Moderate Imaging Research Articles

Effect of viewing angle difference on spaceborne optical estimation of floating Ulva prolifera biomass in the Yellow Sea.

Optical remote sensing provides optimal technical support for the detection and quantification of floating macroalgae. Although the spatial scale effect on optical estimation of floating macroalgae coverage or biomass from different images has been clarified, the directional effect on them has not been investigated until now. In this study, synchronous multi-angle imaging spectroradiometer (MISR) and MODIS images were collected to investigate the multi-angle remote sensing of green tides. A dual thresholding method, based on the difference vegetation index (DVI) and scaled algae index, was employed to determine algae pixels. In addition, piecewise empirical models were developed for MISR and MODIS images to estimate the total biomass of green tides based on laboratory measurements and DVI values. Comparative analysis of DVI histograms and total biomass shows that the sensor zenith angle has a significant impact on the quantification of green tides. Under the same solar conditions, as the sensor zenith angle increases, the optical signals received from algae pixels weaken, resulting in a decrease in the quantification of green tides. In future research, the observation geometry (including the solar/sensor zenith angle and the solar/sensor azimuth angle) needs to be considered to improve the accuracy of optical remote detection and quantification of floating macroalgae.

Application of a regional climate model on autumn dust events over the Urmia Basin

Dust events have occurred over the Urmia Lake Basin in northwestern Iran for many years. In this study, we identified the mechanism and source of dust events in the region and assessed the ability of the Regional Climate Model (RegCM) to simulate these events. The observation data includes horizontal visibility and present weather phenomena achieved in Tabriz, Urmia, and Saqez stations. The ERA5, MERRA-2, and CAMS data were prepared for this research. The long-term dusty day analysis was conducted from 2000 to 2019. The synoptic-dynamic analysis and RegCM simulations were done during dust events on 28–31 October 2017—the HYSPLIT model and MODIS image usage to consider more detail. The results show that the Urmia Basin experiences two peaks, dusty days in the May and autumn months, including September and October. The persistent and widespread dust event in Urmia resulted from the approaching trough at 500 hPa to the north of Saudi Arabia and Iraq, trigged low pressure and increasing wind speed over the area. Based on the synoptic-dynamic analysis, MODIS satellite images, and the outputs of HYSPLIT, Iraq deserts were the primary source of dust on 28–31 October 2017. The dust distribution in the RegCM model, MERRA-2 data, and MODIS image agree. The RegCM aerosol optical depth (AOD) output time series agreed with MERRA-2 data, with a correlation coefficient of 0.84 and an index of agreement (IOA) of 0.56 in Urmia stations. The local dust source detected by the surface emission flux in the eastern Lake Urmia close to Tabriz can be a reason for the dustiest day in Tabriz. The AOD diurnal variation obtained from RegCM, MERRA-2, and CAM are similar in Tabriz and Urmia. The AOD of RegCM is underestimated compared to MERRA-2 and CAM in three selected stations.

Comprehensive study of algal blooms variation in Jiaozhou Bay based on google earth engine and deep learning

The Jiaozhou Bay ecosystem, a crucial marine ecosystem in China, has been plagued by frequent harmful algal blooms as due to deteriorating water quality and eutrophication. This study analyzed the temporal and spatial changes of harmful algal blooms in Jiaozhou Bay from 2000 to 2022 using the Floating Algae Index (FAI) calculated from MODIS (2000–2022) and Sentinel-2 (2015–2022) satellite image datasets. The calculation results of the image datasets were compared. The frequency of planktonic algal outbreaks was low and constant until 2017, but has increased annually since then. Algae blooms are most common in the summer and primarily concentrated along the bay’s coast, middle, and mouth, with obvious seasonal and spatial distribution characteristics. Several factors influencing algal outbreaks were identified, including sea surface temperature, wind speed, air pressure, dissolved oxygen, nitrogen and phosphorus ratios, chemical oxygen demand, and petroleum pollutants. Algal bloom outbreaks in Jiaozhou Bay are expected to remain high in 2023. The findings provide crucial information for water quality management and future algal outbreak prediction and prevention in Jiaozhou Bay.

