Normalized Difference Vegetation Index Series Research Articles

Short-Term Tree-Ring Series of Pinus hartwegii Lindl. Taken at Ground Level Correlate to Normalized Difference Vegetation Index Series

In this work, we propose that small wood core samples (≤10 cm length), taken from 0.3 m above the soil, represent an accurate means of correlating forest growth with remote sensing data. Short-term correlations between the Normalized Difference Vegetation Index (NDVI) and tree-ring components of the last three to four decades were tested, using 210 Pinus hartwegii wood cores collected at two locations, Tláloc (TLA) and Jocotitlán (JOCO) in central Mexico. The NDVI time series were generated with the Google Earth Engine (GEE) using Landsat 8 images. Also, seasonal trends in NDVI (e.g., spring, summer, autumn, winter) were analyzed through longitudinal analysis. The results showed more statistically significant dendrochronological indices in TLA than in JOCO, but both locations consistently showed an NDVI decrease in 2018 and 2020, indicating a reduction in vegetation vigor. At the two locations, the minimum and maximum NDVI occurred in April and October, respectively. Seasonal NDVI changes for spring were mainly seen at TLA with a decreasing trend, which may be related to a less defined dry season. The significant correlations (p < 0.05) between tree-ring components and the NDVI occurred in the dry season, indicating that the productivity of a given year is defined by the tree vigor shown in April and May, in the case of TLA, and between January and March, for JOCO. Although the NDVI values of JOCO were higher than those of TLA, tree growth, expressed by tree-ring indices, was lower. Our proposed field method to correlate tree-ring information and the NDVI is reliable and can be used in other coniferous forests.

Remote Sensing-Based Classification of Winter Irrigation Fields Using the Random Forest Algorithm and GF-1 Data: A Case Study of Jinzhong Basin, North China

Irrigation is one of the key agricultural management practices of crop cultivation in the world. Irrigation practice is traceable on satellite images. Most irrigated area mapping methods were developed based on time series of NDVI or backscatter coefficient within the growing season. However, it has been found that winter irrigation out of growing season is also dominating in north China. This kind of irrigation aims to increase the soil moisture for coping with spring drought and reduce the wind erosion in spring. This study developed a remote sensing-based classification approach to identify irrigated fields out of growing season with Radom Forest algorithm. Four spectral bands and all Normalized Difference Vegetation Index (NDVI) like indices computed from any two of these four bands for each of the seven scenes of GF-1 satellite data were used as the input features in the building of separated RF models and in applying the built models for the classification. The results showed that the mean of the highest out-of-bag accuracies for seven RF models was 94.9% and the mean of the averaged out-of-bag accuracies in the plateau for seven RF models was 94.1%; the overall accuracy for all seven classified outputs was in the range of 86.8–92.5%, Kappa in the range of 84.0–91.0% and F1-Score in the range of 82.1–90.1%. These results showed that the classification was neither overperformed nor underperformed as the accuracies of all classified images were lower than the model ones. This study also found that irrigation started to be applied as early as in November and irrigated fields were increased and suspended in December and January due to freezing conditions. The newly irrigated fields were found again in March and April when the temperature rose above zero degrees. The area of irrigated fields in the study area were increasing over time with sizes of 98.6, 166.9, 208.0, 292.8, 538.0, 623.1, 653.8 km2 from December to April, accounting for 6.1%, 10.4%, 12.9%, 18.2%, 33.4%, 38.7%, and 40.6% of the total irrigatable land in the study area, respectively. The results showed that the method developed in this study performed well. This study found on the satellite images that 40.6% of irrigatable fields were already irrigated before the sowing season and the irrigation authorities were supposed to improve their water supply capacity in the whole year with this information. This study may complement the traditional consideration of retrieving irrigation maps only in growing season with remote sensing images for a large area.

