Normalized Difference Vegetation Index Profiles Research Articles

English

Crop mapping and its health monitoring are of key importance for a country like Pakistan where a considerable share of the GDP originates from agricultural production. Currently, extensive capital and human resource are utilized to record the crop types and monitor crop health due to variations in crop pattern and intercropping practices being observed in many parts of the country. Satellite Remote Sensing, however, can be utilized very efficiently to identify crop types and estimate acreage by using unique phenological cycles derived from NDVI thresholds. In this research, we proposed a semi-automated, efficient and cost-effective state-of-the-art technique for crop classification using satellite remote sensing datasets and GIS techniques to replace outdated conventional survey-based crop estimations procedures. This research was conducted in Sahiwal district, Pakistan to categorise several crop types using two major inputs: satellite (Sentinel-2, 10m) imagery and local crop calendar. The research adopted a semi-automatic approach to find the main crops of Rabi (winter) and Kharif (summer) using orthorectified and atmospherically corrected Level-1C images to extract phenological information based on Normalized Difference Vegetation Index (NDVI). The complete phenological life cycle of crops including sowing, growing, and harvesting stages were deeply analyzed with the help of crop calendar and NDVI profiles. Then this information passed through iterative selforganizing (ISO Data Model) of unsupervised classification technique in the form of temporal stacked images. The NDVI profiles of each crop were synchronized with the crop calendar of the study area to develop the crop categorization procedure. The results were validated with the statistics of the agriculture department Lahore and field samples gave about 85% and 88% accuracy for Rabi and Kharif cropping seasons respectively. It is concluded from the research that the tested methodology is capable of providing a fast, cost-efficient, and reliable solution for crop mapping instead of conventional survey-based crop area assessment techniques (conventional Girdawri) which requires a vast number of resources and time, and it can be replicated in different areas with minor changes

Remote sensing crop group-specific indicators to support regional yield forecasting in Europe

Operational crop yield forecasting services typically provides crop yield forecasts based on regression models between official yields and agro-environmental variables, among which meteorological data, crop simulation model or satellite-derived indicators. The reliability with which these variables infer on yields depends, among many other factors, also on their aggregation in the space domain, for example on the type of the crop masks utilized in the aggregation process from point to regional scale. This work investigates how the yield explanatory power of satellite-derived indicators is changing moving from time-stable arable land masks to annual crop group-specific masks. We compare in particular the linkage between time series of regional crop yield in Europe and the Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS), when generic arable land masks or crop group-specific and year-specific information are applied to aggregate pixel values at regional level. Regional (Eurostat level NUTS-2) crop yield statistics were collected from official databases for the period 2003–2019, while NDVI data were derived from MODIS daily products at 250 m spatial resolution for the same reference period. Regional NDVI profiles were retrieved by averaging single pixels time series according to the information of five crop masks, including generic arable land masks, crop group-specific static masks (separating winter and spring crops from summer crops), and annual crop group-specific masks (distinguishing between the two crop groups and varying in time). A compared correlation analysis between yield data and regional temporal NDVI profiles was performed assuming a linear regression model. Coefficient of determination R2 and Root Mean Squared Error (RMSE) were computed to assess the models’ errors and to analyze the effect of the aggregation on the different crop masks. Results indicated an improvement in yield estimation when using annual crop group-specific indicators with respect to generic and static products. Although not homogeneously distributed throughout Europe, advantages have been highlighted both in terms of accuracy and timeliness of the prediction. In most regions, the introduction of annual masks allowed to reduce RMSE values by 0.3 t/ha and advanced forecast times by up to 30 days. The added value in the use of annual crop group-specific masks concerned the two European most cultivated crops, namely soft wheat and grain maize.

