Normalized Difference Vegetation Index Time Series Research Articles

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

Remote Detection of Large-Area Crop Types: The Role of Plant Phenology and Topography

Sustainable agricultural practices necessitate accurate baseline data of crop types and their detailed spatial distribution. Compared with field surveys, remote sensing has demonstrated superior performance, offering spatially explicit crop distribution in a timely manner. Recent studies have taken advantage of remote sensing time series to capture the variation in plant phenology, inferring major crop types. However, such an approach was rarely used to extract detailed, multiple crop types spanning a large area, and the impact of topography has yet to be well analyzed in mountainous regions. This study aims to answer two questions in crop type extraction: (i) Is it feasible to accurately map multiple crop types over a large mountainous area with phenology-based modeling? (ii) What are the effects of topography in such modeling? To answer the questions, phenological metrics were extracted from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite time series, and the random forests classifier was used to map 12 crop types in South China (236,700 km2), featuring a subtropical monsoon climate and high topographic variation. Our study revealed promising results using MODIS EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) time series, although EVI outperformed NDVI (overall accuracy: 85% versus 81%). The spectral and temporal metrics of plant phenology significantly contributed to crop identification, where the spectral information exhibited greater importance. The increase of slope led to a decrease in model accuracy in general. However, uniformly distributed tree plantations (e.g., tea-oil camellia, gum, and tea trees) being cultivated on large slopes (>15 degrees) achieved accuracies greater than 80%.

A Spatialization Method for Grain Yield Statistical Data: A Study on Winter Wheat of Shandong Province, China

Core Ideas Dividing the study area into subregions and classifying them improved winter wheat classification accuracy.Single‐phase Normalized Difference Vegetation Indices acquired on 6 March, 23 April, 25 May, and 29 June were the best variables for building the wheat yield spatialization model.The extraction accuracy of winter wheat area greatly impacted the spatialization of yield.The proposed model can provide a technical reference for producing high‐resolution crop yield distribution maps. Grain yield data based on administrative divisions (counties, cities, etc.) for statistics lack spatial information, which can be effectively solved by grain yield spatialization. This paper proposes a spatialization method for grain yield based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data. The method was tested by taking winter wheat (Triticum aestivum L.) in Shandong Province in China as an example. First, the classification and regression tree (CART) algorithm was trained to extract the winter wheat planting pixels in 2016. The average NDVIs of the different growing stages (returning green, jointing, heading, and milk ripening) were calculated from the MODIS NDVI time series data. The relationship between winter wheat yield and NDVI variables (including single‐phase NDVI and the average NDVI of different growing stages) was analyzed by univariate and multiple linear regressions. The NDVI variable with the highest correlation to winter wheat yield and the minimum root mean square error of the fitting equation were chosen as input to build the spatialization model. The results show that the classification accuracy of winter wheat estimated with the confusion matrix was 82.51% and that the average precision of planting acreage compared with county‐level statistical data was 87.64%. The average relative error of yield spatialization at the county level was 22.71%. The method developed in this paper is easy to operate and popularize, and it can provide a technical reference for producing high‐resolution crop yield distribution maps of long time series through spatialization.

A Novel Method for Separating Woody and Herbaceous Time Series

Mapping the spatial distribution of woody and herbaceous vegetation in high temporal resolution in savannas would be beneficial for modeling interrelationships between trees and grasses, and monitoring fuel loads and biomass for livestock. In this study, we developed a frequency decomposition method to separate woody and herbaceous vegetation components using Normalized Difference Vegetation Index (NDVI) time series. The results were validated using fractional cover data derived from high-resolution images. The validation revealed a close relationship between our decomposed NDVI and corresponding fractional cover (R2 = 0.55 and 0.64 for woody and herbaceous components, respectively). We examined the spatial and temporal patterns of the decomposed NDVI, where woody and herbaceous NDVI showed different responses to precipitation. The methods proposed in this study can be used to separate the woody and herbaceous NDVI time series as an alternative approach for monitoring woody and herbaceous vegetation interrelationships related to climatic drivers.