Monitoring vegetation dynamics (2010–2020) in Shengnongjia Forestry District with cloud-removed MODIS NDVI series by a spatio-temporal reconstruction method

Shengnongjia Forestry District is the national natural reserve with abundant biological resources in China. Monitoring its variations of vegetation for the intimate connection with eco-environmental changes is of great significance. In this paper, the 16-day composite MODIS normalized difference vegetation index (NDVI) products (MOD13A1) with 500 m resolution from 2011 to 2020 were selected to investigate the vegetation dynamics in Shengnongjia Forestry District. To alleviate the cloud contamination in NDVI products, a spatio-temporal prefill method with harmonic analysis of time series (ST-HANTS) is proposed. ST-HANTS first prefills the raw NDVI time series using spatio-temporal information to reduce data gaps, which effectively improves the reconstruction performance of the subsequent HANTS algorithm. In the simulation experiments, ST-HANTS shows the highest average correlation coefficient and the lowest average root mean square error compared with other mainstream methods, including HANTS, Savitzky–Golay filter, wavelet transform, and data assimilation. The reconstruction curves are close to the upper envelope of the NDVI time series, which is more consistent with the vegetation phenology and can effectively capture the key points in the growth cycle. By analyzing the cloud-free NDVI time series reconstructed with ST-HANTS, results reveal the overall trend of Shennongjia vegetation coverage is high in the middle while low at the edge. Except for the population centers and Hongping Airport, the NDVI of most areas is above 0.7 and shows a remarkable increasing tendency. Moreover, the fluctuation degree of NDVI in the whole study area is low, indicating that the ecological environment of Shennongjia is relatively stable. The vegetation variations are influenced by land surface temperature and precipitation, and the vegetation growth response to precipitation exhibits an apparent hysteresis.

Drought Assessment across Erbil Using Satellite Products

In this article, meteorological and agricultural droughts across the Erbil province, Iraq, were assessed using remote sensing data and satellite products. To this end, the long-term (2000–2022) Standardized Precipitation Evapotranspiration index (SPEI) at 1- and 3-month accumulation periods (SPEI-1 and SPEI-3) as well as the Normalized Difference Vegetation Index (NDVI) across Erbil were utilized. While the former was retrieved from the global SPEI data repository, the latter was derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The spatiotemporal variations in the SPEI indices indicated that two to nine extreme drought events occurred in the province with an increasing northward pattern. An increasing trend in the long-term NDVI series was also detected, having more diversity in vegetation coverage in the northern part of the province. The relationship between the SPEI and MODIS-NDVI was found to be positive but insignificant. Thus, we concluded that short-term meteorological droughts were not the only reason for the agricultural droughts in Erbil. Furthermore, the climate characteristics related to the cumulative water balance over a previous season is not an important trigger for the spatial variation in vegetation coverage across the province.

Use of time series normalized difference vegetation index (NDVI) to monitor fall armyworm (Spodoptera frugiperda) damage on maize production systems in Africa

Fall armyworm (FAW) Spodoptera frugiperda (J.E. Smith), damage was monitored at a regional scale using time series data in Western and Southern African countries. The study employed the normalized difference vegetation index (NDVI) computed from Landsat 8 imagery using the Google Earth Engine (GEE) using image composites for the years 2013 to 2020 for the study areas. The index was then reclassified based on the NDVI threshold values into low, sparse, moderate, and dense classes. FAW prevalence data were then utilized to validate the correlation between the FAW infestation and NDVI values. FAW was associated with a decrease in vegetation productivity between the years 2016, 2017, and 2018 when the pest infestation was reported in the study areas. The validation results showed that there is a correlation between FAW infestation and NDVI (R20.83). Our study highlighted that NDVI can be used as a proxy to quantify pest damage to vegetation productivity.