Phenology Patterns and Postfire Vegetation Regeneration in the Chiquitania Region of Bolivia Using Sentinel-2

The natural regeneration of ecosystems impacted by fires is a high priority in Bolivia, and represents one of the country’s greatest environmental challenges. With the abundance of spatial data and access to improved technologies, it is critical to provide an effective method of analysis to evaluate changes in land use in the face of the global need to understand the dynamics of vegetation in regeneration processes. In this context, we evaluated the dynamics of natural regeneration through phenological patterns by measuring the maximal and minimal spectral thresholds at four fire-impacted sites in Chiquitania in 2019 and 2020, and compared them with unburned areas using harmonic fitted values of the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR). We used two-way ANOVA test to evaluate the significant differences in the values of the profiles of NDVI and NBR indices. We quantified severity at the four study sites using the dNBR obtained from the difference between pre- and postfire NBR. Additionally, we selected 66 sampling sites to apply the Composite Burn Index (CBI) methodology. Our results indicate that NBR is the most reliable index for interannual comparisons and determining changes in the phenological pattern, which allow for the detection of postfire regeneration. Fire severity levels based on dNBR and CBI indices are reliable methodologies that allow for determining the severity and dynamics of changes in postfire regeneration levels in forested and nonforested areas.

A Remote Sensing-Based Analysis of the Impact of Syrian Crisis on Agricultural Land Abandonment in Yarmouk River Basin.

In this study, we implemented a remote sensing-based approach for monitoring abandoned agricultural land in the Yarmouk River Basin (YRB) in Southern Syria and Northern Jordan during the Syrian crisis. A time series analysis for the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) was conducted using 1650 multi-temporal images from Landsat-5 and Landsat-8 between 1986 and 2021. We analyzed the agricultural phenological profiles and investigated the impact of the Syrian crisis on agricultural activities in YRB. The analysis was performed using JavaScript commands in Google Earth Engine. The results confirmed the impact of the Syrian crisis on agricultural land use. The phenological characteristics of NDVI and NDMI during the crisis (2013–2021) were compared to the phenological profiles for the period before the crisis (1986–2010). The NDVI and NDMI profiles had smooth, bell-shaped, and single beak NDVI and NDMI values during the period of crisis in comparison to those irregular phenological profiles for the period before the crisis or during the de-escalation/reconciliation period in the study area. The maximum average NDVI and NDMI values was found in March during the crisis, indicating the progress of natural vegetation and fallow land, while they fluctuated between March and April before the crisis or during the de-escalation/reconciliation period, indicating regular agricultural and cultivation practices.

A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter

Normalized Difference Vegetation Index (NDVI) data derived from Landsat satellites are important resources for vegetation monitoring. However, Landsat NDVI time-series data are usually temporally discontinuous owing to the nominal 16-day revisit cycle, frequent cloud contamination, and other factors. Although several methods have been proposed to reconstruct continuous Landsat NDVI time-series data, some challenges remain in the existing reconstruction methods. In this study, we developed a simple but effective Gap Filling and Savitzky–Golay filtering method (referred to as “GF-SG”) to reconstruct high-quality Landsat NDVI time-series data. This new method first generates a synthesized NDVI time series by filling missing values in the original Landsat NDVI time-series data by integrating the MODIS NDVI time-series data and cloud-free Landsat observations. Then, a weighted Savitzky-Golay filter was designed to remove the residual noise in the synthesized time series. Compared with three previous typical methods (IFSDAF, STAIR, and Fill-and-Fit) in two challenging areas (the Coleambally irrigated area in Australia and the Taian cultivated area in China) with heterogeneous parcels and complex NDVI profiles, we found that GF-SG performed the best with three obvious improvements. First, GF-SG improved the reconstruction of long-term continuous missing values in Landsat NDVI time series, whereas the other methods were less reliable for reconstructing these long data gaps. Second, the performance of GF-SG was less affected by the residual noise caused by cloud detection errors in the Landsat image, which is due to the incorporation of the weighted SG filter in the new method. Third, GF-SG was simple and could be implemented on the computing platform Google Earth Engine (GEE), which is particularly important for the practical application of the new method at a large spatial scale. The GEE code is freely available at https://code.earthengine.google.com/3a883c9e84ad119045bcb88e4de77b47?noload=true. We expect that this practical approach can further popularize the use of Landsat NDVI time-series data in ecological, geographical, and environmental research.