Tracking the spatio-temporal change of the main food crop planting structure in the Yellow River Basin over 2001–2020

Accurate extraction of crop planting structure is the fundamental for estimating regional crop yield and crop water consumption in the Yellow River Basin (YRB). However, the planted area of the main food crops in the YRB in recent 20 years is still unclear. Therefore, the MODIS image data was used to extract the growing area of the main food crops (winter wheat, summer maize, spring wheat and spring maize) in the YRB over 2001–2020 by the combination of the differential algorithm, spectral mutation algorithm and threshold method. The results revealed that the combined method could be used to track the planted area of the main food crop in the YRB, with R2 and RMSE values ranged from 0.51 to 0.82 and 11.01 to 68.92 × 103 hm2, respectively. The planted area of winter wheat and summer maize in the down-stream irrigation district accounted for 74.7% and 86.7% of the total in the YRB, respectively. Inner Mongolia and Ningxia autonomous regions are the main producing areas for spring wheat and spring maize, accounting for 73.8% and 83.5% of the total in the YRB, respectively. The planted area of winter wheat, summer maize and spring maize had an increasing trend with the rate of 1.440 × 104 hm2 per year, 2.375 × 104 hm2 per year and 2.257 × 104 hm2 per year over 2001–2020. Whereas, the planted area of spring wheat had a decreasing trend with the rate of −2.019 × 104 hm2 per year. Implementation of water-saving irrigation measures, variations of diverted water from the Yellow River, local policies improvement and agricultural market might be the main driving forces for the changes of the planted area for food crops. The results can provide a fundamental agricultural information dataset for further study of regional crop water consumption in the YRB.

Spatiotemporal Features of the Surface Urban Heat Island of Bacău City (Romania) during the Warm Season and Local Trends of LST Imposed by Land Use Changes during the Last 20 Years

Using MODIS and Landsat LST images, the present paper advances a series of results on the characteristics of the surface heat island (SUHI) of Bacău City (Romania) during the warm season (April to September) for a period of 20 years (2001–2020). At the same time, given their higher temporal resolution and their availability for both day and night, MODIS LST was used to understand the spatial features of the SUHI in relation to land use. In this way, a total of 946 MODIS Terra and 483 Landsat satellite images were used to outline the main LST characteristics of the days with clear sky in this middle-sized city in northeast Romania. In order to analyze MODIS LST changes in relation to land use changes in the period 2001–2018, we used the standardized CORINE Land Cover datasets. With the help of the Rodionov test, we were able to determine the geometry and intensity of the SUHI. During the day, the spatial extension of the SUHI reaches its maximum level and is delimited by the isotherm of 31.0 °C, which is 1.5–2.0 °C warmer than the neighboring non-urban areas. During the night, the SUHI has a more regulated spatial extension around the central area of the city, delimited by the 15.5 °C isotherm with LST values that are 1.0–1.5 °C warmer than the surrounding non-urban areas. Additionally, from a methodological point of view, we highlight that resampled MODIS and Landsat images at a spatial resolution of 500 m can be used with confidence to understand the detailed spatial features of the SUHI. The results of this study could help the elaboration of future policies meant to mitigate the effects of urbanization on the SUHI in an era of increasing air temperatures during summer.

Investigating the effect of surface urban heat island on the trend of temperature changes