A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem

Information on crop yield is important for food security, in particular under the conditions of climate change and growing population worldwide. We developed a new fully distributed, high spatial resolution, model of biomass accumulation and crop yield applicable to a highly heterogeneous desert-oasis agroecosystem. The bulk of required input data is obtained by retrieving pixel-wise biogeophysical variables from a suite of very diverse satellite data. Both temperature and water stress conditions at field-scale are given full consideration, while the model was designed to strike a balance between model applicability and satisfactory characterization of the heterogeneous desert-oasis system to estimate field-scale yield. The development of this model relies on three main innovations. First, the start and end of the growing season were estimated for each pixel by calibrating the high spatial and temporal resolution observations of Normalized Difference Vegetation Index (NDVI) by Sentinal-2 (S2) MSI (Multi-Spectral Instrument) against limited local phenological information. Second, to monitor crop water stress, account taken of irrigation, a process-based water and energy balance model was applied to estimate the actual evapotranspiration (ET). This requires knowledge of soil water availability, which is characterized by downscaling the ASCAT (Advanced SCATterrometer) soil moisture data product. To capture the dominant features of the eco-hydrological conditions in the desert and oasis agroecosystem, ET was further downscaled from the 1 km resolution. Third, likewise the water stress indicator, the air temperature stress indicator was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system, by generating time series of air temperature at 1 km spatial resolution using the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) data product. In the temporal dimension, gaps were mitigated by applying time series analysis techniques to reconstruct cloud-free time series of LST, NDVI, fAPAR and albedo. These innovations add up to a high resolution characterization of crop response to the geospatial variability of weather and climate forcing in the desert-oasis agroecosystem. The model was applied to the dominant crops, i.e., spring wheat, maize, sunflower, and melon, in the oases of the Shiyang River Basin (northwestern China) characterized by a rather fragmented land use. The high resolution of pixel-wise ecohydrological parameters, i.e., crop phenology, temperature stress and water stress factors successfully reflect differences of crops with different phenology and location in the oases. The relative errors for wheat and maize yields compared to the census data are less than 5% at district level. At the county level, the relative errors of wheat yields of Liangzhou, Minqin, Gulang, Jinchuan, and Yongchang equal to 0.87%, 24.2%, 9.7%, 12.5%, and 7.2%. For maize, the dominant crop, the error on estimated yields was less than 5%, except in Gulang. The relative error on estimated yield for sunflower was less than 10% compared to agricultural census data. The relative error on estimated melon yield was 16%. This performance highlights the applicability of the model to estimate field-scale yields in agroecosystems characterized by fragmented land use.

Analysis of the Spatial and Temporal Evolution Patterns of Grassland Health and Its Driving Factors in Xilingol

The combination of natural environment changes and human activities affects the growth of grasslands. In order to quantitatively assess the causes of spatial and temporal variation of grasslands in Xilingol, this study assessed the spatial and temporal evolution patterns of grassland health based on MOD13A1 long time series Normalized Difference Vegetation Index (NDVI) data from 2000–2019 using trend analysis. The geodetector model was used to explore the dominant drivers of spatial variation in grassland NDVI, combined with 34 factors covering natural environmental changes and human disturbances over the same period. The results show that the grasslands of Xilingol showed an overall recovery trend from 2000 to 2019, with an average annual NDVI growth rate of 0.0028/a, a monthly increasing rate of 0.0005/month, and 68.06% of the grassland at an average recovery level. Moisture-dominated natural climate change factors, such as Growing Season Precipitation (Prep2), Annual Mean Water Vapor Pressure (WVP), and Annual Mean Relative Humidity (RH), were the underlying cause of grassland health changes during the study period, with the highest explanatory factor being growing season precipitation (q value of 0.59 on a multi-year average). The influence of primary production value among human activities was greater, and the explanatory factor of tertiary production value showed an increasing trend. The interactions among natural and anthropogenic factors significantly enhances their explanatory credibility for NDVI, with the type of interaction dominated by the two-factor enhancement. Risk detection of the top 10 dominant drivers in terms of q statistic were carried out to obtain the threshold range of each driver in the high zone of grassland NDVI, which can provide a scientific reference for the sustainable restoration of grassland.

A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps.

Study on air temperature estimation and its influencing factors in a complex mountainous area

