Assessment of Irrigation Efficiency by Coupling Remote Sensing and Ground-Based Data: Case Study of Sprinkler Irrigation of Alfalfa in the Saratovskoye Zavolgie Region of Russia.

Nowadays, the leading role of data from sensors to monitor crop irrigation practices is indisputable. The combination of ground and space monitoring data and agrohydrological modeling made it possible to evaluate the effectiveness of crop irrigation. This paper presents some additions to recently published results of field study at the territory of the Privolzhskaya irrigation system located on the left bank of the Volga in the Russian Federation, during the growing season of 2012. Data were obtained for 19 crops of irrigated alfalfa during the second year of their growing period. Irrigation water applications to these crops was carried out by the center pivot sprinklers. The actual crop evapotranspiration and its components being derived with the SEBAL model from MODIS satellite images data. As a result, a time series of daily values of evapotranspiration and transpiration were obtained for the area occupied by each of these crops. To assess the effectiveness of irrigation of alfalfa crops, six indicators were used based on the use of data on yield, irrigation depth, actual evapotranspiration, transpiration and basal evaporation deficit. The series of indicators estimating irrigation effectiveness were analyzed and ranked. The obtained rank values were used to analyze the similarity and non-similarity of indicators of irrigation effectiveness of alfalfa crops. As a result of this analysis, the opportunity to assess irrigation effectiveness with the help of data from ground and space-based sensors was proved.

Spatial-Temporal Dynamics of Cropping Frequency in Hubei Province over 2001-2015.

Mapping crop patterns with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. In this paper, a hierarchical clustering method was proposed to map cropping frequency from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Indices (EVI) data and the spatial and temporal patterns of cropping frequency from 2001 to 2015 in Hubei Province of China were analyzed. The results are as follows: (1) The total double crop areas decreased slightly, while total single crop areas decreased significantly during 2001 and 2015; (2) The transfer between double crop and single crop was frequent in Hubei with about 11~15% croplands changed their cropping frequency every 5 years; (3) The crop system has obvious regional differentiation for their change trend at the county level.

Investigation of temporal change in glacial extent of Chitral watershed using Landsat data.

Glaciers are also known as solid reservoirs, and in this regard, Pakistan is a blessed country to have enriched glaciers. The change in glacial extent becomes very crucial for rivers whose discharges are associated with glacier melt. Even a little change in the glacial extent may bring a significant change in the resulting river flows. Considering climate change scenarios, many researchers have predicted future flows in such catchments. But in almost all such studies, the reduction in the glaciers is not normally based on any rational. Therefore, research is needed in order to estimate how glaciers are actually behaving under the change of temperature and precipitations to better estimate the future flows. For this purpose, Chitral watershed was considered as the study area. The seasonal change in the snow extent was estimated by using MODIS data for various years that helped to identify the month with minimum glacial extent. With the help of remote sensing, unsupervised classification was performed to estimate the glacier area in Chitral watershed. The results show a definite receding trend with respect to time in the glaciers of the region for the past decade.

Monitoring and estimating spatial-temporary land use changes of the Aegean region with remotely sensed data.

Natural resources are affected by parameters such as natural events, global warming, or irregular urbanization, and land use classes are changing. Faulty practices or wrong approaches to land uses can also lead to deterioration and destruction in the structure of land use classes. For this reason, it should be monitored regularly, and plans for the future should be made. Aegean region, covering 12% of Turkey, is one of the most important regions in terms of tourism, agriculture, and industry. Based on the previous research results, although the spatial change on the basis of cities has been examined, the spatial change of the entire Aegean region has not been examined so far and predictions for the future have not been made. This study aims to examine the change in land use classes of the Aegean Region between 2001 and 2019 using MCD12Q1.006 MODIS Land Cover Type Yearly Global 500 m data. In addition, an estimation study was made using these data for the year 2030. The results showed an increase in the urban area, forest, savannas, wetlands, and ice/snow between 2001 and 2019. On the other hand, a decrease was detected in agricultural areas, water bodies, grasslands, bare lands, and shrubs. Using the cellular automata (CA) method for estimation, first of all, the accuracy of the model was determined by estimating the year 2019. Then, using the same model, an estimation study was carried out for the year 2030. When the estimation results for 2030 are examined, an increase is detected in urban areas; it has been determined that there is a decrease in agricultural areas. This study has demonstrated the successful usability of MODIS data in spatial change estimation. In addition, the results obtained have revealed a comprehensive foresight that can be used in urban planning in order to ensure the sustainable development of the Aegean region in the future.

Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region.

Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (Tair) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time.

Is there any difference on cyanobacterial blooms patterns between Lake Chaohu and Lake Taihu over the last 20 years?