Natural Hazards, Human Factors, and “Ghost Towns”: a Multi-Level Approach

The abandonment of inhabited places is a phenomenon that concerns many countries worldwide and Italy particularly where a large number of deserted settlements are hosted. Many are the factors driving and conditioning the abandonment of a site, such as natural extreme events (e.g. earthquakes, landslides, and floods) and/or human (not) actions. Once the site is abandoned, the built-up area experiences a progressive physical decay so posing problems about the policies to be adopted to manage and maintain the buildings (or their ruins). That being stated, the article proposes an integrated methodological approach to analyse both the natural/human factors causing the abandonment of settlements and conservation state of deserted places over time. To test the methodology, we considered the old town of Craco (Basilicata, Southern Italy) as a case study. That “ghost town”, whose fascinating urban and natural landscapes have been the set of numerous international films, was gradually transferred to other two places since the 1960s due to the landslides that have affected the site over the centuries. Three were the explicit key aims of the research. The first was to scrutinise the activations/reactivations of the landslides jointly with their effects on the built environment so to critical go over the actions put into the field by the institutions to mitigate the hydrogeological risk. The second was to examine whether and how the landslide occurrences conditioned the urban growth of the Craco over time. The third was to investigate in relation to the vegetation growth in the Craco downtown over the last 15 years or so, to infer clues on future decay trends and conservation strategies of the built environment. The purposes were reached considering a geological-geomorphological, historical, and remote-sensing approach. In detail, the first goal was met (re)considering a cross-correlated analysis, in diachronic key, of edited/unedited archive sources with geological/geomorphological perspectives. The second was followed up performing the analysis of the Craco urban growth over the centuries and correlating it with the history of landslide occurrences. The third target was pursued by means of NDVI (Normalized Difference Vegetation Index) time series obtained from Landsat TM and Sentinel 2 data along with HSV (Hue, Saturation, Value) colour system techniques applied to multi-date Google Earth photos. From the perspective angle, the results of this research can contribute to setup proper resilience strategies for sites subject to hydrogeological hazard similar to that affects Craco, thus helping to identify conservation plans as well as enhancement policies of “ghost towns”.

Spatial-Temporal Pattern of Human-Induced Land Degradation in Northern China in the Past 3 Decades-RESTREND Approach.

Land degradation is one of the world’s most serious environmental issues. Human activities play an important role in it. Therefore, human-induced land degradation monitoring is of crucial scientific significance in revealing the evolution of land degradation and guiding its governance. Based on the residual trend (RESTREND) approach and using Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) 3g and monthly precipitation as data sources, a quantitative evaluation is conducted on the conditions of human-induced land degradation during 1982–2012 in northern China. The results indicate that (1) the “optimal cumulative precipitation-NDVImax” regression model constructed herein can improve the capability of recognizing human-induced land degradation of arid and semiarid areas in the RESTREND approach. Moreover, long time-series NDVI and precipitation data may reduce the uncertainty of quantifying human-induced land degradation. (2) In the past 3 decades, northern China has experienced three stages of human-induced land degradation, i.e., rapid development, overall reversal with local development, and continuous reversion. Human-induced land degradation in the agro-pastoral ecotone of northern China has shown a rapid restoration trend since the 1990s. (3) It is believed that the dominant factor of land degradation has a significant spatial-temporal scale effect and spatial heterogeneity. Therefore, concrete issues should be specifically analyzed to improve our understanding of land degradation development and reversal, the spatial-temporal pattern and the driving forces of land degradation in the past 3 decades in northern China. Climate change may be the main driving force of land degradation. However, the influence of human activities on the development and reversal of land degradation in small areas and in a short time is more remarkable.

Detection of rainstorm pattern in arid regions using MODIS NDVI time series analysis

The normalized difference vegetation index (NDVI) was used to delineate potential water suppliers west of the Arabian Peninsula. Time series NDVI data extracted from the moderate resolution imaging spectroradiometer NDVI product were used to develop a robust estimate of rainstorm frequency and intensity. A total of 216 NDVI images were acquired between February 2000 and January 2018 to carry out this investigation. As NDVI values of negative records correspond to water, it was possible to address and delineate the occurrence and duration of temporal ponded water. Results showed that at least 7 locations are potential to harvest water from flashfloods. Some locations witnessed 10, 11 and 13 rainstorms and ponding of water ranged from 1 to 20 months. These locations, if properly managed, could sustain a fresh water resource for local uses. The study demonstrates that NDVI time series curves could help identify the time/duration of previous rainstorms.

