Special Issue "Advances of Multi-Temporal Remote Sensing in Vegetation and Agriculture Research"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editors

Dr. Jiali Shang
E-Mail Website
Guest Editor
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada960 Carling Ave. Ottawa, Ontario K1A 0C6, Canada
Interests: optical and radar integration for agriculture application
Dr. Jiangui Liu
E-Mail Website
Guest Editor
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Ave. Ottawa, Ontario K1A 0C6, Canada
Interests: remote sensing; crop and soil biophysical parameters estimation; crop productivity; agri-environmental sustainability assessment
Dr. Catherine Champagne
E-Mail Website
Guest Editor
Agriculture and Agri-Food Canada, Ottawa, ON, Canada
Interests: earth observation; agro-climate data; soil moisture; drought monitoring; crop yield estimation
Special Issues and Collections in MDPI journals
Dr. Taifeng Dong
E-Mail Website
Guest Editor
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Ave. Ottawa, Ontario K1A 0C6, Canada
Interests: remote sensing application in agriculture; bio-geophysical properties estimation; crop phenology; data assimilation
Dr. John Kovacs
E-Mail Website
Guest Editor
Department of Geography, Nipissing University North Bay, Ontario P1B 8L7, Canada
Interests: Mangrove forests; wetlands; radar remote sensing systems; optical remote sensing systems; hyperspectral remote sensing systems; Mexican Pacific; West Africa

Special Issue Information

Dear Colleagues,

The dynamics of vegetated lands reflect the combined effects of human beings, climate, and the environment. Remote sensing is able to obtain spatial-temporal information of land surface conditions; thus, it is considered a powerful tool to study the driving forces of vegetation dynamics. In more recent years, with the rapid development of hardware, software, and data analysis algorithms, the breadth and depth of remote sensing-based research and applications have expanded tremendously. In particular, with the increased emphasis on climate change and its associated ecological impact, there has been a rising demand in using multi-temporal remote sensing to study vegetation dynamics, greenhouse gas emission, and agricultural production and adaptation under frequent extreme weather events. From a precision farming perspective, multi-temporal remote sensing has been routinely used for monitoring crop growth conditions and pest and disease invasions.

This Special Issue invites contributions showcasing multi-temporal remote sensing applications from vegetation (forest, grassland, and wetland) and agriculture from various platforms (satellite, aircraft, and UAV), sensors (optical, thermal, and radar), and scales (global, national and regional), spanning a wide range of topics including but not limited to the following areas:

  • Long-term vegetation monitoring;
  • Assimilation of multi-temporal/sensor data into process-based crop/ecological models;
  • Loss of agricultural land;
  • Wetland dynamic;
  • Deforestation and afforestation;
  • Crop rotation patterns and cropping practices
  • Impact of drought/flood on crop production and soil environment;
  • UAV sensing in support of precision agriculture

Dr. Jiali Shang
Dr. Jiangui Liu
Dr. Catherine Champagne
Dr. Taifeng Dong
Dr. John Kovacs
Guest Editors

Manuscript Submission Information

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Published Papers (29 papers)

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Open AccessArticle
Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)
Remote Sens. 2020, 12(3), 488; https://doi.org/10.3390/rs12030488 - 03 Feb 2020
Abstract
It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected [...] Read more.
It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected by clouds and rainfall. To obtain a complete and continuous dataset on the spatiotemporal variations in LST in Africa, a reconstruction model based on the moderate resolution imaging spectroradiometer (MODIS) LST time series and ground station data was built to refactor the LST dataset (2003–2017). The first step in the reconstruction model is to filter low-quality LST pixels contaminated by clouds and then fill the pixels using observation data from ground weather stations. Then, the missing pixels are interpolated using the inverse distance weighting (IDW) method. The evaluation shows that the accuracy between reconstructed LST and ground station data is high (root mean square er–ror (RMSE) = 0.84 °C, mean absolute error (MAE) = 0.75 °C and correlation coefficient (R) = 0.91). The spatiotemporal analysis of the LST indicates that the change in the annual average LST from 2003–2017 was weak and the warming trend in Africa was remarkably uneven. Geographically, “the warming is more pronounced in the north and the west than in the south and the east”. The most significant warming occurred near the equatorial region in South Africa (slope > 0.05, R > 0.61, p < 0.05) and the central (slope = 0.08, R = 0.89, p < 0.05) regions, and a nonsignificant decreasing trend occurred in Botswana. Additionally, the mid-north region (north of Chad, north of Niger and south of Algeria) became colder (slope > −0.07, R = 0.9, p < 0.05), with a nonsignificant trend. Seasonally, significant warming was more pronounced in winter, mostly in the west, especially in Mauritania (slope > 0.09, R > 0.9, p < 0.5). The response of the different types of surface to the surface temperature has shown variability at different times, which provides important information to understand the effects of temperature changes on crop yields, which is critical for the planning of agricultural farming systems in Africa. Full article
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Open AccessArticle
Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China
Remote Sens. 2020, 12(2), 236; https://doi.org/10.3390/rs12020236 - 09 Jan 2020
Abstract
Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, [...] Read more.
Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops. Full article
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Open AccessArticle
Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China
Remote Sens. 2020, 12(2), 226; https://doi.org/10.3390/rs12020226 - 09 Jan 2020
Abstract
Currently, the main remote sensing-based crop mapping methods are based on spectral-temporal features. However, there has been a lack research on the selection of the multi-temporal images, and most of the methods are based on the use of all the available images during [...] Read more.
Currently, the main remote sensing-based crop mapping methods are based on spectral-temporal features. However, there has been a lack research on the selection of the multi-temporal images, and most of the methods are based on the use of all the available images during the cycle of crop growth. In this study, in order to explore the optimal temporal window for crop mapping with limited remote sensing data, we tested all possible combinations of temporal windows in an exhaustive manner, and made a comprehensive consideration of the spatial accuracy and statistical accuracy as evaluation indices. We collected all the available cloud-free Sentinel-2 multi-spectral images for the winter wheat and rapeseed growth periods in the study area in southern China, and used the random forest (RF) method as the classifier to identify the optimal temporal window. The spatial and statistical accuracies of all the results were assessed by using ground survey data and local agricultural census data. The optimal temporal window for the mapping of winter wheat and rapeseed in the study area was obtained by identifying the best-performing set of results. In addition, the variable importance (VI) index was used to evaluate the importance of the different bands for crop mapping. The results of the spatial accuracy, statistical accuracy, and the VI showed that the combinations of images from the later stages of crop growth were more suitable for crop mapping. Full article
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Open AccessArticle
Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data
Remote Sens. 2020, 12(1), 96; https://doi.org/10.3390/rs12010096 - 26 Dec 2019
Cited by 1
Abstract
Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop [...] Read more.
Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently. Full article
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Open AccessArticle
Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches
Remote Sens. 2020, 12(1), 21; https://doi.org/10.3390/rs12010021 - 18 Dec 2019
Abstract
Maize is an extremely important grain crop, and the demand has increased sharply throughout the world. China contributes nearly one-fifth of the total production alone with its decreasing arable land. Timely and accurate prediction of maize yield in China is critical for ensuring [...] Read more.
Maize is an extremely important grain crop, and the demand has increased sharply throughout the world. China contributes nearly one-fifth of the total production alone with its decreasing arable land. Timely and accurate prediction of maize yield in China is critical for ensuring global food security. Previous studies primarily used either visible or near-infrared (NIR) based vegetation indices (VIs), or climate data, or both to predict crop yield. However, other satellite data from different spectral bands have been underutilized, which contain unique information on crop growth and yield. In addition, although a joint application of multi-source data significantly improves crop yield prediction, the combinations of input variables that could achieve the best results have not been well investigated. Here we integrated optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield across four agro-ecological zones (AEZs) in China using a regression-based method (LASSO), two machine learning (ML) methods (RF and XGBoost), and deep learning (DL) network (LSTM). The results showed that combining multi-source data explained more than 75% of yield variation. Satellite data at the silking stage contributed more information than other variables, and solar-induced chlorophyll fluorescence (SIF) had an almost equivalent performance with the enhanced vegetation index (EVI) largely due to the low signal to noise ratio and coarse spatial resolution. The extremely high temperature and vapor pressure deficit during the reproductive period were the most important climate variables affecting maize production in China. Soil properties and management factors contained extra information on crop growth conditions that cannot be fully captured by satellite and climate data. We found that ML and DL approaches definitely outperformed regression-based methods, and ML had more computational efficiency and easier generalizations relative to DL. Our study is an important effort to combine multi-source remote sensed and environmental data for large-scale yield prediction. The proposed methodology provides a paradigm for other crop yield predictions and in other regions. Full article
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Open AccessArticle
Land Use Changes in the Zoige Plateau Based on the Object-Oriented Method and Their Effects on Landscape Patterns
Remote Sens. 2020, 12(1), 14; https://doi.org/10.3390/rs12010014 - 18 Dec 2019
Abstract
Land use/land cover change (LUCC) is the most direct driving force of landscape pattern change. The Zoige Plateau is a natural ecosystem with the largest high-altitude swamp wetland in China and its land use pattern has undergone great changes in recent years, but [...] Read more.