Near-surface air temperature (Ta) is an important parameter in agricultural production and climate change. Satellite remote sensing data provide an effective way to estimate regional-scale air temperature. Therefore, taking Gansu section of the upper Weihe River Basin as the study area, using the filtered reconstructed high-quality long-time series normalized difference vegetation index (NDVI), interpolated reconstructed land surface temperature (LST), surface albedo, and digital elevation model (DEM) as the input data, the back-propagation artificial neural network algorithm (BP-ANN) was combined with a multiple linear regression method to estimate regional air temperature, and the influencing factors of air temperature estimation were analyzed. This method effectively compensates for the fact that air temperature data provided by a single station cannot represent regional air temperature information. The result shows that the temperature estimation accuracy is high. In terms of interannual variation, the air temperature in the study area showed a slightly increasing trend, with an average annual increase of 0.047°C. The calculation results of the interannual variation rate of temperature showed that the area with increased air temperature accounted for 75.8% of the total area. In terms of seasonal variation, compared with that in summer and winter, the air temperature rising trend in autumn was obvious, and the air temperature in the middle of the study area decreased in spring, which is prone to frost disasters. LST and NDVI in the study area were positively correlated with air temperature, and their positive correlation distribution areas accounted for 93.62% and 94.34% of the total study area, respectively. NDVI, LST and DEM influence the temperature change in the study area. The results show that there is a significant positive correlation between NDVI and air temperature, and the change of NDVI has a positive effect on the spatiotemporal variation of air temperature. The correlation coefficient between LST and air temperature in the southeast of the study area is negative, and there is a difference. In addition, the correlation coefficient between LST and air temperature in other areas of the study area is positive. The air temperature decreased with elevation, air temperature decreases by 0.27°C every hundred meters.

Vegetation changes in Yellow River Delta wetlands from 2018 to 2020 using PIE-Engine and short time series Sentinel-2 images

Vegetation is the functional subject in the wetland ecosystem and plays an irreplaceable role in biodiversity conservation. It is of great significance to monitor wetland vegetation for scientific assessment of the impact of vegetation on ecological environment and biodiversity. In this paper, a method for extracting wetland vegetation based on short time series Normalized Difference Vegetation Index (NDVI) data set was constructed. First, time series NDVI data were constructed using Sentinel-2 images. Then, the Support Vector Machine (SVM) classifier was used to classify the wetland vegetation types. The distributions of the main wetland vegetation in the study area in 2018 and 2020 were got. Finally, the land cover transfer matrix was calculated to analyze the spatial pattern and change of wetland vegetation emphatically from 2018 to 2020. Based on 46 Sentinel-2 images acquired in 2018 and 2020, the spatial pattern and change of vegetation in the Yellow River Delta wetlands were extracted and analyzed in this paper. The results show that: (1) The method for extracting wetland vegetation in estuary delta based on PIE-Engine platform and short time series NDVI data constructed in this paper can effectively extract the wetland vegetation information. The overall accuracy of the classification results reached 90.47% in 2018 and 80.30% in 2020. The Kappa coefficient of the classification results are 0.874 in 2018 and 0.739 in 2020 respectively. Compared with the results from the random forest classification method and the maximum likelihood classification method, the accuracy is improved by 6.40% and 13.04%, and the Kappa coefficient is improved by 0.055 and 0.069. (2) There were significant changes in vegetation coverage in the Yellow River Delta wetlands from 2018 to 2020. The Spartina alterniflora increased by 3.74km2. The Suaeda salsa degraded seriously, and the total area decreased by 20.38km2. In addition, the increase of Spartina alterniflora effectively guaranteed the stability of the coastline in the study area. This study can provide a theoretical basis for wetlands vegetation classificaton, and the classificaton results can provide scientific reference for protecting the ecological environment of wetlands and maintaining ecological stability.

Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine

An analysis of the landscape spatial structure and diversity in the mountain ranges of Northeast Siberia is essential to assess how tundra and boreal landscapes may respond to climate change and anthropogenic impacts in the vast mountainous permafrost of the Arctic regions. In addition, a precise landscape map is required for knowledge-based territorial planning and management. In this article, we aimed to explore and enhanced methods to analyse and map the permafrost landscape in Orulgan Ridge. The Google Earth Engine cloud platform was used to generate vegetation cover maps based on multi-fusion classification of Sentinel 2 MSI and Landsat 8 OLI time series data. Phenological features based on the monthly median values of time series Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Moisture Index (NDMI) were used to recognize geobotanical units according to the hierarchical concept of permafrost landscapes by the Support Vector Machine (SVM) classifier. In addition, geomorphological variables of megarelief (mountains and river valleys) were identified using the GIS-based terrain analysis and landform classification of the ASTER GDEM scenes mosaic. The resulting environmental variables made it possible to categorize nine classes of mountain permafrost landscapes. The result obtained was compared with previous permafrost landscape maps, which revealed a significant difference in distribution and spatial structure of intrazonal valleys and mountain tundra landscapes. Analysis of the landscape structure revealed a significant distribution of classes of mountain Larix-sparse forests and tundra. Landscape diversity was described by six longitudinal and latitudinal landscape hypsometric profiles. River valleys allow boreal–taiga landscapes to move up to high-mountainous regions. The features of the landscape structure and diversity of the ridge are noted, which, along with the specific spatial organization of vegetation and relief, can be of key importance for environmental monitoring and the study of regional variability of climatic changes.