A Markov regime-switching regression approach to modelling NDVI from surface temperature and soil moisture

ABSTRACTBuilding accurate relationships between vegetation amount and climatic variables is helpful in understanding and informing sustainable environmental management. The common approach in this regard is to develop a generic, linear relationship or season-dependent relationships. Such approaches, however, fail to hold if data characteristics deviate from expected patterns. This study applied a regime-switching regression model, namely the Markov-switching (MS) approach, to predict time-series Normalized Difference Vegetation Index (NDVI). This was done using surface temperature, soil moisture and the interaction of surface temperature and soil moisture as regressors at monthly temporal resolution. Modelling was executed at the biome spatial (broad vegetation categories) scale. The results showed that the MS approach captured the non-linear dynamics in the data for each of the eight biomes considered in the study. The accuracy of the MS approach compared to non-switching modelling approach was evident in model comparison criteria including significance of parameter estimates, coefficient of determination (R2), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood as well as post-modelling diagnostics such as residual plots, autocorrelation function (ACF) and partial autocorrelation function (PACF) of residuals, and squared residuals. Overall, the study clearly demonstrates the superiority of MS modelling that captures non-linear relationships that may not be modelled using conventional non-switching modelling. Further studies are encouraged to test the approach at larger spatial scales.

Framework for agricultural performance assessment based on MODIS multitemporal data

We present a hierarchical classification framework for automated detection and mapping of spatial patterns of agricultural performance using satellite-based Earth observation data exemplified for the Aral Sea Basin (ASB) in Central Asia. The core element of the framework is the derivation of a composite agricultural performance index which is composed of different subindicators taking into account cropping intensity, crop diversity, crop rotations, fallow land frequency, land utilization, water use efficiency, and water availability. We derive these subindicators from net primary productivity and evapotranspiration data obtained from the MODIS sensor on board the Terra satellite during the observation period from 2000 to 2016, as well as from cropland maps created through multiannual classification of normalized difference vegetation index (NDVI). We classified pixel-based NDVI time series covering more than 8 × 106 ha of irrigated cropland based on a hierarchical approach concatenating unsupervised and supervised classification techniques to automatically generate and refine training labels, which are then used to train a decision fusion classifier, achieving an average overall accuracy of 78%. The results give unprecedented insights into spatial patterns of agricultural performance in the ASB. The proposed method is transferable and applicable for global-scale mapping, and the results of this remote sensing-aided assessment can provide important information for regional agricultural planning purposes.

Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS

Remote sensing of phenology usually works at the regional and global scales, which imposes considerable variations in the solar zenith angle (SZA) across space and time. Variations in SZA alters the shape and profile of the surface reflectance and vegetation index (VI) time series, but this effect on remote-sensing-derived vegetation phenology has not been adequately evaluated. The objective of this study is to understand the behaviour of VIs response to SZA, and to further improve the interpretation of satellite observed vegetation dynamics, across space and time. In this study, the sensitivity of two widely used VIs—the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—to SZA was investigated at four northern Australian savanna sites, over a latitudinal distance of 9.8° (~1100 km). Complete time series of surface reflectances, as acquired with different SZA configurations, were simulated using Bidirectional Reflectance Distribution Function (BRDF) parameters provided by MODerate Resolution Imaging Spectroradiometer (MODIS). The sun-angle dependency of the four phenological transition dates were assessed. Results showed that while NDVI was very sensitive to SZA, such sensitivity was nearly absent for EVI. A negative correlation was also observed between NDVI sensitivity to SZA and vegetation cover, with sensitivity declining to the same level as EVI when vegetation cover was high. Different sun-angle configurations resulted in considerable variations in the shape and magnitude of the phenological profiles. The sensitivity of VIs to SZA was generally greater during the dry season (with only active trees present) than in the wet season (with both active trees and grasses), thus, the sun-angle effect on VIs was phenophase-dependent. The sun-angle effect on NDVI time series resulted in considerable differences in the phenological metrics across different sun-angle configurations. Across four sites, the sun-angle effect caused 15.5 days, 21.6 days, and 20.5 days differences in the start, peak, and the end of the growing season derived from NDVI time series, with seasonally varying SZA at local solar noon, as compared to those metrics derived from NDVI time series with fixed SZA. In comparison, those differences in the start, peak, and end of the growing season for EVI were significantly smaller, with only 4.8 days, 4.9 days, and 3 days, respectively. Our results suggest the potential importance of considering the seasonal SZA effect on VI time series prior to the retrieval of phenological metrics.