Land use/land cover change (LUCC) is the most direct driving force of landscape pattern change. The Zoige Plateau is a natural ecosystem with the largest high-altitude swamp wetland in China and its land use pattern has undergone great changes in recent years, but how the changes of each land use type affect the landscape pattern is uncertain. Here, we used the object-oriented method to extract land use information in 2015. Then, combined with land use data, the land use change characteristics from 2000 to 2015 were analyzed. We used the correlation analysis method to analyze the effects of land use changes on landscape pattern systematically. Three key conclusions were reached. (1) Land use information for the Zoige Plateau could be extracted with high accuracy by combining the object-oriented method and support vector machine (SVM). The overall accuracy was 93.2% and the Kappa coefficient was 0.889. (2) The comprehensive dynamic degree of land use was the highest from 2010 to 2015. From 2000 to 2015, the wetland area decreased the fastest because 57.05% of the wetlands were transferred out. Construction land increased the fastest, and the transferred in area from grassland and farmland were the main reason. (3) The effects of unused land, farmland, and construction land on the overall landscape pattern were stronger than that of the other types, among which farmland had the most significant impact (with a correlation coefficient of 0.959, p < 0.001). The change of unused land was the most highly significant factor associated with the landscape area pattern, and both the water body and unused land showed strong correlations with landscape shape pattern change. This suggested that the effects of land use types occupying a relatively small area on the landscape pattern were intensified. This study will provide guidance for the environmental management of local land resources and other natural ecosystem areas. Full article
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Open AccessArticle
Assessment of Leaf Area Index of Rice for a Growing Cycle Using Multi-Temporal C-Band PolSAR Datasets
Remote Sens. 2019, 11(22), 2640; https://doi.org/10.3390/rs11222640 - 12 Nov 2019
Abstract
C-band polarimetric synthetic aperture radar (PolSAR) data has been previously explored for estimating the leaf area index (LAI) of rice. Although the rice-growing cycle was partially covered in most of the studies, details for each phenological phase need to be further characterized. Additionally, [...] Read more.
C-band polarimetric synthetic aperture radar (PolSAR) data has been previously explored for estimating the leaf area index (LAI) of rice. Although the rice-growing cycle was partially covered in most of the studies, details for each phenological phase need to be further characterized. Additionally, the selection and exploration of polarimetric parameters are not comprehensive. This study evaluates the potential of a set of polarimetric parameters derived from multi-temporal RADARSAT-2 datasets for rice LAI estimation. The relationships of rice LAI with backscattering coefficients and polarimetric decomposition parameters have been examined in a complete phenological cycle. Most polarimetric parameters had weak relationships (R2 < 0.30) with LAI at the transplanting, reproductive, and maturity phase. Stronger relationships (R2 > 0.50) were observed at the vegetative phase. HV/VV and RVI FD had significant relationships (R2 > 0.80) with rice LAI for the whole growth period. They were utilized to develop empirical models. The best LAI inversion performance (RMSE = 0.81) was obtained when RVI FD was used. The acceptable error demonstrated the possibility to use the decomposition parameters for rice LAI estimation. The HV/VV-based model had a slightly lower estimation accuracy (RMSE = 1.29) but can be a practical alternative considering the wide availability of dual-polarized datasets. Full article
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Open AccessArticle
Assessing the Impact of Satellite Revisit Rate on Estimation of Corn Phenological Transition Timing through Shape Model Fitting
Remote Sens. 2019, 11(21), 2558; https://doi.org/10.3390/rs11212558 - 31 Oct 2019
Abstract
Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging [...] Read more.