Informational analysis of MODIS NDVI and EVI time series of sites affected and unaffected by wildfires

Vegetation dynamic processes influenced by fires has represented a scientific topic of great importance because of the environmental impact of fires. The use of satellites to investigate vegetational trends has become quite common for the wide coverage and high spatial resolution of satellite images. In this paper, we study the temporal variability of MODIS Aqua and Terra Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data acquired from 2000 to 2020 and covering the area burned by Camp Fire (California) in 2018. In 2008 other two fires, the BTU Lightning Complex Fire and the Humboldt Fire burned a large part of the same area. The objective of our study, thus, is the characterization of the time dynamics of MODIS Aqua and Terra NDVI and EVI series of sites affected by one and two wildfires by using the Fisher–Shannon (FS) method that allows to have information about the organization/disorder of the time series in relationship with the vegetation post-fire recovery process. Our results suggest that multiple fires cause a decrease of organization and order of NDVI and EVI, indicating a loss a regularity of their time variation.

Estimating Yield from NDVI, Weather Data, and Soil Water Depletion for Sugar Beet and Potato in Northern Belgium

Crop-yield models based on vegetation indices such as the normalized difference vegetation index (NDVI) have been developed to monitor crop yield at higher spatial and temporal resolutions compared to agricultural statistical data. We evaluated the model performance of NDVI-based random forest models for sugar beet and potato farm yields in northern Belgium during 2016–2018. We also evaluated whether weather variables and root-zone soil water depletion during the growing season improved the model performance. The NDVI integral did not explain early and late potato yield variability and only partly explained sugar-beet yield variability. The NDVI series of early and late potato crops were not sensitive enough to yield affecting weather and soil water conditions. We found that water-saturated conditions early in the growing season and elevated temperatures late in the growing season explained a large part of the sugar-beet and late-potato yield variability. The NDVI integral in combination with monthly precipitation, maximum temperature, and root-zone soil water depletion during the growing season explained farm-scale sugar beet (R2 = 0.84, MSE = 48.8) and late potato (R2 = 0.56, MSE = 57.3) yield variability well from 2016 to 2018 in northern Belgium.

Improved identification of cotton cultivated areas by applying instance-based transfer learning on the time series of MODIS NDVI

Cotton is an important cash crop and strategic material in the world, as the main source of natural and renewable fiber for textiles. Accurate and timely cotton distribution maps are crucial for monitoring and managing cotton cultivation system. Remotely sensed data have been widely used in cropland mapping, whereas relatively less attention has been paid specifically to cotton mapping partly due to the difficulty in obtaining the training samples over large regions. To resolve this issue, this study introduced the instance-based transfer learning to identify the cotton cultivated areas using the remotely sensed images. The annual time series of normalized difference vegetation index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images was adopted as input features. The random forest (RF) and long short-term memory (LSTM) algorithms integrated with the Transfer AdaBoost (TrAdaBoost) algorithm were adopted to generate the RF-based and LSTM-based TrAdaBoost approaches, respectively. The experiments were conducted in Arkansas State of the United States, where the cropland data layer (CDL) was available and utilized as a source of auxiliary data. The results showed that both the RF-based and LSTM-based TrAdaBoost performed better than the original RF and LSTM under the condition that the training samples in the target domain were limited. The advantages of transfer learning were much greater when the percentage of training samples from the source domain equals 80%. The two transfer learning approaches were then applied to identify the cotton cultivated areas in Uzbekistan. The cotton areas detected by MODIS images in 2018 were agreed well with the statistics at the sub-national level, with the R2 values of 0.57 and 0.64, respectively. These results demonstrate the potential of the RF-based and LSTM-based TrAdaBoost approaches in generating the cotton distribution maps when there are few samples in the target domain.