Interannual and Seasonal Vegetation Changes and Influencing Factors in the Extra-High Mountainous Areas of Southern Tibet

The ecosystem of extra-high mountain areas is very fragile. Understanding local vegetation changes is crucial for projecting ecosystem dynamics. In this paper, we make a case for Himalayan mountain areas to explore vegetation dynamics and their influencing factors. Firstly, the interannual trends of the normalized difference vegetation index (NDVI) were extracted by the Ensemble Empirical Mode Decomposition (EEMD) algorithm and linear regression method. Moreover, the influence of environmental factors on interannual NDVI trends was assessed using the Random Forests algorithm and partial dependence plots. Subsequently, the time-lag effects of seasonal NDVI on different climatic factors were discussed and the effects of these factors on seasonal NDVI changes were determined by partial correlation analysis. The results show that (1) an overall weak upward trend was observed in NDVI variations from 1982 to 2015, and 1989 is considered to be the breakpoint of the NDVI time series; (2) interannual temperature trends and the shortest distance to large lakes were the most important factors in explaining interannual NDVI trends. Temperature trends were positively correlated with NDVI trends. The relationship between the shortest distance to large lakes and the NDVI trend is an inverted U-shaped; (3) the time-lags of NDVI responses to four climatic factors were shorter in Autumn than that in Summer. The NDVI responds quickly to precipitation and downward long-wave radiation; (4) downward long-wave radiation was the main climate factor that influenced NDVI changes in Autumn and the growing season because of the warming effect at night. This study is important to improve the understanding of vegetation changes in mountainous regions.

Exploring improvements to the design of an operational seasonal forage scarcity index from NDVI time series for livestock insurance in East Africa

Recurrent drought represents a major threat in arid and semi-arid regions of East Africa where pastoralists depend on their livestock for subsistence. In Kenya and southern Ethiopia, an existing satellite-based index insurance scheme aims to protect pastoralists against the adverse effects of drought. Under that scheme, payouts are made based on an area-aggregated seasonal forage scarcity index derived from remotely-sensed Normalized Difference Vegetation Index (NDVI). NDVI values are directly averaged per unit areas of insurance (UAI), which are based on administrative borders but take into limited account the ecological variability within the unit. The choice of administrative boundaries at the onset of the analysis may negatively impact the performance of the product. Our study explores an alternative index design based on an ecological stratification of the study area. First, we performed an unsupervised classification of NDVI time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) to group pixels with similar temporal NDVI trajectories. Next, we used average NDVI profiles and ancillary data to discard areas deemed insignificant for forage production. We then transformed NDVI values into z-scores to assess how each pixel relates to the multi-year distribution of NDVI values per class and season. In the final step, we calculated the alternative forage scarcity index, which is the percentage of pixels with anomalously low NDVI values per season and UAI (i.e. z-score ≤ −1.0). To evaluate its performance, we compared unit-level results for both the original and alternative designs against spatially-aggregated monthly household survey data on livestock mortality from 16 sample sites corresponding to eight administrative units within the study area. Besides performing better in predicting livestock mortality (i.e. increases of 53% and 39% in correlation coefficients as measured by Pearson’s r and Spearman’s ρ, respectively) and strengthening the ecological significance of the index, the proposed design has a number of other advantages: 1) the index is calculated using a much larger statistical basis, 2) it allows for analysis of forage conditions at sub-unit level, and 3) it offers a more flexible structure for payout calculation. These advantages could be of particular relevance for expanding the index-insurance scheme to agro-pastoral regions characterized by more heterogeneous landscapes. Finally, we propose to consider using this approach to better account for the ecological variability of rangelands in other NDVI-based early warning and monitoring systems.

