accuracy of 37.7% and Overall Accuracy of 54.5%. In October, the Kappa Accuracy increased to 39.9%, and the Overall Accuracy reached 56.5%. In conclusion, the NDVI method did not meet the criteria for accurately interpreting land cover classification.

Analysis of Changes in Mangrove Vegetation Area Using The Normalized Difference Vegetation Index Method in The Waters of Bungin Permai, South Konawe

Changes around mangrove vegetation in Bungin Permai Waters, Tinanggea District, South Konawe Regency over the last 30 years (1992-2022). Aims to analyze changes in mangrove vegetation areas spatially and temporarily. The research method used remote sensing of Landsat 5 TM, 7 ETM +, and Landsat 8 OLI satellite image data and NDVI method to see changes in mangrove vegetation area, reduced mangrove vegetation area by 75% ranging from 767 Ha. Shows that, along with the reduction in mangrove vegetation area can cause an increase in TSS concentration, the potential for sea level rise to settlements, the absence of sedimentation rate retaining media to the high seas, to the loss of mangrove area animal habitat. The area of mangrove vegetation decreases by 75% or around 767 Ha over time.

Investigation of Meteorological Effects on Çivril Lake, Turkey, with Sentinel-2 Data on Google Earth Engine Platform

Lakes and reservoirs, comprising surface water bodies that vary significantly seasonally, play an essential role in the global water cycle due to their ability to hold, store, and clean water. They are crucial to our planet’s ecology and climate systems. This study analyzed Harmonized Sentinel-2 images using the Google Earth Engine (GEE) cloud platform to examine the short-term changes in the surface water bodies of Çivril Lake from March 2018 to March 2023 with meteorological data and lake surface water temperature (LSWT). This study used the Sentinel-2 Level-2A archive, a cloud filter, the NDVI (normalized difference vegetation index), NDWI (normalized difference water index), MNDWI (modified NDWI), and SWI (Sentinel water index) methods on lake surfaces utilizing the GEE platform and the random forests (RFs) method to calculate the water surface areas. The information on the water surfaces collected between March 2018 and March 2023 was used to track the trend of changes in the lake’s area. The seasonal (spring, summer, autumn, and winter) yearly and monthly changes in water areas were identified. Precipitation, evaporation, and temperature are gathered meteorological parameters that impact the observed variation in surface water bodies for the same area. The correlations between the lake area reduction and the chosen meteorological parameters revealed a strong positive or negative significant association. Meteorological parameters and human activities selected during different seasons, months, and years have directly affected the shrinkage of the lake area.

Mapping and Change Assessment of Captive Limestone Mining Areas Using Landsat-5/8 Images

Limestone is a non-metallic mineral extensively used in cement manufacturing and construction sector. Extensive mineral mining processes impact the environment. The study aims to map and evaluate the limestone mining area change at the Yerraguntla industrial zone in the YSR district of Andhra Pradesh, India. The normalized difference vegetation index (NDVI) and modified soil-adjusted vegetation index (MSAVI) are computed from the Landsat-5/8 images using Quantum GIS (QGIS) software. Experimental results show that the limestone mining area increases from 307 ha to 469.92 ha during 2005-2019. NDVI method is more effective than MSAVI in change assessment of limestone mining areas with overall accuracy of 87.75 % and 79.49 % and kappa coefficient of 0.89 and 0.62 respectively in 2019. The finding is compared with industry field survey reports (487.10 ha). This study contributes to the limestone mining industry management in developing a land-environmental management plan for the long-term sustainability of limestone mining.

Space monitoring in precision agriculture

The article studies the practice of using space monitoring tools in the agro-industrial complex. It is shown that the use of space images is an element of an integrated high-tech agricultural management system in precision farming. The article considers the possibilities of applying the Normalized difference vegetation index method in order to increase the productivity of agricultural land, improve the processes of managing the agricultural complex, determine the limiting factors of sustainable development and predict yields.

