Temporal and Spatial Delineation the Rice Growing Stages for Cropping Calendar Estimation in the Southern of Vietnam using Remote Sensing

Background: MODerate resolution imaging spectroradiometer (MODIS) is the crucial instrument aboard. It provides global maps of several land surface characteristics. Method: The study uses MODIS to delineate the rice sowing and progress of the rice cropping calendar in the Vietnamese Mekong Delta. The study used multi-time series of normalized difference vegetation index (NDVI) images from 250 m spatial resolution MOD13Q1 images with 16-day combination to determine rice sowing/planting and harvesting schedules (from 01/01/2008 to 30/09/2009). Using 82 MODIS images, the study calculates the NDVI time series for rice sowing/transplanting stages in the Mekong Delta. Over time, the relationship between NDVI values and the rice cropping stages determines each cropping season starting and ending time. Result: As a result, we delineate three (3) major rice cropping systems and eight (8) cropping seasons. In which Main Winter-Spring and Early Summer-Autumn and Late Main Winter-Spring and Main Summer-Autumn cropping seasons dominated. MODIS satellite images are efficient and helpful for determining the current state of rice evolution. It is suitable for the regional or national level, which can provide quick and low-cost information for managers and decision-makers to select the proper strategies for crop management.

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Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series

Remote Sensing ◽

10.3390/rs11212497 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2497

Author(s):

Laura Recuero ◽

Javier Litago ◽

Jorge E. Pinzón ◽

Margarita Huesca ◽

Maria C. Moyano ◽

...

Keyword(s):

Time Series ◽

Spectral Analysis ◽

Land Surface ◽

Arid Regions ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Periodic Patterns ◽

Annual Cycles ◽

Ndvi Time Series ◽

Global Vegetation

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

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Monitoring Broadscale Vegetational Diversity and Change across North American Landscapes Using Land Surface Phenology

Forests ◽

10.3390/f11060606 ◽

2020 ◽

Vol 11 (6) ◽

pp. 606

Author(s):

Bjorn-Gustaf J. Brooks ◽

Danny C. Lee ◽

Lars Y. Pomara ◽

William W. Hargrove

Keyword(s):

Time Series ◽

Factor Analysis ◽

Coordinate Transformation ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Ecological Gradients ◽

Land Surface Phenology ◽

Polar Coordinate ◽

Vegetational Diversity

We describe a polar coordinate transformation of vegetation index profiles which permits a broad-scale comparison of location-specific phenological variability influenced by climate, topography, land use, and other factors. We apply statistical data reduction techniques to identify fundamental dimensions of phenological variability and to classify phenological types with intuitive ecological interpretation. Remote sensing-based land surface phenology can reveal ecologically meaningful vegetational diversity and dynamics across broad landscapes. Land surface phenology is inherently complex at regional to continental scales, varying with latitude, elevation, and multiple biophysical factors. Quantifying phenological change across ecological gradients at these scales is a potentially powerful way to monitor ecological development, disturbance, and diversity. Polar coordinate transformation was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series spanning 2000-2018 across North America. In a first step, 46 NDVI values per year were reduced to 11 intuitive annual metrics, such as the midpoint of the growing season and degree of seasonality, measured relative to location-specific annual phenological cycles. Second, factor analysis further reduced these metrics to fundamental phenology dimensions corresponding to annual timing, productivity, and seasonality. The factor analysis explained over 95% of the variability in the metrics and represented a more than ten-fold reduction in data volume from the original time series. In a final step, phenological classes (‘phenoclasses’) based on the statistical clustering of the factor data, were computed to describe the phenological state of each pixel during each year, which facilitated the tracking of year-to-year dynamics. Collectively the phenology metrics, factors, and phenoclasses provide a system for characterizing land surface phenology and for monitoring phenological change that is indicative of ecological gradients, development, disturbance, and other aspects of landscape-scale diversity and dynamics.

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Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel

International Journal of Wildland Fire ◽

10.1071/wf08078 ◽

2010 ◽

Vol 19 (1) ◽

pp. 75 ◽

Cited By ~ 78

Author(s):

Willem J. D. van Leeuwen ◽

Grant M. Casady ◽

Daniel G. Neary ◽

Susana Bautista ◽

José Antonio Alloza ◽

...

Keyword(s):

Time Series ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Series Data ◽

Data Interpretation ◽

Series Data ◽

Vegetation Response ◽

Burned Areas ◽

The Usa

Due to the challenges faced by resource managers in maintaining post-fire ecosystem health, there is a need for methods to assess the ecological consequences of disturbances. This research examines an approach for assessing changes in post-fire vegetation dynamics for sites in Spain, Israel and the USA that burned in 1998, 1999 and 2002 respectively. Moderate Resolution Imaging Spectroradiometer satellite Normalized Difference Vegetation Index (NDVI) time-series data (2000–07) are used for all sites to characterise and track the seasonal and spatial changes in vegetation response. Post-fire trends and metrics for burned areas are evaluated and compared with unburned reference sites to account for the influence of local environmental conditions. Time-series data interpretation provides insights into climatic influences on the post-fire vegetation. Although only two sites show increases in post-fire vegetation, all sites show declines in heterogeneity across the site. The evaluation of land surface phenological metrics, including the start and end of the season, the base and peak NDVI, and the integrated seasonal NDVI, show promising results, indicating trends in some measures of post-fire phenology. Results indicate that this monitoring approach, based on readily available satellite-based time-series vegetation data, provides a valuable tool for assessing post-fire vegetation response.

