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The phenomenon of drought is one of the most significant environmental and climatic extreme events; it is more complex in terms of measuring, monitoring, and identifying the possible effects and hazards associated. The remote sensing index, which included the Vegetation Health Index (VHI), was used to describe the geographic and temporal distribution of the springtime agricultural drought. VHI can be calculated based on the Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Normalized Difference Vegetation Index (NDVI) derived from MODIS Terra satellite data. The outcomes showed that the droughts in the years 2001–2002, 2008–2009 were the most extreme in twenty-two years.  The drought years 2001-2002 were followed by the drought-free years 2002/2003,2008/2009. The years 2017-2018 show this phenomenon distributed mostly in the south and southwest bearing. According to the findings, the southwest and western regions are more vulnerable to drought. The results of the drought index VHI indicate that the country is facing non-uniform cycles of drought for all types of droughts. Also, the results showed that there is a negative significance between rainfall and extreme, severe, and moderate drought, even if there was no substantial negative link between rainfall and dryness. It is recommended to adopt the VHI index to monitor the desiccation of vegetation in semi-arid areas with little access to surface meteorological information.

Large areas of the province of Punjab, Pakistan are endemic for fascioliasis, resulting in high economic losses due to livestock infection but also affecting humans directly. The prevalence in livestock varies pronouncedly in space and time (1-70%). Climatic factors influencing fascioliasis presence and potential spread were analysed based on data from five meteorological stations during 1990-2010. Variables such as wet days (Mt), water-budget-based system (Wb-bs) indices and the normalized difference vegetation index (NDVI), were obtained and correlated with geographical distribution, seasonality patterns and the two-decade evolution of fascioliasis in livestock throughout the province. The combined approach by these three indices proved to furnish a useful tool to analyse the complex epidemiology that includes (i) sheep-goats and cattlebuffaloes presenting different immunological responses to fasciolids; (ii) overlap of Fasciola hepatica and F. gigantica; (iii) co-existence of highlands and lowlands in the area studied; and (iv) disease transmission following bi-seasonality with one peak related to natural rainfall and another peak related to man-made irrigation. Results suggest a human infection situation of concern and illustrate how climate and anthropogenic environment modifications influence both geographical distribution and seasonality of fascioliasis risks. Increased fascioliasis risk throughout the Punjab plain and its decrease in the northern highlands of the province became evident during the study period. The high risk in the lowlands is worrying given that Punjab province largely consists of low-altitude, highly irrigated plains. The importance of livestock in this province makes it essential to prioritise adequate control measures. An annual treatment scheme to control the disease is recommended to be applied throughout the whole province.

Vegetation indices provide information for various precision-agriculture practices, by providing quantitative data about crop growth and health. To provide a concise and up-to-date review of vegetation indices in precision agriculture, this study focused on the major vegetation indices with the criterion of their frequency in scientific papers indexed in the Web of Science Core Collection (WoSCC) since 2000. Based on the scientific papers with the topic of “precision agriculture” combined with “vegetation index”, this study found that the United States and China are global leaders in total precision-agriculture research and the application of vegetation indices, while the analysis adjusted for the country area showed much more homogenous global development of vegetation indices in precision agriculture. Among these studies, vegetation indices based on the multispectral sensor are much more frequently adopted in scientific studies than their low-cost alternatives based on the RGB sensor. The normalized difference vegetation index (NDVI) was determined as the dominant vegetation index, with a total of 2200 studies since the year 2000. With the existence of vegetation indices that improved the shortcomings of NDVI, such as enhanced vegetation index (EVI) and soil-adjusted vegetation index (SAVI), this study recognized their potential for enabling superior results to those of NDVI in future studies.

The present study aims to assess the trend of spatiotemporal relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) under different ranges of LST and NDVI values for Raipur City of India using fifteen cloud-free Landsat data sets of the pre-monsoon season from 2002 to 2018. LST maintains a strong negative relationship with NDVI for the whole of the study area. The relationship is quite insignificant for both the high LST zones and low LST zones. The results also indicate that under the positive NDVI values, the LST–NDVI relationships are strong to moderately negative, whereas it is positive and non-consistent under the negative values of NDVI. The results also show that the relationship is stronger in the earlier times, whereas it is weaker in recent times. An increase in heterogeneous landscape inside the city boundary strongly supports the changing pattern of LST–NDVI relationship.

Many time-series smoothing methods can be used for reducing noise and extracting plant phenological parameters from remotely-sensed data, but there is still no conclusive evidence in favor of one method over others. Here we use moderate-resolution imaging spectroradiometer (MODIS) derived normalized difference vegetation index (NDVI) to investigate five smoothing methods: Savitzky-Golay fitting (SG), locally weighted regression scatterplot smoothing (LO), spline smoothing (SP), asymmetric Gaussian function fitting (AG), and double logistic function fitting (DL). We use ground tower measured NDVI (10 sites) and gross primary productivity (GPP, 4 sites) to evaluate the smoothed satellite-derived NDVI time-series, and elevation data to evaluate phenology parameters derived from smoothed NDVI. The results indicate that all smoothing methods can reduce noise and improve signal quality, but that no single method always performs better than others. Overall, the local filtering methods (SG and LO) can generate very accurate results if smoothing parameters are optimally calibrated. If local calibration cannot be performed, cross validation is a way to automatically determine the smoothing parameter. However, this method may in some cases generate poor fits, and when calibration is not possible the function fitting methods (AG and DL) provide the most robust description of the seasonal dynamics.

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran’s greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices.