ESTIMATION OF LAND SURFACE TEMPERATURE IN BUNGO DISTRICT USING THERMAL CHANNELS OF LANDSAT 8 IMAGES

The purpose of this study was to determine the land surface temperature in Bungo Regency using the Landsat 8 image thermal channel by carrying out three stages: (1) Mapping the comparison of vegetation density in 2016 and 2021 using the NDVI (Normalized Difference Vegetation Index) method. (2) Mapping land surface temperatures in 2016 and 2021 using the Land Surface Temperature method. (3) Knowing the relationship between LST and NDVI using the Correlation Person test. The results of the study explain the comparison of vegetation density using the Normalized Difference Vegetation Index (NDVI) method in 2016 and 2021 in Bungo Regency. In 2016 the classification is very dense with an area of ​​124,871 Ha, the classification dense with an area of ​​115,732 Ha, the classification medium with an area of ​​98,536 Ha, the classification is rare with an area of ​​71,920 Ha, and very rare classification with an area of ​​54,839 Ha. Whereas in 2021 the very dense classification will decrease to 117,216 Ha, the dense classification will decrease to 112,365 Ha, the moderate classification will decrease to 95,892 Ha, the rare classification will increase to 79,310 Ha, and the very rare classification will increase to 61,084.

UTILIZING LANDSAT 8 IMAGERY FOR MAPPING OF BURNED AREAS USING THE NORMALIZE DIFFERENCE VEGETATION INDEX (NDVI) AND NORMALIZE BURN RATIO (NBR) METHODS

This study aims (1) to map changes in the area of ​​forest land in the western part of Bengkalis Regency in 2016 and 2021, (2) to determine the distribution of the area of ​​forest burned in the western part of Bengkalis Regency, (3) to determine the severity of forest fires in the District of Bengkalis West Bengkalis.This study used the NDVI (Normalized Difference Vegetation Index) method by Huete et. Al by compositing band 5 (NIR) and band 4 (Red) on Landsat 8 imagery which was processed using ArcGIS software before and after a forest fire. As well as the NBR (Normalized Burn Ratio) and dNBR (Difference Normalized Burn Ratio) methods by Eidenshink et al by compositing band 5 (NIR) and band 7 (SWIR) on Landsat 8 images processed using QGIS software. For sampling using random sampling method and accuracy test using overall accuracy, user's accuracy, producer's accuracy, and kappa analysis. The results of this study are (1) the area of ​​forest land in Bengkalis Regency continues to decrease every year, in 2016 the area of ​​forest land903,920 ha and 2021 the total forest area is463,441 ha. (2)The area of ​​forest land burned due to forest fires in Bengkalis Regency, which burned the least was 267.43 ha, while it was 1468.93 ha and the most extensive was 2186.53 ha.(3) Based on one forest fire distribution map, it is divided into 7 fire severity classes, namely high post-fire regrowth, low post-fire regrowth, no burning, low, medium-high and very high and the most dominant forest fire level is low-high.

The impact of traditional gold mining on land use changes and vegetation index in Mandor Subwatershed, West Kalimantan

Traditional gold mining activities altered the environmental structure of the Mandor Subwatershed significantly. The expansion of critical land in the Mandor Subwatershed causes flooding due to the lack of water catchment areas. The purpose of this study was to identify the impact of traditional gold mining on land use change in the Mandor Subwatershed. The research was conducted with a spatial analysis approach using Landsat multitemporal images from 2002, 2013, and 2022, followed by a field survey. A comparison of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) methods was used to determine the changing process of vegetation density. The accuracy of vegetation index analysis indicated that the EVI method was more accurate for identifying vegetation density than the NDVI method. Land use change from 2002 to 2022 was dominated by an increase in the land area devoted to mining and oil palm plantations. The impact of this traditional gold mining has led to significant deforestation and land degradation over the past 20 years in the Mandor Subwatershed. This affects the condition of the surrounding environment as well as human health.