Analysis of Optimum Growth Stages for Winter Crop Separability using Multi-Temporal NDVI Profiles in Vijapur Taluka, Gujarat State

The multi-date Remote sensing satellite data has shown its great potential in agricultural applications. The crop monitoring throughout its growing season requires up-to-date and timely information for better crop growth management. In the present study analysis of growth stages of different winter crops grown in Vijapur Taluka was carried for determining optimum growth period for crop separability using multi-temporal NDVI profiles. The multi-temporal Sentinel-2 multi-spectral data (spatial resolution 10-m) covering study area for the winter crop period from November-2018 to March-2019 was downloaded from https://earthexplorer.usgs.gov/. Ground Truth (GT) data on winter crops grown in different villages of Vijapur Taluka was collected at multiple locations coinciding with different growth stages of winter crops. The spectral behavior of wheat, cotton, potato, fennel and castor crops during active growth stages was studied and it was observed that the spectral response of these crops is quite distinct. The maximum spectral difference was observed in the NIR spectral band as compared to other visible spectral bands of Sentinel-2 multi-spectral data. However, wheat and potato have almost similar spectral response during the month of January when wheat was at flag leaf to flowering stage and potato was at maximum vegetative growth stage. This suggests that, single-date Sentinel-2 multispectral digital data of active vegetative growth stages of these two crops during the month of January is not appropriate and sufficient for unique discrimination of wheat and potato. Therefore, Normalized Difference Vegetation Index (NDVI) profiles of these major winter crops were generated to monitor crop growth stages where maximum spectral differences can be observed. The results of NDVI growth profiles indicated that, during second fortnight of February wheat was grain filling and grain-milk stage and potato was at maximum vegetative stage with tuber maturity and at the same time other crops namely cotton, castor and fennel were at the maturity stages, have quite distinct NDVI values. During this period major winter crops with different growth stages in this study area will have quite distinct spectral behaviour which indicates that the period of second fortnight of February is quite appropriate and unique for discriminating these winter crops using single date satellite data.

The Role of Field Survey in the Identification of Farmland Abandonment in Slovakia Using Sentinel-2 Data

Agricultural land abandonment is a dynamic process characterized by both significant spectral variability and spectral similarity to areas of agricultural land. The identification of abandoned agricultural land (AAL) based on remote sensing data must be preceded by field surveys that are focused on the acquisition of the physiognomic characteristics (the composition of species, height of vegetation, textures, and clustering into patterns) of these areas. This study aims to document the physiognomic and spectral differences between AAL and other land cover/land use classes in Slovakia. The Normalized Difference Vegetation Index (NDVI), derived from Sentinel-2 time series for vegetation from April to September 2018, was applied. NDVI values were calculated for each Sentinel-2 scene, and NDVI profiles for selected samples were used to create phenological profiles for each AAL and land cover/land use class. The dispersion of the NDVI values for these classes, their median, and the root mean square error between NDVI data show that overgrowth by herbaceous plants is characterized by more significant dynamics (0.40–0.75), resulting in better spectral discriminability than classes overgrown by shrubs and trees (0.70–0.80). Field survey data are a fundamental prerequisite for the correct explanation of the discriminability of these AAL classes.

Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing

The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of <3 days with GCC, while these metrics from VIgreen and NDVI resulted in a slightly higher bias of (3–10) days. The observed agreement between UAVNDVI vs. satelliteNDVI and PhenoCamGCC vs. satelliteGCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis.

Spatial variability of evapotranspiration and pressure on groundwater resources: remote sensing monitoring by crop type in the Bekaa plain, Lebanon

The lack of data on water consumption by agriculture in the context of climate change led the authors to estimate the water balance of the Bekaa Valley. Plots were mapped according to the normalized difference vegetation index profiles of spring and summer crop with the Sentinels-2 images. Surface energy balance model python (PYSEBAL) estimated the seasonal evapotranspiration (ETseason) and net irrigation requirements (NIRseason). Spring and summer crop maps were accurate: 90% and 93%. The results of PYSEBAL were validated by the comparison between averaged daily ET of PYSEBAL and FAO-56 method (root mean square error from 0.38 to 1.93 and the mean average error from 0.33 to 1.64 mm / day). The water balance was negative: 0.16 km3 of groundwater was extracted against 0.10 km3 of rainfall available for aquifer recharge in 2017. This is explained by ET/ha/season (alfalfa: 1094 mm / ha / season, orchards 881, corn 719, tobacco 606, cucumber 558, late potatoes 487, summer vegetables 483, early potatoes 467, vineyard 390, spring vegetables 237, and wheat 313). ETseason varies according to temperature (strong correlation between daily ET and temperature, Pearson: 0.74), relative humidity (Pearson: −0.65), surface radiation (Pearson: 0.75), and percentage of sand in the soil and its depth (strong negative Pearson: −0.62 and −0.57).