Agricultural monitoring is an important application of earth-observing satellite systems. In particular, image time-series data are often fit to functions called shape models that are used to derive phenological transition dates or predict yield. This paper aimed to investigate the impact of imaging frequency on model fitting and estimation of corn phenological transition timing. Images (PlanetScope 4-band surface reflectance) and in situ measurements (Soil Plant Analysis Development (SPAD) and leaf area index (LAI)) were collected over a corn field in the mid-Atlantic during the 2018 growing season. Correlation was performed between candidate vegetation indices and SPAD and LAI measurements. The Normalized Difference Vegetation Index (NDVI) was chosen for shape model fitting based on the ground truth correlation and initial fitting results. Plot-average NDVI time-series were cleaned and fit to an asymmetric double sigmoid function, from which the day of year (DOY) of six different function parameters were extracted. These points were related to ground-measured phenological stages. New time-series were then created by removing images from the original time-series, so that average temporal spacing between images ranged from 3 to 24 days. Fitting was performed on the resampled time-series, and phenological transition dates were recalculated. Average range of estimated dates increased by 1 day and average absolute deviation between dates estimated from original and resampled time-series data increased by 1/3 of a day for every day of increase in average revisit interval. In the context of this study, higher imaging frequency led to greater precision in estimates of shape model fitting parameters used to estimate corn phenological transition timing. Full article
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Open AccessArticle
Estimating Rainfall Interception of Vegetation Canopy from MODIS Imageries in Southern China
Remote Sens. 2019, 11(21), 2468; https://doi.org/10.3390/rs11212468 - 23 Oct 2019
Abstract
The interception of rainfall by vegetation canopies plays an important role in the hydrologic process of ecosystems. Most estimates of canopy rainfall interception in present studies are mainly through field observations at the plot region. However, it is difficult, yet important, to map [...] Read more.
The interception of rainfall by vegetation canopies plays an important role in the hydrologic process of ecosystems. Most estimates of canopy rainfall interception in present studies are mainly through field observations at the plot region. However, it is difficult, yet important, to map the regional rainfall interception by vegetation canopy at a larger scale, especially in the southern rainy areas of China. To obtain a better understanding of the spatiotemporal variation of vegetation canopy rainfall interception with regard to the basin scale in this region, we extended a rainfall interception model by combining the observed rainfall data and moderate resolution imaging spectroradiometer leaf area index (MODIS_LAI) data to quantitatively estimate the vegetation canopy rainfall interception rate (CRIR) at small/medium basin scales in Guangdong Province, which is undergoing large changes in vegetation cover due to rapid urban expansion in the area. The results showed that the CRIR in Guangdong declined continuously during 2004–2012, but increased slightly in 2016, and the spatial variability of CRIR showed a diminishing yearly trend. The CRIR also exhibited a distinctive spatial pattern, with a higher rate to the east and west of the mountainous areas and a lower rate in the central mountainous and coastal areas. This pattern was more closely related to the spatial variation of the LAI than that of rainfall due to frequent extreme rainfall events saturating vegetation leaves. Further analysis demonstrated that forest coverage, instead of background climate, has a certain impact on the canopy rainfall interception, especially the proportion of broad-leaved forests in the basin, but more in-depth study is warranted in the future. In conclusion, the results of this study provide insights into the spatiotemporal variation of canopy rainfall interception at the basin scale of the Guangdong Province, and suggest that forest cover should be increased by adjusting the species composition to increase the proportion of native broad-leaved species based on the local condition within the basin. In addition, these results would be helpful in accurately assessing the impacts of forest ecosystems on regional water cycling, and provide scientific and practical implications for water resources management. Full article
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Open AccessArticle
Combining Evapotranspiration and Soil Apparent Electrical Conductivity Mapping to Identify Potential Precision Irrigation Benefits
Remote Sens. 2019, 11(21), 2460; https://doi.org/10.3390/rs11212460 - 23 Oct 2019
Abstract
Precision irrigation optimizes the spatiotemporal application of water using evapotranspiration (ET) maps to assess water stress or soil apparent electrical conductivity (ECa) maps as a proxy for plant available water content. However, ET and ECa maps are rarely used together. [...] Read more.