Urbanization has led to the emergence of surface urban heat islands (SUHI) and air pollution, necessitating comprehensive investigations into the urbanization process. This study explores the impact of urbanization on temperature trends in northern Iran using remote sensing and neural networks. MODIS images from 2001, 2010, and 2019 were employed to determine climatic features and vegetation levels. SUHI indices and the Urban Thermal Field Variance Index (UTFVI) were utilized to assess ecological comfort and urban heat islands. Markov chain methods were used for forecasting future thermal pollution. The relationship between temperature, air pollutants, spectral indices, and thermal indices was investigated through regression analysis. Furthermore, multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM) neural networks were employed for predicting indicator values. The findings reveal an increase in thermal pollution indicated by elevated Land Surface Temperature (LST), SUHI, and UTFVI values, along with a decrease in Normalized Difference Vegetation Index (NDVI) values between 2001 and 2019. The Urbanization Index (UI) values demonstrate a 5% increase in urban areas during the same period. The UTFVI values suggest a decline in ecological comfort due to urbanization and reduced vegetation, with the majority of the region experiencing the poorest thermal comfort in 2019. The SUHI index peaked at 11.72 in 2019, with the highest values observed in spring and the lowest in winter. Correlation analysis highlights a significant relationship between LST and various factors, including SUHI, UTFVI, CH4, SO2, and CO2, underscoring the influence of temperature increase on pollution escalation in the study area. Air pollution assessment indicates the highest concentration of CO gas in the northern and southern parts of the region. Tehran exhibits the highest CO gas levels at 0.051 ppm, while Golestan records the lowest at 0.012 ppm. SO2 levels reach their peak in northern regions (0.0088 ppm) and are lowest in southern regions (0 ppm). The Markov and CA-Markov chain methods predict that by 2050, approximately 50% of the region will experience high temperatures. Neural network results demonstrate that the LSTM method outperforms MLP and RBF methods, with R2 values of 0.99 and 0.98, respectively, indicating higher accuracy in LST prediction during testing and validation stages. Overall, this study highlights the potential of neural networks in predicting thermal pollution and facilitating effective land management strategies in urban areas.

Estimation Of Pelagic Fishing Area in Banten’s Southern Sea

The purpose of this research are to determine the average distribution of SST, chlorophyll-a, and see surface currents, and analyze the relationship between them with pelagic fish catches as an analysis material for determining pelagic fishing area in Banten’s Southern Sea. Data collected includes SST and chlorophyll-a using Aqua MODIS images data and and see surface currents data from Copernicus Marine Data for 6 years. The data was processed to produce the value of each parameter and then analyzed the parameters with the catches and determine the potential pelagic fishing area. The results showed that the SST throughout 2016 had a relatively high value allegedly due to the negative Indian Ocean Dipole (IOD). During the eastern until the end of second transitional season in 2019 there was a positive IOD which caused SST to decrease and chlorophyll-a to be larger than in other fishing seasons. See surface currents in the western season tend to be larger than in other fishing seasons. SST and chlorophyll-a have a stronger relationship with catches than see surface currents. The potential fishing area are around Tanjung Mantiyung, Muara Binuangeun, dan Tanjung Panto.

Pola Distribusi Spasial Dan Temporal Spesies <em>Lebtobrama Sp.</em> Dengan Menggunakan Teknologi Citra Satelit Aqua Modis Di Perairan Distrik Naukenjerai Kabupaten Merauke.

This study aims to determine the spatial and temporal variations of chlorophyll-a and SST by using fashionable satellite data in Naukenjerai District waters and the correlation of chlorophyll-a and SST to the distribution of species Lebtobrama Sp. in the Naukenjerai District. This research was conducted in May to June 2022 in the waters of the Naukenjerai District. MODIS image data analysis was performed at the Aquatic Resources Management Laboratory. The data used are data on the catch and the coordinates of the catch from May to June 2022. The results of this study indicate that the distribution of species Lebtobrama Sp. catches from May to June was 857 kg for 12 trips. While the range of chlorophyll-a in May to June ranged from 0.3 mg /m³ - 2.28 mg/m³ and the SPL range from May to June ranged between 25.42°C - 28.42°C. The results of the correlation between chlorophyll-a and species Lebtobrama Sp. catches showed a moderate relationship (correlation coefficient (r) 0.48), while the relationship between SPL and species Lebtobrama Sp. catches showed a strong relationship (correlation coefficient (r) 0.58) and the relationship of chlorophyll-a and SPL with the trapped catch shows a strong relationship (correlation coefficient (r) 0.59).