Conservation tillage practices often leave appreciable amounts of crop residues on soil surfaces after harvesting and generally improve soil structure, enhance soil organic C (SOC) content, and reduce soil erosion. Remote sensing methods have shown great promise in efficiently estimating crop residue cover, and thus inferring soil tillage intensity. Furthermore, these tillage intensity estimates can be used in soil C models. Reflectance spectra of more than 4200 soils and 80 crop residues were measured in the laboratory across the 350- to 2500-nm wavelength region. Six remote sensing spectral indices were used to estimate crop residue cover: the Cellulose Absorption Index (CAI), the Lignin-Cellulose Absorption Index (LCA), the Normalized Difference Tillage Index (NDTI), the Normalized Difference Senescent Vegetation Index (NDSVI), and the Normalized Difference Indices 5 and 7 (NDI5 and NDI7, respectively). Soil mineralogy and SOC affected these spectral indices for crop residue cover more than soil taxonomic order, which generally had little effect on spectral reflectance. The values of the spectral indices for soils were similar within Land Resource Regions and, specifically, for Major Land Resource Areas. The CAI showed the best separation between soils and residues, followed by LCA and NDTI. Although NDSVI, NDI5, and NDI7 had significant overlaps between soil and residue index values, assessments of crop residue cover classes may be possible with local calibrations. Future satellite sensors should include appropriate bands for assessing crop residue and nonphotosynthetic vegetation.

Precision irrigation management is based on the accuracy and feasibility of sensor data assessing the plant water status. Multispectral and thermal infrared images acquired from an unmanned aerial vehicle (UAV) were analyzed to evaluate the applicability of the data in the assessment of variants of subsurface irrigation configurations. The study was carried out in a Cabernet Sauvignon orchard located near Benton City, Washington. Plants were subsurface irrigated at a 30, 60, and 90 cm depth, with 15%, 30%, and 60% irrigation of the standard irrigation level as determined by the grower in commercial production management. Half of the plots were irrigated using pulse irrigation and the other half using continuous irrigation techniques. The treatments were compared to the control plots that received standard surface irrigation at a continuous rate. The results showed differences in fruit yield when the control was compared to deficit irrigated treatments (15%, 30%, 60% of standard irrigation), while no differences were found for comparisons of the techniques (pulse, continuous) or depths of irrigation (30, 60, 90 cm). Leaf stomatal conductance of control and 60% irrigation treatments were statistically different compared to treatments receiving 30% and 15% irrigation. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and canopy temperature were correlated to fruit yield and leaf stomatal conductance. Significant correlations (p < 0.01) were observed between NDVI, GNDVI, and canopy temperature with fruit yield (Pearson’s correlation coefficient, r = 0.68, 0.73, and −0.83, respectively), and with leaf stomatal conductance (r = 0.56, 0.65, and −0.63, respectively) at 44 days before harvest. This study demonstrates the potential of using low-altitude multispectral and thermal imagery data in the assessment of irrigation techniques and relative degree of plant water stress. In addition, results provide a feasibility analysis of our hypothesis that thermal infrared images can be used as a rapid tool to estimate leaf stomatal conductance, indicative of the spatial variation in the vineyard. This is critically important, as such data will provide a near real-time crop stress assessment for better irrigation management/scheduling in wine grape production.

The Mongolian Plateau, comprising the nation of Mongolia and the Inner Mongolia Autonomous Region of China, has been influenced by significant climatic changes and intensive human activities. Previous satellite-based analyses have suggested an increasing tendency in the vegetation cover over recent decades. However, several ground-based observations have indicated a decline in vegetation production. This study aimed to explore long-term changes in vegetation greenness and land surface phenology in relation to changes in temperature and precipitation on the Plateau between 1982 and 2011 using the normalized difference vegetation index (NDVI). Across the Plateau, a significantly positive trend in the growing season (May–September) NDVI was observed from 1982 to 1998, but since that time, the NDVI has not shown a persistent increase, thus causing an insignificant trend over the entire study period. For the steppe vegetation (a major vegetation type on the Plateau), the NDVI increased significantly in spring but decreased in summer. Precipitation was the dominant factor related to changes in steppe vegetation. Warming in spring contributed to earlier vegetation green-up only in meadow steppe vegetation, implying that water deficiency in typical and desert steppe vegetation may eliminate the effect of warming. Our results also suggest a combined effect of climatic and non-climatic factors and highlight the need to examine the role of regional human activities in the control of vegetation dynamics.

Remote sensing applications in water resources management are quite essential in watershed characterization, particularly when mega basins are under investigation. Water quality parameters help in decision making regarding the further use of water based on its quality. Water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used in the current study to estimate the water quality parameters in the dam lake of Wadi Baysh, Saudi Arabia. Water quality parameters were collected daily over 2 years (2017–2018) from the water treatment station located within the dam vicinity and were correspondingly tested against remotely sensed water quality parameters. Remote sensing data were collected from Sentinel-2 sensor, European Space Agency (ESA) on a satellite temporal resolution basis. Data were pre-processed then processed to estimate the maximum chlorophyll index (MCI), green normalized difference vegetation index (GNDVI) and normalized difference turbidity index (NDTI). Zonal statistics were used to improve the regression analysis between the spatial data estimated from the remote sensing images and the nonspatial data collected from the water treatment plant. Results showed different correlation coefficients between the ground truth collected data and the corresponding indices conducted from remote sensing data. Actual chlorophyll a concentration showed high correlation with estimated MCI mean values with an R2 of 0.96, actual nitrate concentration showed high correlation with the estimated GNDVI mean values with an R2 of 0.94, and the actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R2 of 0.94. The research findings support the use of remote sensing data of Sentinel-2 to estimate water quality parameters in arid environments.