Vegetation dynamics of Sangkub watershed in North Sulawesi Province indicated by NDVI of Landsat data

<p>Vegetation can be an important indicator of ecosystem change, the influence of anthropogenic and non-anthropogenic factors. Therefore, it is important to study the dynamics of vegetation changes. One technique that can be used for vegetation analysis is the Normalized Difference Vegetation Index (NDVI). NDVI is an indicator of the active biomass that helps distinguish vegetation from other land cover and can provide information about changes in Spatio-temporal vegetation dynamics, thus allowing for assessment of the ecological conditions. This study aimed to investigate the dynamic of vegetation in the Sangkub watershed area located in North Sulawesi Province. This analysis used geospatial technology with the NDVI method, utilizing Landsat 5 and Landsat 8 satellite imagery data in three periods the year 2000, 2015, and 2020. The results showed that vegetation cover of the Sangkub watershed decreased substantially, whereas the non-vegetated area increased gradually over time.</p>

Remote Sensing Monitoring of Changes in Lake Aquatic Vegetation before and after the Removal of the Fence Based on Sentinel-2: A Case Study in Lake Futou, Hubei Province

Aquatic vegetation is an important component and main primary producer of lake ecosystems and plays an important role in improving water quality and maintaining biodiversity, which is critical to diagnosing the health of aquatic ecosystems in shallow lakes. It is therefore important to accurately obtain information on dynamic changes and spatial-temporal distribution of aquatic vegetation. Based on the Sentinel-2 satellite remote sensing images from 2016–2022, we studied the feasibility of using remote sensing technology to monitor the spatial-temporal changes of aquatic vegetation before and after the removal of the fence, taking Futou Lake in Hubei Province as a case study. Two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Submerged Aquatic Vegetation Index (SAVI) were applied to identify the open water and the aquatic vegetation through two threshold determination methods, Otsu algorithm and manual division threshold method. The results show that: (1) the classification based on the NDVI and manual division threshold method performs the best, with the overall classification accuracy of 94.44% and the Kappa coefficient of 85.23%. (2) The growth of aquatic vegetation is divided into stages, the first stage is enclosing culture, and the distribution of aquatic vegetation is less in 2016–2017, all around 10 km2. The second stage is after the removal of the fence, the distribution area of aquatic vegetation in 2018 is on an upward trend, and in 2019–2022 it is growing rapidly. (3) Spatially, the aquatic vegetation was mainly distributed at the former fence, specifically in the northeastern and southwestern waters of the Futou Lake and it spread to the core area of the lake, probably due to the elevation of the siltation of the lake bottom. (4) Potamogeton crispus and Trapa are the dominant species, the peak of the distribution range in Futou Lake occurs in 2021 with an area of about 50.89 km2, which needs to be controlled moderately. (5) The area covered by Potamogeton crispus in the Futou Lake has increased significantly, probably due to the siltation and accumulation of nutrients in the Futou Lake caused by the history of purse seine farming.

MAPPING THE DISTRIBUTION OF MANGROVE BY REMOTE SENSING IN THE COASTAL OF KARAWANG REGENCY

Mangroves are forest vegetation that grows between tidal lines so that mangrove forests are also called tidal forests. Mangrove ecosystems have high biodiversity. The mangrove ecosystem has a physical function to protect the coast from abrasion and erosion, this ecosystem also has ecological and economic value. The purpose of this study was to determine the condition and extent of mangrove distribution on the coast of Karawang Regency. The research was carried out on the coast of Karawang Regency from March to June 2021. The data used is primary data by directly observing the research location and secondary data using remote sensing data from the Landsat 8 satellite. Image data processing and analysis is carried out using the NDVI (Normalized Difference Vegetation Index) method which can create a boundary between mangrove and non-mangrove. With this method, it can be seen the changes in the mangrove distribution area from year to year. The results of data processing and analysis show that in 2013 the distribution of mangroves on the coast of Karawang Regency has an area of ​​207.88 km2, in 2016 it has an area of ​​357.22 km2 and in 2020 its area is 237.61 km2.