Determining fire frequency and its relationship with rangeland aboveground grass biomass using MODIS and Landsat imagery

ABSTRACT High frequency burned area (BA) images provide an opportunity to monitor rangeland fire frequency (FF). This study related grass aboveground biomass (AGB) to FF in a 4800 ha biodiversity conservation savannah-grassland rangeland. Archived (2000–2021), 500 m resolution MODIS burned area monthly images were used. Following Boolean coding (fire event pixel = 1, no-fire pixel = 0), addition GIS overlay analysis yielded total fires per pixel location. Fire detection accuracy was assessed using 2018–2021 management fire event records. Sample grass AGB data were obtained at the end of the 2020 and 2021 growing seasons from widely dispersed sampling sites with wide grass cover uniformity, where a 1 m quadrat was tossed randomly in a 20 m × 20 m plot up to three times. The quadrat-enclosed grass was harvested to soil level, air-dried, and weighed to generate site average AGB values, which were correlated with vegetation index (VI) values from sampling near-concurrent surface reflectance (L2SP) Landsat-8 OLI images. Four biomass-sensitive VIs utilising Landsat sensor spectral ranges were tested. The Enhanced Vegetation Index (EVI) yielded the strongest relationship (r = 0.410, p < 0.01). A linear model predicting grass AGB from EVI values was developed using 58% of sample data for training (R 2 = 0.3062, p < 0.01) and 42% for validation (R 2 = 0.5225, p < 0.001). Using the model, sampling site historical grass AGB values were predicted on same season, L2SP Landsat (TM, ETM+, OLI) images from 2000, 2002, 2006, 2009, 2013 and 2016, whose dates were selected by comparing rainfall. The MODIS images detected 73% of fires larger than 25 ha (one pixel). Most sites experienced long-term AGB gains, at faster rates in high FF (4–5 fires), low grazing sites. Most fires occurred as ecologically undesired late burns, indicating the utility of archived high-frequency BA images for rangeland management.

Utilization of MODIS Imagery to determine the rice growth stage in West Java Province

The estimation of lowland rice was not in accordance with the management of rice during the previous planting period. A method that can reach large rice fields quickly is needed to find out the phases of rice to maximize the management of paddy fields properly regardless of the condition of the rice. This study aimed to determine the planting phase of lowland rice based on its area indirectly, while the method used in this study was EVI based on MODIS images for the past ten years. The analysis of rice growth phases in West Java Province using EVI showed differences in rice growth phases in each district. This difference can be seen in the distribution of planting, heading, and harvest season. Vegetation showed that 26 districts in West Java had different planting areas, headings, and harvests each time. With the knowledge of the area, efforts are made so that the management of paddy fields should be based on the age of the rice at that time.

Sea Ice Velocimetry in Marginal Ice Zones from MODIS Images

Sea Ice Velocimetry in Marginal Ice Zones from MODIS Images

Temporal and Spatial Variation Analysis of Lake Area Based on the ESTARFM Model: A Case Study of Qilu Lake in Yunnan Province, China

Qilu Lake is one of the nine plateau lakes in Yunnan Province, China. In recent years, under the influence of extreme climate and human activities, the area of Qilu Lake has shrunk significantly, the water level has dropped, and the problem of water shortage has become increasingly serious. Based on the Landsat and MODIS image data from 2000 to 2020, this study applied the ESTARFM spatiotemporal fusion model to unify the data images used in the study to February, used three kinds of water body indexes, selected the water body index most suitable for the study area to extract relevant information, and analyzed the spatiotemporal change characteristics of Qilu Lake area in the last 20 years. The results showed that: (1) Based on the ESTARFM model, the Landsat and MODIS data on 18 January 2020, the Landsat and MODIS data on 9 May 2020, and the MODIS data on the date to be predicted (February 13) were fused to obtain the Landsat image data of the predicted date, which met the accuracy requirements; (2) Taking 2005 as an example, the NDWI, MNDWI, and AWEIsh indexes were used to extract the water body with the precisions of 99.0%, 99.6% and 98.6%, respectively, and then the MNDWI water body index was selected to extract the lake area; (3) In the past 20 years, the overall area of Qilu Lake has shown a downward trend, with the area reduced by 0.7132 km2. From 2000 to 2010, the lake area was relatively stable, fluctuating up and down around 36 km2. From 2010 to 2015, the lake area decreased sharply, with a change rate of −40%. After 2015, the lake area gradually increased; (4) The spatial change of Qilu Lake area mainly occurred in the southwest and west, which decreased by 0.44 km2 and 0.49 km2, respectively, and there were small fluctuations in other directions. In the past two decades, the shape index of Qilu Lake has shown a downward trend as a whole; the contour of the lake tends to be simplified, the contour is complex and stable from 2000 to 2010, and the shape index decreases from 2.17 to 1.74 from 2010 to 2020; (5) The change in the Qilu Lake area is positively correlated with the change in the water level. Polynomial models with different times were selected as the model for retrieving water level elevation from the Qilu Lake water surface area, with a highest correlation coefficient of 0.9259. The temporal and spatial changes of the Qilu Lake area in the last 20 years are the result of the joint action of natural factors and socio-economic factors. According to the analysis, the annual average temperature, annual precipitation, annual average sunshine hours, and population density are the main driving forces leading to the change. In the future, the government and relevant researchers should strengthen real-time monitoring and regular research, formulate and optimize emergency plans to deal with changes in the ecological environment of lakes, and promote the sustainable development of the ecological environment and social economy of the basin.