Precision irrigation optimizes the spatiotemporal application of water using evapotranspiration (ET) maps to assess water stress or soil apparent electrical conductivity (ECa) maps as a proxy for plant available water content. However, ET and ECa maps are rarely used together. We developed high-resolution ET and ECa maps for six irrigated fields in the Midwest United States between 2014–2016. Our research goals were to (1) validate ET maps developed using the High-Resolution Mapping of EvapoTranspiration (HRMET) model and aerial imagery via comparison with ground observations in potato, sweet corn, and pea agroecosystems; (2) characterize relationships between ET and ECa; and (3) identify potential precision irrigation benefits across rotations. We demonstrated the synergy of combined ET and ECa mapping for evaluating whether intrafield differences in ECa correspond to actual water use for different crop rotations. We found that ET and ECa have stronger relationships in sweet corn and potato rotations than field corn. Thus, sweet corn and potato crops may benefit more from precision irrigation than field corn, even when grown rotationally on the same field. We recommend that future research consider crop rotation, intrafield soil variability, and existing irrigation practices together when determining potential water use, savings, and yield gains from precision irrigation. Full article
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Open AccessArticle
Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada
Remote Sens. 2019, 11(20), 2419; https://doi.org/10.3390/rs11202419 - 18 Oct 2019
Abstract
This study investigated the estimation of grain yields of three major annual crops in Ontario (corn, soybean, and winter wheat) using MODIS reflectance data extracted with a general cropland mask and crop-specific masks. Time-series two-band enhanced vegetation index (EVI2) was derived from the [...] Read more.
This study investigated the estimation of grain yields of three major annual crops in Ontario (corn, soybean, and winter wheat) using MODIS reflectance data extracted with a general cropland mask and crop-specific masks. Time-series two-band enhanced vegetation index (EVI2) was derived from the 8 day composite 250 m MODIS reflectance data from 2003 to 2016. Using a general cropland mask, the strongest positive linear correlation between crop yields and EVI2 was observed at the end of July to early August, whereas a negative correlation was observed in spring. Using crop-specific masks, the time of the strongest positive linear correlation for winter wheat was found between mid-May and early June, corresponding to peak growth stages of the crop. EVI2 derived at peak growth stages of a crop provided good predictive capability for grain yield estimation, with considerable inter-annual variation. A multiple linear regression model was established for county-level yield estimation using EVI2 at peak growth stages and the year as independent variables. The model accounted for the spatiotemporal variability of grain yields of about 30% and 47% for winter wheat, 63% and 65% for corn, and 59% and 64% for soybean using the general cropland mask and crop-specific masks, respectively. A negative correlation during the spring indicated that vegetation index extracted using a general cropland mask should be used with caution in regions with mixed crops, as factors other than the growth conditions of the targeted crops may also be captured by remote sensing data. Full article
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Open AccessArticle
Large Scale Agricultural Plastic Mulch Detecting and Monitoring with Multi-Source Remote Sensing Data: A Case Study in Xinjiang, China
Remote Sens. 2019, 11(18), 2088; https://doi.org/10.3390/rs11182088 - 06 Sep 2019
Abstract
Plastic mulching has been widely practiced in crop cultivation worldwide due to its potential to significantly increase crop production. However, it also has a great impact on the regional climate and ecological environment. More importantly, it often leads to unexpected soil pollution due [...] Read more.