Pattern Extraction of Topsoil and Subsoil Heterogeneity and Soil‐Crop Interaction Using Unsupervised Bayesian Machine Learning: An Application to Satellite‐Derived NDVI Time Series and Electromagnetic Induction Measurements

AbstractThe link between remotely sensed surface vegetation performances with the heterogeneity of subsurface physical properties is investigated by means of a Bayesian unsupervised learning approach. This question has considerable relevance and practical implications for precision agriculture as visible spatial differences in crop development and yield are often directly related to horizontal and vertical variations in soil texture caused by, for example, complex deposition/erosion processes. In addition, active and relict geomorphological settings, such as floodplains and buried paleochannels, can cast significant complexity into surface hydrology and crop modeling. This also requires a better approach to detect, quantify, and analyze topsoil and subsoil heterogeneity and soil‐crop interaction. In this work, we introduce a novel unsupervised Bayesian pattern recognition framework to address the extraction of these complex patterns. The proposed approach is first validated using two synthetic data sets and then applied to real‐world data sets of three test fields, which consists of satellite‐derived normalized difference vegetation index (NDVI) time series and proximal soil measurement data acquired by a multireceiver electromagnetic induction geophysical system. We show, for the first time, how the similarity and joint spatial patterns between crop NDVI time series and soil electromagnetic induction information can be extracted in a statistically rigorous means, and the associated heterogeneity and correlation can be analyzed in a quantitative manner. Some preliminary results from this study improve our understanding the link of above surface crop performance with the heterogeneous subsurface. Additional investigations have been planned for further testing the validity and generalization of these findings.

Comparison of Normalized Difference Vegetation Index Derived from Landsat, MODIS, and AVHRR for the Mesopotamian Marshes Between 2002 and 2018

The Mesopotamian marshes are a group of water bodies located in southern Iraq, in the shape of a triangle, with the cities Amarah, Nasiriyah, and Basra located at its corners. The marshes are appropriate habitats for a variety of birds and most of the commercial fisheries in the region. The normalized difference vegetation index (NDVI) has been derived using observations from various satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very-High-Resolution Radiometer (AVHRR), and Landsat over the Mesopotamian marshlands for the 17-year period between 2002 and 2018. We have chosen this time series (2002–2018) to monitor the change in vegetation of the study area since it is considered as a period of rehabilitation for the marshes (following a period when there was little to no water flowing into the marshes). Statistical analyses were performed to monitor the variability of the maximum biomass time (month of June). The results illustrated a strong positive correlation between the NDVI derived from Landsat, MODIS, and AVHRR. The statistical correlations were 0.79, 0.77, and 0.96 between Landsat and AVHRR, MODIS and AVHRR, and Landsat and MODIS, respectively. The linear slope of NDVI (Landsat, MODIS, and AVHRR) for each pixel over the period 2002–2018 displays a long-term trend of green biomass (NDVI) change in the study area, and the slope is slightly negative over most of the area. Slope values (−0.002 to −0.05) denote a slight decrease in the observed vegetation index over 17 years. The green biomass of the marshlands increased by 33.2% of the total area over 17 years. The areas of negative and positive slopes correspond to the same areas in slope map when calculated from Landsat, MODIS, and AVHRR, although they are different in spatial resolution (30 m, 1 km, and 5 km, respectively). The time series of the average NDVI (2002–2018) for three different sensors shows the highest and lowest NDVI values during the same years (for the month of June each year). The highest values were 0.19, 0.22, and 0.22 for Landsat, MODIS, and AVHRR, respectively, in 2006, and the lowest values were 0.09, 0.14, and 0.09 for Landsat, MODIS, and AVHRR, respectively, in 2003.

Vegetation Phenological Characterization of Alluvial Plain Shorea robusta-dominated Tropical Moist Deciduous Forest of Northeast India Using MODIS NDVI Time Series Data

The current study attempts to extract the phenological variables of alluvial plain Shorea robusta-dominated tropical moist deciduous forest of Northeast India using threshold-based method. A total of 230 Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 time series normalized difference vegetation index (NDVI) datasets (year 2006–2015) has been used. Time series NDVI datasets were fitted to an adaptive Savitzky–Golay filter for smoothing. Important phenological matrices, namely start of season (SOS), end of season (EOS), length of season (LOS), peak of season (POS), seasonal amplitude, seasonally integrals (large and small integral), were evaluated. SOS varied from 106 to 120 day of year (DOY) (average of 110 ± 17.6), and EOS varied from 425 to 441 DOY (average of 431 ± 14.33). POS reaches in the month of September and October (average 262 ± 15). In the current study, a mean amplitude of 0.35, lower value of small integral (4.70 ± 0.34) and higher value of large integral (16.24 ± 0.25) signify that the studied forest is highly productive ecosystem, with semievergreen or moist deciduous canopy. Strong linear relationship of NDVI with temperature and rainfall was witnessed, particularly with a 1–2 month time lag. Also, NDVI-temperature correlation was found stronger than NDVI-precipitation correlation, suggesting that the area being a humid subtropical region, temperature plays a greater role in the timing of phenological events than rainfall and can act as a crucial factor for growth of the species under the climate changing scenario.