Serious cyanobacterial blooms (CBs) caused by lake eutrophication have become a global ecological and environmental problem and have adversely affected the production, life, and health of human beings. Lake Chaohu and Lake Taihu are two large closed shallow eutrophication lakes in the Yangtze River Delta in China with frequent CBs. In this study, the floating algae index (FAI) algorithm was applied to detect a long-time CBs sequence using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2019. The common characteristics and differences of the CBs patterns were further explored in both lakes over the last 20 years. The results showed that the severity of CBs in Lakes Chaohu and Taihu presented a similar trend of decreasing and then increasing during the period of 2000-2004 and 2005-2007, respectively. Although the severity of CBs in the two lakes was alleviated after 2008, CBs in Lake Taihu has gradually increased since 2011 and severe CBs broke out again in 2017 and 2019. Meanwhile, the CBs in Lake Chaohu have varied significantly in different years, and severe CBs were observed in 2012, 2014-2015, and 2018-2019, while in other years, CBs remained relatively low level. The high-frequency regions of CBs were mainly concentrated in the western part in Lake Chaohu and in Zhushan Bay and Meilian Bay in Lake Taihu in the initial years of 2000. However, since 2005, the CBs in Lake Chaohu gradually expanded to the central and eastern parts, and to the northwestern and western shore in Lake Taihu. Furthermore, the relationship between the monthly mean area of CBs (CBsmean) and environmental factors based on principal component analysis (PCA) indicated that temperature was the most important driving factor affecting CBs patterns. Compared to the period from 2001 to 2007, TP played a more important role in both lakes from 2008 to 2019. Various management measures have been adopted to reduce CBs in both lakes and these methods can effectively remove cyanobacteria in a short time, but they do not change CBs patterns in the long period.

Assessment of Spatio-Temporal Variation and Driving Mechanism of Ecological Environment Quality in the Arid Regions of Central Asia, Xinjiang.

Grassland ecosystems are increasingly threatened by pressures from climate change and intensified human activity, especially in the arid region of Central Asia. A comprehensive understanding of the ecological environment changes is crucial for humans to implement environmental protection measures to adapt to climate change and alleviate the contradiction between humans and land. In this study, fractional vegetation coverage (FVC), leaf area index (LAI), gross primary productivity of vegetation (GPP), land surface temperature (LST), and wetness (WET) were retrieved from Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing products in 2008 and 2018. Principal component analysis (PCA) was used to establish the MODIS data-based ecological index (MODEI) in the study area, and the spatial differentiation characteristics and driving mechanism of ecological quality in the last ten years were explored. The results showed that: (1) FVC, GPP, LAI, and WET had positive effects on the ecological environment, while LST had a negative impact on the ecological environment. FVC and GPP were more significant than other indicators. (2) The MODEI showed a spatial pattern of "excellent in the north and poor in the south" and changed from north to south in the study area. (3) From 2008 to 2018, the average MODEI of Fuyun County increased from 0.292 to 0.303, indicating that the ecological quality in Fuyun County became better overall. The improved areas were mainly located in the summer pastures at higher elevations. In comparison, the deteriorated areas were concentrated in the spring and autumn pastures and winter pastures at lower elevations. The areas where the ecological environment had obviously improved and degraded were distributed along the banks of the Irtysh River and the Ulungur River. (4) With the increase in precipitation and the decrease in grazing pressure, the MODEI of summer pasture was improved. The deterioration of ecological environment quality in spring and autumn pastures and winter pastures was related to the excessive grazing pressure. The more significant changes in the MODEI on both sides of the river were associated with implementing the herdsmen settlement project. On the one hand, the implementation of newly settled villages increased the area of construction land on both sides of the river, which led to the deterioration of ecological quality; on the other hand, due to the increase in cropland land and the planting of artificial grasses along the river, the ecological quality was improved. The study offers significant information for managers to make more targeted ecological restoration efforts in ecologically fragile areas.

Remote Sensing of Agro-droughts in Guangdong Province of China Using MODIS Satellite Data.

A practical approach was developed in the study for drought monitoring in Guangdong province of China on the basis of vegetation supply water index (VSWI) and precipitation distance index (PDI). A comprehensive index for assessment of agro-drought severity (SADI) was then established from the normalized VSWI and PDI. Using MODIS satellite images and precipitation data from ground-observed meteorological stations, we applied the approach to Guangdong for drought monitoring in 2006. The monitoring results showed that the drought severity on average was very low in the province during the main growing season from May to September in 2006. However, seasonal variation of the severity was also obvious in difference counties of the province. Higher severity of drought could be seen in the periods of late-June (In China each month is traditionally divided into 3 periods. Each is with 10 days and has different names. This division system is mainly with consideration of farming seasons hence has been widely used as the basis of drought monitoring periods in China. In order to keep this tradition, we define, for example, for June, the early-June as the period from 1st to 10th of June, the mid-June as the period from 11th to 20th, and the late-June as the period from 21st to 30th. So mid-August denotes the period from 11th to 20th of August, and early-July the period from 1st to 10th of July, and so on.), early-July, mid-August and late-September. Regionally, Leizhou Peninsula in the west had the most serious drought before mid-May. Validation indicated that our monitoring results were generally consistent with the drought statistics data and the results from Chinese National Satellite Meteorological Center (CNSMC), which used only remote sensing data. This consistence confirmed the applicability of our approach for drought monitoring. Our better identification of drought severity in Leizhou Peninsula of western Guangdong than that of CNSMC might suggest that the approach developed in the study was able to provide a better alternative to increase the accuracy of drought monitoring for agricultural administration and farming.

Validating the Association of Japanese Encephalitis Vector Abundance with Paddy Growth, Using MODIS Data.

Japanese encephalitis (JE) is one of the leading causes of viral encephalitis in Southeast Asia, particularly India. The major vector transmitting the disease, Culex tritaeniorhynchus, breeds in paddy field and its associated water bodies. The incidence of human infection usually occurs after the peak in vector abundance. Earlier, an association between JE vector abundance and paddy growth was demonstrated in Bellary district of Karnataka state, India, using radar satellite (RISAT 1) data. In this study, an attempt has been made to validate this phenomenon with the data collected from Uttar Pradesh state, using moderate resolution imaging spectroradiometer data.