Winter Crop Growth Monitoring using Multi-Temporal NDVI Profiles in Kapadvanj Taluka, Gujarat State

In the present study on winter crop growth monitoring in different villages in Kapadvanj Taluka of Kheda district was carried out using multi-temporal Sentinel-2 multi-spectral data (spatial resolution 10-m). Multi-temporal Sentinel-2 data covering study area for the winter crop period from November-2018 to March-2019 was downloaded from https://earthexplorer.usgs.gov/. The major objective of this study was to monitor site-specific crop growth in different villages of Kapadvanj Taluka by generating Normalized Difference Vegetation Index (NDVI) profiles of major winter crops. The spectral behavior of wheat, potato, bajara and castor crops during active growth stages was studied and it was observed that the spectral response of wheat and potato crops have quite distinct spectral behavior. However, castor and bajara crops do not show distinct spectral behavior. In this study, from the NDVI profiles of different crops it was observed that very distinct growth stages like early growth stage to flag leaf emergence which correspondence to rising of NDVI, followed by flag leaf emergence to flowering and grain filling stages which corresponds to maximum NDVI and finally physiological maturity stages corresponding to declining of NDVI of all the winter crops.

Spectral Response of Winter Crops Using Multi-temporal Sentinel-2 MSI Data in Visnagar Taluka, Gujarat State

Developing Countries like India has more than half of the population engaged in fishery, agriculture etc. and agriculture drives the economy of the Nation. Traditional survey methods are tedious and time consuming, however, with advancement in Remote Sensing Technology data for Agriculture monitoring can be obtained at local and regional scale. The present study demonstrates the use of multi-year multi-date Sentinel-2 MSI data for winter crop monitoring in in Visnagar taluka Mahesansa district of Gujarat state. Multi-temporal Sentinel-2 data covering study area for the winter crop period from November-2018 to March-2019 was downloaded from https://earthexplorer.usgs.gov/ .The major objective of this study was to monitor site-specific crop growth in different villages of Visnagar Taluka by generating Normalized Difference Vegetation Index (NDVI) profiles of major winter crops. The spectral behavior of wheat, mustard, Fennel and castor crops during active growth stages was studied. The results indicated that different winter crops can be discriminated using the data during the early growth phases like flag-leaf emergence to flowering stages of these crops. Even it was observed that the spectral response of normal-sown and late-sown wheat crop have quite distinct spectral behavior as compared to other winter crops. In this study, the NDVI profiles of different winter crops indicated that Last week of January to first week of February, when all the winter crops were at grand growth stages of grain filling and grain-milk, pod formation (mustard & castor) stages have similar NDVI values, therefore during this period major winter crops in this region may not be possible to discriminate using single date satellite data.

Winter wheat growth assessment using Temporal Normalized Phenology Index (TNPI) in Bhuj Taluka, Gujarat State, India