Monitoring land cover changes and droughts using statistical analysis and multi-sensor remote sensing data

Climate change is of paramount importance for ecosystems, especially in arid and semi-arid areas. The major aim of the current study is to monitor vegetation and land use changes, and run drought assessment using field and satellite data. The main precipitation proportions in the studied region are influenced by the Westerlies, meaning that any variations in these precipitation systems significantly impact the region. The utilized data entailed MODIS images for 16- and 8-day intervals between 2000 and 2013, TM and OLI sensor images recorded in 1985 and 2013, precipitation network data of TRMM satellite between 2000 and 2013, and synoptic data 32-year period. The Mann-Kendall (MK) test was used to monitor temporal changes in meteorological station data in annual and seasonal scales. The results indicated that there was a downward trend in 50% of the meteorological stations in the annual scale. This falling trend was statistically significant at the level of 95%. At the end, drought was assessed using PCI, APCI, VSWI, and NVSWI. The results showed that vegetation, forest, pasture, and agriculture areas recorded the strongest correlations with initial precipitation at the beginning of the study. Based on interactions among various factors influencing vegetation indices, reduction in green vegetation, especially the area of oak forests in the studied period, is around 95,744 hectares, which is attributed to lower precipitation rate. Increasing of agricultural land and water zones during the studied years is the result of human management and depends on how surface and underground water resources are exploited.

A remote sensing based method for assessing the impact of O3 on the net primary productivity of terrestrial ecosystems in China

O3 pollution in China has been increasing in recent years, but the process of O3 impact on net primary productivity of terrestrial ecosystems remains unclear. We attempts to explore a remote sensing-based method to assess the impact of O3 on NPP of China’s terrestrial ecosystems by combining MODIS NPP and the latest ground observation data of O3 concentration. By comparing the NPP data of MODIS image pixels with the 6-year average AOT40 data of corresponding pixels, we extracted the signal data that highlighted the effects of O3 on NPP and established the response relationships between AOT40 and NPP. It was found that NPP was significantly negatively correlated with AOT40 in farmland and grassland ecosystems in China (farmland: r = 0.8674, p &amp;lt; 0.003, grassland: r = 0.7181, p &amp;lt; 0.03). Then the response relationships were adopted to evaluate the effect in China in 2014. The results showed that the estimated percentage of O3-induced NPP decline was in the range of 5%–35%. Among them, the most significant declines were found in farmland ecosystems, with the vast majority of declines in 10%–35%. The decrease rate of evergreen coniferous forests ranked second, mostly in the range of 15%–20%. The grassland ecosystems declined at a lower rate, almost between 5% and 15%. And the evergreen broad-leaved forests has the lowest decline rate, most distributed in 0%–5%. The highest percentage decreases were mainly found in the Beijing-Tianjin-Hebei region and Shandong Province. And the decline rate of farmland ecosystems was significantly higher than other ecosystem types.