Off-peak NDVI correction to reconstruct Landsat time series for post-fire recovery in high-latitude forests

Collecting long-term satellite image series in high latitudes has been challenging due to its short growing season. For off-peak imagery, its reflective properties need to be corrected to maintain the spectral consistency. This study compares three statistical approaches to reconstructing a 30-year normalized difference vegetation index (NDVI) series for forest recovery assessment after the 1987 Black Dragon Fire in the Greater Hinggan Mountains Forest, Northeast China. To correct the off-peak NDVI to peak NDVI, the Landsat paired regression takes advantage of the scene-to-scene linear relationship between the two images, the GIMMS booster approach compensates the NDVI increment rate based on the 15-day AVHRR NDVI3g products between the two dates, and the climatic adjustment approach compensates the absolute NDVI change relying on the non-linear climatic influence on forest growth. The results find that the Landsat paired regression achieves the best performance with the image-wide residues within ± 0.2. The climatic adjustment picks the first-level NDVI under climatic control, while the GIMMS booster is heavily affected by the NDVI3g data quality. All approaches agree that the early-season (May-June) images are better sources for NDVI series reconstruction. The late-season images (especially October) are subject to fall senescence and early snowfall and therefore, are not recommended for satellite image series in high-latitude forests. The reconstructed NDVI series effectively corrects the off-season troughs along the trajectory. NDVI in burned forests increase rapidly in five years, and the heavily burned forests have the highest rate. Forest greenness could recover back to normal in ten years. This study confirms the feasibility of off-peak correction for building sparse image series. With advanced data availability such as the 5-day Sentinel-2 (10–60 m), daily MODIS imagery (500–1000 m), and hourly climatic reanalysis dataset (1–10 km), all three proposed approaches in this study could be improved for better application for post-fire monitoring of high-latitude forests.

Planetscope Nanosatellites Image Classification Using Machine Learning

To adopt sustainable crop practices in changing climate, understanding the climatic parameters and water requirements with vegetation is crucial on a spatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps are one of the highest resolution data that can transform agricultural practices and management on a large scale. High-resolution PS nanosatellite data was utilized in the current study to monitor agriculture’s spatiotemporal assessment for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVI was utilized to assess the vegetation pattern change in the study area. The current study area has sparse vegetation, and exposed soil exhibits brightness due to low soil moisture, constraining NDVI. Therefore, a machine learning (ML) based Random Forest (RF) classification model was used to compare the vegetation extent and computational cost of NDVI. The RF model has been compared with NDVI in the current investigation. It is one of the most precise classification methods because it can model the complexity of input variables, handle outliers, treat noise effectively, and avoid overfitting. Multinomial Logistic Regression (MLR) was implemented to compare the performance of both NDVI and RF-based classification. RF model provided good accuracy (98%) for all vegetation classes based on user accuracy, producer accuracy, and kappa coefficient.

Intellligent sustainable agricultural water practice using multi sensor spatiotemporal evolution

ABSTRACT The amount of water taken from non-renewable resources such as aquifers to fulfill irrigation requirements is rarely monitored, putting sustainable agriculture under threat in the face of changing climate. In the present research, an attempt was made to apply multi-sensor (Landsat suite, GRACE, GRACE-FO) satellite data to monitor spatiotemporal evolution of agriculture for the Al-Qassim region, Kingdom of Saudi Arabia (KSA). For this purpose, time series of NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index), and MSAVI2 (Modified Soil-Adjusted Vegetation 2) was utilized to assess vegetation pattern change in the study area. The present investigation used High-resolution Planetscope (PS) nanosatellite data to validate the vegetation results. Mann Kendall trend analysis and linear regression were performed to study the temporal pattern, and the relationship between vegetation, GRACE, and climate variables was performed from 1984 to 2020. Water extraction based on the averaged value of JPL GWS and CSR GWS showed a decreasing trend of −10.24 ± 1.4 mm/year from 2003-2020. The annual rainfall showed a decreasing trend, while the annual temperature showed an increasing trend from 1982-2020. The correlation of vegetation indices with rainfall of one-month lag showed a significantly better relationship of 0.74, 0.74, and 0.75, respectively, for NDVI, SAVI, and MSAVI2. The correlation between temperature and all three vegetation indices is a strong negative correlation: −0.85 for NDVI and −0.9 for SAVI and MSAVI.