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Correction of Directional Effects in VEGETATION NDVI Time-Series

Remote Sensing ◽

10.3390/rs13061130 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1130

Author(s):

Jonathan León-Tavares ◽

Jean-Louis Roujean ◽

Bruno Smets ◽

Erwin Wolters ◽

Carolien Toté ◽

...

Keyword(s):

Time Series ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Global Scale ◽

Frequency Noise ◽

Surface Reflectance ◽

Computationally Efficient ◽

Land Surface Reflectance

Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we describe a BRDF correction methodology, for the purpose of the Copernicus Global Land Service framework, which includes notably an adaptive data accumulation window and provides uncertainties associated with the NDVI computed with normalized reflectance. Assessing the general performance of the methodology in comparing time-series between normalized and directional NDVI reveals a significant removal of the high-frequency noise due to directional effects. The proposed methodology is computationally efficient to operate at a global scale to deliver a BRDF-corrected NDVI product based on long-term Time-Series of VEGETATION sensor and its follow-on with the Copernicus Sentinel-3 satellite constellation.

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SACRA – a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI

Hydrology and Earth System Sciences ◽

10.5194/hess-19-4441-2015 ◽

2015 ◽

Vol 19 (11) ◽

pp. 4441-4461 ◽

Cited By ~ 6

Author(s):

S. Kotsuki ◽

K. Tanaka

Keyword(s):

Time Series ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Data Set ◽

Advantages And Disadvantages ◽

Crop Calendar ◽

Future Supply ◽

The Difference ◽

Numerical Estimations

Abstract. To date, many studies have performed numerical estimations of biomass production and agricultural water demand to understand the present and future supply–demand relationship. A crop calendar (CC), which defines the date or month when farmers sow and harvest crops, is an essential input for the numerical estimations. This study aims to present a new global data set, the SAtellite-derived CRop calendar for Agricultural simulations (SACRA), and to discuss advantages and disadvantages compared to existing census-based and model-derived products. We estimate global CC at a spatial resolution of 5 arcmin using satellite-sensed normalized difference vegetation index (NDVI) data, which corresponds to vegetation vitality and senescence on the land surface. Using the time series of the NDVI averaged from three consecutive years (2004–2006), sowing/harvesting dates are estimated for six crops (temperate-wheat, snow-wheat, maize, rice, soybean and cotton). We assume time series of the NDVI represent the phenology of one dominant crop and estimate CCs of the dominant crop in each grid. The dominant crops are determined using harvested areas based on census-based data. The cultivation period of SACRA is identified from the time series of the NDVI; therefore, SACRA considers current effects of human decisions and natural disasters. The difference between the estimated sowing dates and other existing products is less than 2 months (< 62 days) in most of the areas. A major disadvantage of our method is that the mixture of several crops in a grid is not considered in SACRA. The assumption of one dominant crop in each grid is a major source of discrepancy in crop calendars between SACRA and other products. The disadvantages of our approach may be reduced with future improvements based on finer satellite sensors and crop-type classification studies to consider several dominant crops in each grid. The comparison of the CC also demonstrates that identification of wheat type (sowing in spring or fall) is a major source of error in global CC estimations.

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A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽

10.3390/technologies9020040 ◽

2021 ◽

Vol 9 (2) ◽

pp. 40

Author(s):

Guang Yang ◽

Yuntao Ma ◽

Jiaqi Hu

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Extraction Methods ◽

Average Error ◽

Geographic Coordinate System ◽

Optimal Thresholds ◽

Moderate Resolution Imaging Spectroradiometer ◽

Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

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Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽

10.3390/atmos12010012 ◽

2020 ◽

Vol 12 (1) ◽

pp. 12

Author(s):

Yulia Ivanova ◽

Anton Kovalev ◽

Vlad Soukhovolsky

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Principal Component ◽

Stem Growth ◽

Forest Growth ◽

Parabolic Approximation ◽

New Approach ◽

The Relationship ◽

Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

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A Simple Method for Converting 1-km Resolution Daily Clear-Sky LST into Real LST

Remote Sensing ◽

10.3390/rs12101641 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1641

Author(s):

Yunfei Zhang ◽

Yunhao Chen ◽

Jing Li ◽

Xi Chen

Keyword(s):

Land Surface ◽

Missing Values ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Shortwave Radiation ◽

Simple Method ◽

The Real ◽

Independent Variables ◽

Clear Sky ◽

Reconstruction Methods

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

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An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data

Remote Sensing ◽

10.3390/rs12244181 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4181

Author(s):

Kunlun Xiang ◽

Wenping Yuan ◽

Liwen Wang ◽

Yujiao Deng

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Statistical Data ◽

Spatial Information ◽

Hydrological Cycle ◽

Mainland China ◽

Mean Annual Precipitation ◽

Irrigated Area ◽

Moderate Resolution

Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions.

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Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

Remote Sensing ◽

10.3390/rs13020323 ◽

2021 ◽

Vol 13 (2) ◽

pp. 323

Author(s):

Liang Chen ◽

Xuelei Wang ◽

Xiaobin Cai ◽

Chao Yang ◽

Xiaorong Lu

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Maximum Temperature ◽

Maximum Temperature Difference ◽

Rapid Urbanization ◽

The Impact ◽

The City

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

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