Spatiotemporal variation of long-term surface and vertical suspended particulate matter in the Liaohe estuary, China

The estuary and its nearshore waters are of great ecological value. As one of the important water quality evaluation indicators, suspended particulate matter (SPM) is also relevant to the ecology of the estuary. However, the current research on SPM of surface water is quite well established. It is urgent to study the vertical distribution of SPM due to its significance for further studies of biogeochemical processes in the water column. This study developed remote estimation model of SPM for five different depth water layers in the Liaohe estuary based on in situ Rrs(λ) data and support vector regression (SVR) methods, and the model performed well (RMSE = 24.32 mg/L, MAPE = 26.11%, MAE = 18.37 mg/L, N = 115). After applying the model to 10,090 MODIS images collected in 2000–2021, results show that: (1) Vertical distribution of SPM concentration in the Liaohe estuary could be divided into three types: top-down decreasing type (Type Ⅰ), uniform type (Type Ⅱ) and top-down increasing type (Type III). (2) Seasonal distribution of SPM concentration in the Liaohe estuary is: medium in spring (76.30 ± 10.34 mg/L), low in summer (61.80 ± 6.98 mg/L) and high in autumn (82.28 ± 19.12 mg/L). There is a significant decreasing trend of SPM from 2000 to 2021 (p < 0.01). (3) Meteorological factors (wind speed and precipitation), water depth, human reforestation and reclamation activities are closely related to SPM concentration.

Evaluation of Snowmelt Impacts on Flood Flows Based on Remote Sensing Using SRM Model

Snowmelt is an important source of stream flows in mountainous areas. This study investigated the impact of snowmelt on flooding. First, the study area was divided into four zones based on elevation. Second, the Snow-Covered Area (SCA) from 2013 to 2018 was estimated from daily MODIS images with the help of Google Earth Engine. Runoff in the area was then simulated using the Snowmelt Runoff Model (SRM). As a result, short periods with high runoff and the possibility of floods were identified, while the contribution of snowmelt and rainfall in the total runoff was separated. The results showed that while the snowmelt on average accounted for only 23% of total runoff in the zone with elevation under 2000 m, the ratio increased with elevation, ultimately reaching as high as 87% in the zone with elevation above 3000 m. As the height increases, the effect of snow on runoff and flooding increases so much that it should not be ignored. However, in most hydrological studies, the effect of snow is ignored due to the lack of sufficient data about snow. This study showed that snow can be very effective, especially in high areas.

Use of remote sensing data to predict soil organic carbon in some agricultural soils of Iran

Quantifying and monitoring of SOC (soil organic carbon) content in an agriculture soil is essential for understanding the ecological environment, influencing the climate change and implementing the best agriculture management. Present study tried to derive some machine learning-based models for predicting SOC content by employing remote sensing data in some agriculture soils enriched by calcareous materials, in central part of Iran. Soil samples were taken from 336 points at arable and permanent agriculture soils. Soil features such as SOC, sand, silt, clay, bulk density and calcium carbonate equivalent contents were measured at each point. After that, machine learning models including artificial neural networks (ANN), support vector regression (SVR) and genetic expression programming (GEP) were employed to estimate SOC content. The input variables were spectral indices extracted from Landsat-8, Sentinel-2, Sentinel-3 and MODIS images (Scenario I), integrating of the spectral indices extracted from Landsat-8—MODIS and Sentinel-2—Sentinel-3 images (Scenario II) and the spectral indices extracted from the fusing Sentinel-2 and Sentinel-3 images (Scenario IIII). The results of present study showed that the SVR model was the best performance (MAE = 0.0056%, RMSE = 0.620% and R2 = 0.627 at test data sets) for all sampled data by using the extracted remote sensing information at scenario II. Moreover, SVR model yielded the better result in the arable and permanent agriculture soils by using the extracted remote sensing information at scenario II (MAE = 0.0042%, RMSE = 0.672% and R2 = 0.574) and scenario III (MAE = 0.0032%, RMSE = 0.342% and R2 = 0.381), respectively. It was concluded that the combining of remote sensing data extracted from different types of satellites in a various method could improve the accuracy of SOC prediction.