Wheat Acreage Mapping and Yield Prediction Using Landsat-8 OLI Satellite Data: a Case Study in Sahibganj Province, Jharkhand (India)

The emergence of remote sensing technologies and availability of satellite data over three decades have facilitated to monitor and understand the agricultural systems in many intensive agricultural regions. Here, we performed a comprehensive study on utilization of multi-temporal satellite data (i.e., Landsat-8 and MODIS) for wheat acreage and yield estimation during winter season (2016–2017) over the Sahibganj District in Jharkhand (India). Phenological variables were derived using the time-series normalized difference vegetation index (NDVI), which helps to understand the phenological transitions of wheat. The NDVI profile was used to derive rules for decision tree classifier to map the acerage of wheat. The key findings indicate that the acerage of wheat was estimated as ~3870 ha. Further, the long-term wheat statistics data were used to derive a yield model. Based on this model, wheat production was predicted as ~4523 t for the winter season 2016–2017, while, the mean was 3482 t. Predicted wheat yield was as ~1.17 t/ha, which was underestimated by 0.07 t/ha. Thus, it can be concluded that the accuracy of yield prediction depends on the precision of wheat acerage map derived from remote sensing data. A significant challenge for accurate acerage mapping could be the coarser spatial resolution of satellite data as the average plot sizes of Indian farmers can be far smaller than pixel sizes of the satellite data. Nevertheless, this comprehensive case study inferred that satellite-derived wheat acerage can be preferred to predict yield instead of traditional-based survey estimates.

Using Sentinel-2 time series to detect slope movement before the Jinsha River landslide

Detecting slope movement before landslides occur in mountain regions is crucial for disaster reduction. In October 2018, a gigantic landslide occurred on the Jinsha River, causing dammed-lake breach flood 500 km downstream. In this work, we used 25 Sentinel-2 images from November 2015 to August 2018 to explore the capability of this high temporal resolution optical images in detecting slope movement before the Jinsha River landslide. Normalized difference vegetation index (NDVI) was calculated to composite temporal profiles using all Sentinel-2 images. With this NDVI time series, unsupervised K-means classifier was applied to initially classify the study area and find the best thresholds for automatically extracting landslide scars in the image series. These extracted landslide scars were validated using interpreted results from two high spatial resolution images of similar dates in 2015 (user’s accuracy 89.7%, producer’s accuracy 83.6%) and 2018 (user’s accuracy 90.8%, producer’s accuracy 74.9%). After validation, extracted landslide scars of different years were counted and displayed in an RGB composite image to highlight slope movement. In addition, monotonous decrease/increase of NDVI was also observed, indicating continuous expansion of landslide scarps and movement of landslide head along the slope on the landslide surface. This work demonstrated the capability of Sentinel-2 time series images to capture slope movement with short revisit time at low cost. By incorporating other environmental information (such as elevation), this proposed method has the potential to consistently map pre-landslide slope movements over a large region.

Spatiotemporal variation of correlation between vegetation cover and precipitation in an arid mountain-oasis river basin in northwest China