Changes in Land cover using the NDVI (Normalized Difference Vegetation Index) Method in Kedamaian Subdistrict, Bandar Lampung City as Urban City

Land cover change occurs naturally, but in urban areas, Land cover change usually occurs more rapidly than in rural areas. Therefore, it is necessary to analyse Land cover changes using spatial data to determine Land cover changes that occurred in urban areas such as Bandar Lampung City in 2000 and 2020. This study uses Landsat 7 and 8 imagery processed by the NVDI (Normalized Difference Vegetation Index) analysis method with an arithmetic formula to determine the vegetation index NDVI = (NIR-RED) / (NIR + RED). After obtaining the land cover map from the results of the NDVI analysis, a ground check was carried out at 30 location points to determine the actual condition of the field and calibrate the data from NDVI analysis and actual condition. In this study, it was found that the results of Land cover from 2000 to 2020 experienced changes, namely in open land/settlements increased by 74% or 217 ha, shrub land decreased by 27% or 32 ha, low vegetation land decreased by 4.8% or 7 ha, and vegetation land is decreasing by 72.5% or 100 ha. Based on the results of ground checks, changes in Land cover are caused by the construction of settlements, housing, and industrial estates due to an increase in population and urban growth.

Estimating fractional coverage of crop, crop residue, and bare soil using shortwave infrared angle index and Sentinel-2 MSI

ABSTRACT Accurate estimations of soil cover parameters (fractional coverage of crop residue [f cr], crop [f c], and bare soil [f bs]) in croplands can assist in decision-making for agricultural management organisations and are important for understanding the interrelations between global changes and terrestrial ecosystems. In recent decades, optical remote sensing has been widely used for mapping f c, f cr, and f bs. The crop residue and soil spectra decrease rapidly with increasing cropland moisture primarily because of water absorption in the near-infrared and shortwave infrared bands. This leads to a decrease in the denominator of current normalized crop residue spectral indices (shortwave infrared [SWIR] band 1 + band 2), thereby causing them to increase with increasing cropland moisture. This study revealed that the slopes of the soil and crop residue spectra between SWIR1 and SWIR2 were less affected by cropland moisture changes. Based on this feature, we propose a shortwave infrared angle (SWIRA) index and jointly use it with normalized difference vegetation index (NDVI) to estimate and produce multi-temporal maps of f c, f cr, and f bs using broadband Sentinel-2 MSI images. The proposed SWIRA is the slope of the spectrum between SWIR1 and SWIR2. This study (i) uses laboratory-based spectral reflectance to evaluate the performance of the SWIRA and (ii) further evaluates the performance of the SWIRA–NDVI method using multi-temporal Sentinel-2 MSI images. Our results indicate that (i) cropland moisture has a considerably smaller effect on SWIRA than it does on current broadband normalized crop residue spectral indices, and (ii) maps of the estimated f c, f cr, and f bs values can be used to improve crop growth and decision-making, for example, by providing harvest maps and soil tillage intensity maps.

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.

Spatio-temporal evolution of forest in Târgu Lăpuș, northern Transylvania, Romania, using Landsat satellite data

The use of remote sensing indices in order to obtain information for vegetation distributions, heath and patterns is a popular practice with applicability at different scales of detail. The present article presents an example of using the Normalized Difference Vegetation Index (NDVI) and Change Detection method to identify the spatio temporal evolution of forest area in Târgu Lăpuș city, northern Transylvania, Romania. NDVI method is applied at different NDVI threshold values such as 0.1, 0.15, 0.3, 0.45 and 0.6. For the analyzed area, an area of 1.56 km2 with forest gain and 3.59 km2 with forest loss was identified. The advantage of using this method is the large time gap for which it can be done, in our situation being an analysis for the period 1983-2020 so that we can identify the trend of evolution of areas occupied by forests in a city where spatially built it often extends uncontrollably, affecting the degree of compactness of the forests and implicitly their protection functions.