In the present study on winter crop growth monitoring in Bhuj Taluka of Kachchh district was carried out using multi-temporal Sentinel-2 multi-spectral data (spatial resolution 10-m). Sentinel-2 multi-spectral data for the period from October-2018 to March-2019 was analysed in this study. The major objective of this study was to generate crop growth profiles of different crops and identify the relationships between crop phenology and crop growth stages by generating Normalized Difference Vegetation Index (NDVI) profiles of wheat and mustard crops.The results of spectral behavior of wheat (normal & late sown) and mustard during active growth stages indicated that these winter crops have distinct spectral signatures in Sentinel-2 spectral bands. The NDVI growth profiles of wheat and mustard indicate the temporal variations during different growth stages of these crops. The maximum temporal variations were observed during early growth stages to flowering & grain filling stages. The difference in sowing dates of wheat and mustard crops result in different phenological stages of these winter crops. The NDVI curve of Rabi Season-2018-19 was divided into different phenological growth phases namely, i) Phase-I: G0 (Spectral emergence) to G1(Flag leaf emergence), Phase-II: G1 (Flag leaf emergence) to Gmax (Flowering and Grain filling), Phase-III: Gmax (Flowering and Grain filling) to G2 (Dough Stage) and Phase-IV: G2 (Dough Stage) to G3 (Physiological Maturity). The temporal changes in the NDVI was analysed by computing the Temporal Normalized Phenology Index (TNPI) between different phenological phases from G0 to G3.Mean TNPI values of wheat crop for different phenological phases were computed which indicated that rising part of NDVI profile of growth phases from G0 to Gmax higher TNPI values whereas the declining part of NDVI profile of growth phases from Gmax to G3 showed lower TNPI values. The difference NDVI images were generated using the NDVI images of two different phenological phases of wheat crop which represent the temporal changes in wheat crop greenness, where “0” represents no temporal change and “1” represents the maximum temporal change in vegetation greenness across wheat crop areas in the study area.

Rapid diagnosis of agricultural soil health: A novel soil health index based on natural soil productivity and human management

It has become increasingly important to consider its productivity for agricultural soil health assessment. Moreover, one of the main challenges is that there are still few studies on addressing the complex dynamics of soil health assessment by the rapid and cross-regional method. Thus, we proposed a novel conceptual model to evaluate agricultural soil health in order to highlight the synergy and interaction of natural soil productivity and its external inputs; besides, the new proposed soil health index (SHI) can be used to rapidly quantify their influences of soil productivity on soil health assessment, based on the 10-day normalized difference vegetation index (NDVI) time series data. We applied the principal component analysis (PCA) to transform NDVI profiles into responses of crop primary productivity due to different drivers. The results demonstrated that soil productivity in our study area can be identified for different cropping systems by the PCA method; and different principle components (PCs) for the same cropping system can also be used to estimate contributions of natural soil productivity and human management productivity. The SHI indicator, defined by the equation of (PC1-PC2)/(PC1+PC2), was used to explore soil health in our study area. We found that soil in the orchard system was relatively healthier than that in other two cropping systems, indicating the natural soil productivity presented more contributions than that from external inputs. We concluded that it is useful to apply the SHI indicator into soil health assessment, especially considering the local natural situation and human management practices.

Automatic silage maize detection based on phenological rules using Sentinel-2 time-series dataset

ABSTRACT Recently, availability of valuable high spatial and temporal resolution optical satellite imagery such as Sentinel-2 has provided a great opportunity for a wide variety of crop-related studies including crop phenology detection, crop biophysical and biochemical parameter estimation, and crop-type mapping. Most current crop mapping methods focus on supervised classification, demanding a large number of field sampling points to feed the classifier. In this paper, we propose an automatic method for silage maize mapping based on Sentinel-2 time-series data in three agricultural areas in Iran (namely Marvdasht, Abyek, and Mashhad) and an individual area in USA (Tulare County, California). We considered Marvdasht as the training site and other three sites as the test sites. After the preprocessing step, temporal profiles of the Normalized Difference Vegetation Index (NDVI) were computed from Sentinel-2 time-series images in 2017 and 2018. Then, the Savitzky–Golay filter was applied to the NDVI profile for noise reduction. Linear interpolation was also used for the construction of continuous NDVI time-series during the maize growing season. Then, phenological parameters were extracted in Marvdasht, for maize and other crops. After comprehensive analyses, the ratio of the slope from the peak of the greenness to the harvest (SPGH) to the length of the growing season (LOS) was suggested as an appropriate variable for automatic maize detection. Evaluation of this variable in the study sites confirmed its feasibility for automatic maize mapping, with the kappa coefficient values of 0.89, 0.8, 0.9, and 0.8 in Abyek, Marvdasht, Mashhad, and Tulare in 2017, respectively. The performance of the suggested method for the second year (2018) is close to those of 2017 which means that maize detection algorithm results are consistent and robust across years. Moreover, to ascertain high potential of the proposed variable, the results were compared with those of the maximum likelihood (ML) and support vector machine (SVM) classifiers in the test sites. The performance of SVM (with a large number of training samples) was analogous to that of the suggested automatic method in terms of accuracy; however, inferior performance of ML was evident. The results of this research demonstrated the great potential of our proposed phenological method in automatic silage maize mapping.