Temporal Information Extraction for Afforestation in the Middle Section of the Yarlung Zangbo River Using Time-Series Landsat Images Based on Google Earth Engine

Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for these forests. The long-term archived Landsat images (including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI)) provide a good opportunity to capture the temporal change information about AF plantations. Under the condition that there would be an abrupt increasing trend in the normalized difference vegetation index (NDVI) time-series curve after afforestation, and this characteristic can be thought of as the indicator of the AF planting time. To extract the indicator, an algorithm based on the Google Earth Engine (GEE) for detecting this trend change point (TCP) on the maximum NDVI time series within the growing season (May to September) was proposed. In this algorithm, the time-series NDVI was initially smoothed and segmented into two subspaces. Then, a trend change indicator Sdiff was calculated with the difference between the fitting slopes of the subspaces before and after each target point. A self-adaptive method was applied to the NDVI series to find the right year with the maximum TCP, which is recorded as the AF planting time. Based on the proposed method, the AF planting time of the middle section of the YZR valley from 1988 to 2020 was derived. The detected afforestation temporal information was validated by 222 samples collected from the field survey, with a Pearson correlation coefficient of 0.93 and a root mean squared error (RMSE) of 2.95 years. Meanwhile, the area distribution of the AF planted each year has good temporal consistency with the implementation of the eco-reconstruction project. Overall, the study provides a good way to map AF planting times that is not only helpful for sustainable management of AF areas but also provides a basis for further research on the impact of afforestation on desertification control.

Using Temporal and Spatial Scales to Unravel the Effects of Climatic Factors on Vegetation Variations in China

Spatio-temporal variation of climatic factors generally contains spatial and temporal components that have different frequencies, which may significantly affect the overall variance structure of vegetation growth at the original scale. The objective of the study was to explore the temporal- and spatial-scale-specific relationships between vegetation growth and climatic factors based on the data of half-monthly normalized difference vegetation index (NDVI), half-monthly averaged daily mean temperature (DMT), half-monthly averaged daily range of temperature (DRT), and half-monthly accumulated precipitation (AP). The complete ensemble empirical mode decomposition (CEEMD) was used to decompose the temporal series of NDVI and climatic factors, and their temporal-scale-specific relationships were examined based on the original half-month scale. Two-dimensional empirical mode decomposition (2D-EMD) was used to decompose the spatial distributions of temporally averaged NDVI and climatic factors, and their spatial-scale-specific relationships were tested based on the original resolution of 1 km. The dominant temporal scales of NDVI were around 3, 15, and >15 years, while the dominant spatial scales of NDVI were around 2 × 104 and >10 × 104 km2. The temporal-scale-specific effects of climatic factors on NDVI were the strongest under mixed forest and were the weakest under broadleaf forest. On a 15-year time scale, NDVI was positively affected by DMT and AP at the 200–1,000 mm precipitation region and negatively affected by DRT at the 200–600 mm precipitation region. Temporal effects of climatic factors had the greatest effects on NDVI in the precipitation region of 200–600 mm and in Yunnan province, and 98.08% of the study area included multi-temporal scale effects. Relationships between NDVI and climatic factors at the half-month scale and other temporal scales were different under different elevation, latitude, longitude, land types, climatic regions, and precipitation. The spatial-scale-specific effects of climatic factors on NDVI were also differed, and the area with effects of the multi-spatial scale was about 64.38%. This indicated that multi-temporal scale and multi-spatial scale analysis could help to understand the mechanisms of effect of climatic factors on vegetation growth and provide the foundation for future vegetation restoration in fragile ecosystems.

Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping.

Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at five time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into two groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.