NDVI Based Assessment of Land Cover Changes Using Remote Sensing and GIS (A case study of Srinagar district, Kashmir)

Normalized Difference Vegetation Index (NDVI) is an index of greenness or photosynthetic activity in a plant. It is a technique of obtaining various features based upon their spectral signature such as vegetation index, land cover classification, urban areas and remaining areas presented in the image. The NDVI differencing method using Landsat thematic mapping images and Landsat oli was implemented to assess the chane in vegetation cover from 2001to 2017. In the present study, Landsat TM images of 2001 and landsat 8 of 2017 were used to extract NDVI values. The NDVI values calculated from the satellite image of the year 2001 ranges from 0.62 to -0.41 and that of the year 2017 shows a significant change across the whole region and its value ranges from 0.53 to -0.10 based upon their spectral signature .This technique is also used for the mapping of changes in land use and land cover. NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as -0.1, -0.09, 0.14, 0.06, 0.28, 0.35, and 0.5. The NDVI values were initially computed using the Natural Breaks (Jenks) method to classify NDVI map. Results confirmed that the area without vegetation, such as water bodies, as well as built up areas and barren lands, increased from 35 % in 2001 to 39.67 % in 2017.Key words: Normalized Difference Vegetation Index,land use/landcover, spectral signature

Analisis Perubahan Penggunaan Lahan Menggunakan Metode NDVI (Normalized Difference Vegetation Index) pada Kecamatan Natar, Kabupaten Lampung Selatan

Natar District is one of the districts in South Lampung Regency which has an area of 213.77 km2 or around 21,377 HA. In the agricultural sector, most of the land in Natar District is dominated by maize and paddy fields. This study aims to determine changes in land use in 2002, 2009 and 2019 in Natar District, South Lampung Regency. This study uses imagery from Landsat 7 and 8 processed in the NDVI (Normalized Difference Vegetation Index) method with the formula "NDVI = (NIR-RED) / (NIR + RED)". After processing the data, field observations were made to 30 sample points which were spread evenly throughout the Natar District. In this study, the results showed that land conversion to open land increased every year, on the other hand the area of land with low to moderate vegetation density decreased every year. In field observations, it was found that various land uses ranging from settlements, markets, and various uses for agricultural and plantation land. Open land in Natar District in 2019 increased by 32.7% from 2002, on the other hand, low and moderate vegetation land has decreased by 32.5% and 12.5% respectively since 2002. The government must tighten environmental regulations regarding land which can be converted or not. If population growth becomes uncontrolled, the conversion of agricultural land functions increases, it will be impossible for Indonesia to realize national food security

Hourly remote sensing monitoring of harmful algal blooms (HABs) in Taihu Lake based on GOCI images.

The increasingly serious harmful algal blooms (HABs) in Taihu Lake has brought huge losses to the local economy and people's life in Taihu Lake. Satellite remote sensing technology has become one of the most important monitoring methods for HAB disasters due to its large-scale and long-term advantages. GOCI image has become the new data source of HAB monitoring because of its large size and high time resolution. Due to the low spatial resolution (500 m) and the existence of mixed pixels, the error of HAB area obtained by the NDVI method is large. In this paper, the linear mixing model (LMM) and the normalized difference vegetation index (NDVI) threshold method are combined to extract the HAB area from GOCI images with 500-m spatial resolution. Compared with the results of the HAB area extracted by Landsat8 OLI and MODIS data, three small areas in the study area were selected to verify the accuracy of the HAB area extracted from the GOCI image on October 2, 2015. The results show that when the NDVI threshold is 0.1, the area error of HABs is the smallest when the extracted HAB pixels mask the decomposition results of mixed pixels; besides, the area error of HABs extracted from the GOCI image is smaller than that from MODIS image; finally, GOCI image can extract the spatial dynamic distribution of HABs in Taihu Lake within 8 h a day, which has higher temporal resolution than the MODIS image. Compared with the NDVI threshold method and LMM method, the inversion accuracy is greatly improved, and the accuracy is stable in different regions. It can provide technical support for the decision-making and assessment of HAB ecological disasters.