Long-term NDVI and recent vegetation cover profiles of major offshore island nesting sites of sea turtles in Saudi waters of the northern Arabian Gulf

Abstract Vegetation is an important ecological component of offshore islands in the Arabian Gulf (AG), which maintains long-term resilience of these islands. This is achieved by influencing sediment retention and moisture acquisition via condensation during periods of high humidity and by providing a variety of microhabitats for island fauna. The resilience of offshore islands’ ecosystems in the Saudi waters is important because they host the largest number of nesting hawksbill and green turtles in the AG. This study defines the characteristics and the long-term trends in vegetation cover of the offshore islands used by sea turtles as nesting grounds in the northern AG. To establish a ground-validated baseline for vegetation profiles, a 50 m × 50 m grid system is developed on Karan and Jana islands (Is.) with photo-quadrats taken at each grid intersection. The 1,317 and 444 photo-quadrats, for Karan and Jana Is., respectively, were analyzed for maximum plant height and percent cover of living (green) plants, dead plants, and bare sand. Landsat 7 and 8 satellite top-of-atmosphere reflectance images were used to calculate the Normalized Difference Vegetation Index (NDVI) from 1999 through 2018 to analyze the long-term vegetation profiles of the islands. Monthly rainfall data from five meteorological stations along the Eastern Province of Saudi Arabia and Oceanic Nino Index (ONI) are presented to provide a context of the long-term NDVI time series variability. The ground-validated vegetation profiles provided baseline data during the onset of summer in 2017 and revealed differences in maximum plant height and the extent of living, dead vegetation and sand cover on Jana Is. (28.3 cm, 19.9%, 63.3%, and 16.8%) and Karan Is. (21.7 cm, 20.6%, 48.7%, and 30.7%), respectively. The NDVI data for both islands are grouped into three periods, namely: 2001–2007 - high winter, low summer; 2008–2013 – low winter, low summer; 2014–2018 – irregular high/low winter, low summer. The long-term trend showed a slightly decreasing NDVI when compared in the context of the high NDVI measured for the two islands during the early 2000 s, particularly during the winter time. An extended reduction in winter NDVI was recorded for six years from 2008 to 2013, which coincided with reduced rainfall in the region and prolonged La Nina. Five extreme dips in winter NDVI values coincided with strong (2000, 2008, and 2011) and moderate (2012 and 2018) La Nina events. Long-term vegetation profiles of the offshore islands seemed to be tightly coupled with long-term rainfall patterns.

Multi-temporal monitoring of cotton growth through the vegetation profile classification for Tashkent province, Uzbekistan

Abstract As satellite data of the Earth surface seems to be of vital importance for many applications, classification of land use and land cover has been found to vary dramatically in different approaches. In this paper, modified classification algorithm of remote sensing data is presented for processing medium and high spatial resolution satellite images like Landsat and Sentinel in Tashkent province of Uzbekistan. The results of NDVI (Normalized difference vegetation index) profile analysis via Spectral Correlation Mapper classification are shown for the period 1994-2017. It is implied, that combination of optical and radar data with application of Spectral Correlation Mapper classification improve the results of classification for a specific dataset by considering such factors as overall classification accuracy and time and labor involved.

Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8

Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time.