Water resources are essential for survival of both the ecosystem and human society in arid regions. The impact of precipitation on vegetation cover, especially in arid and semiarid areas, has received increasing interest. Although correlation between precipitation and vegetation cover has been reported in arid and semi-arid areas (with annual precipitation from 50 to 200 mm and from 200 to 400 mm, respectively), it is unclear whether the same correlation holds in extremely arid regions (with annual precipitation less than 50 mm). This study used remote sensing data of precipitation and normalized difference vegetation index (NDVI) to investigate their correlation at different temporal and spatial scales in a typical arid mountain-oasis river system located in northwest China. The results showed that precipitation and NDVI are not evenly distributed in space. In the period 1999–2007, precipitation was declining at a rate of 1.1785 mm/year, while during 2008–2015, it has increased at a rate of 2.0516 mm/year. NDVI showed no significant temporal trend in most areas (trend slope = 0.001, Significance > 0.05), except for a slight increase in regions where cropland expanded. The Pearson correlation coefficient between the time series of spatial mean precipitation and NDVI over the whole study area was 0.46 (Significance < 0.01), suggesting strong effects of precipitation on NDVI in the study area. The correlation coefficient between precipitation and NDVI varied spatially with the precipitation pattern in space. Through analysis of phase difference in space and time, it was found that NDVI in the lower reaches of the river system was significantly correlated with accumulative precipitation of the current and previous two months in the upper reaches of the system. Our study shows that the impacts of precipitation on vegetation cover have a clear phase difference in space, and that in an extremely arid area, antecedent precipitation in an adjacent region upstream has strong influences on vegetation cover downstream. Thus, the spatial distribution and antecedent accumulation of precipitation impacts should be taken into consideration when analyzing vegetation dynamics in arid areas.

An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series

The Normalized Difference Vegetation Index (NDVI) is one of the most commonly used vegetation indices for monitoring ecosystem dynamics and modeling biosphere processes. However, global NDVI products are usually provided with relatively coarse spatial resolutions that lack important spatial details. Producing NDVI time-series data with high spatiotemporal resolution is indispensable for monitoring land surfaces and ecosystem changes, especially in spatiotemporally heterogeneous areas. The Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method was developed in this study to fill this need. In accord with the distinctive characteristics of NDVIs with large data variance and high spatial autocorrelation compared with raw reflectance bands, the IFSDAF method first produces a time-dependent increment with linear unmixing and a space-dependent increment via thin plate spline interpolation. It then makes a final prediction by optimal integration of these two increments with the constrained least squares method. Moreover, the IFSDAF was developed with the capacity to use all available finer-scaled images, including those partly contaminated by clouds. NDVI images with coarse spatial resolution (MODIS) and fine spatial resolution (Landsat and Sentinel) in areas with great spatial heterogeneity and significant land cover changes were used to test the performance of the IFSDAF method. The root mean square error and relative root mean square error of predicted relative to observed results were 0.0884 and 22.12%, respectively, in heterogeneous areas, and 0.0546 and 25.77%, respectively, in areas of land-cover change. These promising results demonstrated the strength and robustness of the IFSDAF method in providing reliable NDVI datasets with high spatial and temporal resolution to support research on land surface processes. The efficiency of the proposed IFSDAF method can be greatly improved by using only the space-dependent increment. This simplification will make IFSDAF a feasible method for monitoring global vegetation.

Land tessellation effects in mapping agricultural areas by remote sensing at field level

ABSTRACT While mapping agricultural areas by remote sensing, it is quite common to operate at cadastral parcel level. Unfortunately, this land tessellation is merely administrative: a single parcel can, in fact, be made of differently managed parts whose spectral properties can be significantly different, being often different their content. In this situation, approaches that aggregate spectral signals of pixels belonging to the same parcel to investigate their average behaviour can generate misleading results. In this work, we evaluated how different field tessellation schemes can condition the interpretation of the spectral behaviour of crops with special concern on time series of NDVI (normalized difference vegetation index) and NDWI (normalized difference water index) spectral indices, which are assumed as proxies of plant vigour and crop/soil water content, respectively. The study relies on Sentinel 2 and Landsat 8 data imaging a rice-cultivated area sited in Piemonte (NW Italy). Two reference land tessellation geometries were taken into account: (a) the local cadastral map (purely administrative land division criterion) and (b) a map obtained by image segmentation of the NDVI time series (purely spectral land division criterion). After signal aggregation, some statistics were therefore computed to test differences both in time (within the same parcel along its temporal profile) and in space (within the same image at different positions at the same time). Results obtained exploring the rice growing season 2016 showed that (a) in 23% (70% at 1 sigma) and 27% (70% at 1 sigma) of segments (respectively for NDVI and NDWI) spectral differences, averagely along the year, are significant, possibly leading to wrong interpretation of occurring dynamics in the area; (b) in rice-cultivated fields, spectral differences suffer from seasonality with a higher incidence in Spring, when rice agronomic phases are more dynamic and, in the meantime, critical for management.