Detection of hydrated dust storms on Modis images in western and southwestern Iran

Detection of hydrated dust storms on Modis images in western and southwestern Iran

PhenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data

Satellite-retrieved vegetation phenology has great potential for application in characterizing seasonal and annual land surface dynamics. However, obtaining regional-scale vegetation phenology from satellite remote sensing data often requires extensive data processing and computation, which makes the accurate and rapid retrieval of regional-scale phenology a challenge. To retrieve vegetation phenology from satellite remote sensing data, we developed an open-source tool called phenoC++, which uses parallel technology in C++. phenoC++ includes six common algorithms: amplitude threshold (AT), first-order derivative (FOD), second-order derivative (SOD), third-order derivative (TOD), relative change rate (RCR), and curvature change rate (CCR). We implemented the proposed phenoC++ and evaluated its performance on a site scale with PhenoCam-observed phenology metrics. The result shows that SOS derived from MODIS images by phenoC++ with six methods (i.e., AT, FOD, SOD, RCR, TOD, and CCR) obtained r-values of 0.75, 0.76, 0.75, 0.76, 0.64, and 0.67, and RMSE values of 21.36, 20.41, 22.38, 19.11, 33.56, and 32.14, respectively. Satellite-retrieved EOS by phenoC++ with six methods obtained r-values of 0.58, 0.59, 0.57, 0.56, 0.36, and 0.40, and RMSE values of 52.43, 46.68, 55.13, 49.46, 71.13, and 69.34, respectively. Using PhenoCam-observed phenology as a baseline, SOS retrieved by phenoC++ was superior to MCD12Q2, while EOS retrieved by phenoC++ was slightly inferior to that of MCD12Q2. Moreover, compared with MCD12Q2 on a regional scale, phenoC++-retrieved vegetation phenology yields more effective pixels. The innovative features of phenoC++ are 1) integrating six algorithms for retrieving SOS and EOS; 2) quickly processing data on a large scale with simple input startup parameters; 3) outputting phenology metrics in GeoTIFF format image, which is more convenient to use with other geospatial data. phenoC++ could aid in investigating and addressing large-scale phenology problems of the ecological environment.

Upscaling remote sensing inversion and dynamic monitoring of soil salinization in the Yellow River Delta, China

As a global problem of soil degradation, salinization has become a major obstacle to the sustainable development of the ecological environment and agriculture in coastal plains. However, the traditional process of salinity survey is too cumbersome, expensive and time-consuming to meet the mapping needs in a large scale. Remote sensing technology has become an important tool for digital soil mapping because of its rich sources, real-time and low cost. In order to meet the objective demand for rapid, accurate, and efficient acquisition and monitoring of soil salinization. This paper collected 61 soil samples from the Kenli District (experimental area) and extracted vegetation and salinity indicators from the Landsat image to construct the salinity inversion model by random forest algorithm. Then, taking the Yellow River Delta as the study area, the conversion coefficient of spectral indicators between Landsat and MODIS images was constructed in the form of the ratio of the mean value. Through optimization, the upscaling conversion method based on land use regionalization was proposed to realize the upscaling inversion and dynamic monitoring of soil salinization. The results showed that: (1) The random forest model based on NDVI, RVI, EVI, SI3, and SI5 can better predict the soil salinity in the experimental area, with R2 = 0.821 and RMSE = 2.811 (validation accuracy). (2) The upscaling conversion method based on land use regionalization can effectively reduce the statistical error and collinearity of spectral indicators constructed by MODIS images and improve their correlation with OLI data and soil salinity. (3) From coastal to inland, soil salinization gradually decreases in the Yellow River Delta. From 2000 to 2020, soil salinization increased first and then decreased, and the salinized soil accounted for 20.35%∼35.10%. This study used multi-source remote sensing data to realize the collaborative inversion at different scales, which was significant for the quantitative estimation of soil salinity, salinization control, and sustainable agricultural development in coastal plains.