Estimation of Crop Water Deficit in Lower Bari Doab, Pakistan Using Reflection-Based Crop Coefficient

There is a global realization in all governmental setups of the need to provoke the efficient appraisal of crop water budgeting in order to manage water resources efficiently. This study aims to use the satellite remote sensing techniques to determine the water deficit in the crop rich Lower Bari Doab Canal (LBDC) command area. Crop classification was performed using multi-temporal NDVI profiles of Landsat-8 imagery by distinguishing the crop cycles based on reflectance curves. The reflectance-based crop coefficients (Kc) were derived by linear regression between normalized difference vegetation index (NDVI) cycles of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MYD13Q1 products and Food and Agriculture Organization (FAO) defined crop coefficients. A MODIS 250 m NDVI product of the last 10 years (2004-2013) was used to identify the best performing crop cycle using Fourier filter method. The meteorological parameters including rainfall and temperature substantiated the reference evapotranspiration (ET0) calculated using the Hargreaves method. The difference of potential ET and actual ET, derived from the reflectance-based Kc calculated using reference NDVI and current NDVI, generates the water deficit. Results depict the strong correlation between ET, temperature and rainfall, as the regions having maximum temperature resulted in high ET and low rainfall and vice versa. The derived Kc values were observed to be accurate when compared with the crop calendar. Results revealed maximum water deficit at middle stage of the crops, which were observed to be particularly higher at the tail of the canal command. Moreover, results also depicted that kharif (summer) crops suffer higher deficit in comparison to rabi (winter) crops due to higher ET demand caused by higher temperature. Results of the research can be utilized for rational allocation of canal supplies and guiding farmers towards usage of alternate sources to avoid crop water stress.

LAND COVER MONITORING OF LAGUNA LAKE WATERSHED USING MODIS NDVI DATA

Abstract. Land use and land cover monitoring is an important component in the management of Laguna Lake watershed due to its impacts on the lake’s water quality. Due to limitations caused by cloud cover, satellite systems with limited revisit capability fail to provide sufficient data to more effectively monitor the land surface. Normalized difference vegetation index (NDVI) derived from MODIS image data were used to generate land cover maps for the years 2001, 2005, 2009, 2013, and 2017. These were produced by classifying ISODATA classes using annual NDVI profiles, which resulted in land cover classes, namely, agricultural land, built-up, forest, rangeland, water, and wetland. The resulting maps were post-processed using multi-variate alteration detection (MAD), resulting in multi-temporal land cover maps with improved overall accuracies and kappa coefficients that indicate moderate agreement with ground truth data. Spatiotemporal hot spot analysis was also performed using NDVI data from 2001 to 2017 to identify vegetation hot spot areas, where clustering of low NDVI values were observed over the years. Results showed an increasing trend in built-up areas accompanied by decreasing trends in water and wetland areas, indicating impacts caused by land reclamation and expansion of residential subdivisions near the lakeshore. The decrease in total vegetation area from 2001 to 2017 could be attributed to conversion of land to built-up surface. Vegetated areas in identified hot spots decreased from 41% in 2001 to 19% in 2017. This suggests that vegetation cover in these hot spots was converted to non-vegetated surface during the time period studied.

Understanding the Benefits from Green Areas in Rome: The Role of Evergreen and Deciduous Species in Carbon Dioxide Sequestration Capability

Urban areas are a major source of anthropogenic carbon dioxide (CO2) emissions because of road traffic and local heating with natural gas, oil or coal. Rome is among the largest European cities (129,000 ha) with a large volume of green areas (69.6% of the total Municipality area). The CO2 sequestration (CS) capability for the greenery extending for about 300 km2 inside the area delimited by the Great Ring Road (GRA) in Rome was calculated combining satellite data with CS data measured in the field. Data from Sentinel-2 were collected and the Normalized Difference Vegetation Index (NDVI) was computed on a pixel-base. Three plant classes homogeneous in terms of annual NDVI profile were identified: deciduous trees (DT), evergreen trees (ET) and meadows (M) covering an area of 14,142.027 ha within the GRA, of which M had the highest percentage (48%), followed by DT (27%) and ET (25%). CS ranged from 428,241,492.9 Tons CO2 year-1 (ET) to 263,072,460.6 Tons CO2 year-1 (M). The total CS of the greenery inside the GRA was 1049,490,355.4 Tons CO2 year-1 resulting in an annual economic value of $772,424,901.6/ha. The CO2 sequestration capability of the considered plant classes could be incorporated into the national greenhouse gas emission budget to calculate the contribution of CO2 sequestration to the economy of Rome.