Plastic mulching has been widely practiced in crop cultivation worldwide due to its potential to significantly increase crop production. However, it also has a great impact on the regional climate and ecological environment. More importantly, it often leads to unexpected soil pollution due to fine plastic residuals. Therefore, accurately and timely monitoring of the temporal and spatial distribution of plastic mulch practice in large areas is of great interest to assess its impacts. However, existing plastic-mulched farmland (PMF) detecting efforts are limited to either small areas with high-resolution images or coarse resolution images of large areas. In this study, we examined the potential of cloud computing and multi-temporal, multi-sensor satellite images for detecting PMF in large areas. We first built the plastic-mulched farmland mapping algorithm (PFMA) rules through analyzing its spectral, temporal, and auxiliary features in remote sensing imagery with the classification and regression tree (CART). We then applied the PFMA in the dry region of Xinjiang, China, where a water resource is very scarce and thus plastic mulch has been intensively used and its usage is expected to increase significantly in the near future. The experimental results demonstrated that the PFMA reached an overall accuracy of 92.2% with a producer’s accuracy of 97.6% and a user’s accuracy of 86.7%, and the F-score was 0.914 for the PMF class. We further monitored and analyzed the dynamics of plastic mulch practiced in Xinjiang by applying the PFMA to the years 2000, 2005, 2010, and 2015. The general pattern of plastic mulch usage dynamic in Xinjiang during the period from 2000 to 2015 was well captured by our multi-temporal analysis. Full article
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Open AccessArticle
Intercomparison of AVHRR GIMMS3g, Terra MODIS, and SPOT-VGT NDVI Products over the Mongolian Plateau
Remote Sens. 2019, 11(17), 2030; https://doi.org/10.3390/rs11172030 - 29 Aug 2019
Abstract
The rapid development of remote sensing technology has promoted the generation of different vegetation index products, resulting in substantive accomplishment in comprehensive economic development and monitoring of natural environmental changes. The results of scientific experiments based on various vegetation index products are also [...] Read more.
The rapid development of remote sensing technology has promoted the generation of different vegetation index products, resulting in substantive accomplishment in comprehensive economic development and monitoring of natural environmental changes. The results of scientific experiments based on various vegetation index products are also different with the variation of time and space. In this work, the consistency characteristics among three global normalized difference vegetation index (NDVI) products, namely, GIMMS3g NDVI, MOD13A3 NDVI, and SPOT-VGT NDVI, are intercompared and validated based on Landsat 8 NDVI at biome and regional scale over the Mongolian Plateau (MP) from 2000 to 2014 by decomposing time series datasets. The agreement coefficient (AC) and statistical scores such as Pearson correlation coefficient, root mean square error (RMSE), mean bias error (MBE), and standard deviation (STD) are used to evaluate the consistency between three NDVI datasets. Intercomparison results reveal that GIMMS3g NDVI has the highest values basically over the MP, while SPOT-VGT NDVI has the lowest values. The spatial distribution of AC values between various NDVI products indicates that the three NDVI datasets are highly consistent with each other in the northern regions of the MP, and MOD13A3 NDVI and SPOT-VGT NDVI have better consistency in expressing vegetation cover and change trends due to the highest proportions of pixels with AC values greater than 0.6. However, the trend components of decomposed NDVI sequences show that SPOT-VGT NDVI values are about 0.02 lower than the other two datasets in the whole variation periods. The zonal characteristics show that GIMMS3g NDVI in January 2013 is significantly higher than those of the other two datasets. However, in July 2013, the three datasets are remarkably consistent because of the greater vegetation coverage. Consistency validation results show that values of SPOT-VGT NDVI agree more with Landsat 8 NDVI than GIMMS3g NDVI and MOD13A3 NDVI, and the consistencies in the northeast of the MP are higher than northwest regions. Full article
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Open AccessArticle
Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada
Remote Sens. 2019, 11(15), 1760; https://doi.org/10.3390/rs11151760 - 26 Jul 2019
Cited by 1
Abstract
Information on crop seeding date is required in many applications such as crop management and yield forecasting. This study presents a novel method to estimate crop seeding date at the field level from time-series 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data and growing [...] Read more.
Information on crop seeding date is required in many applications such as crop management and yield forecasting. This study presents a novel method to estimate crop seeding date at the field level from time-series 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data and growing degree days (GDD; base 5 ºC; ºC-days). The start of growing season (SOS) was first derived from time-series EVI2 (two-band Enhanced Vegetation Index) calculated from a MODIS 8-day composite surface reflectance product (MOD09Q1; Collection 6). Based on GDD calculated from the Daymet gridded estimates of daily weather parameters, a simple model was developed to establish a linkage between the observed seeding date and the SOS. Calibration and validation of the model was conducted on three major crops, spring wheat, canola and oats in the Province of Manitoba, Canada. The estimated SOS had a strong linear correlation with the observed seeding date; with a deviation of a few days depending on the year. The seeding date of the three crops can be calculated from the SOS by adjusting the number of days needed to accumulate GDD (AGDD) for emergence. The overall root-mean-square-difference (RMSD) of the estimated seeding date was less than 10 days. Validation showed that the accuracy of the estimated seeding date was crop-type independent. The developed method is useful for estimating the historical crop seeding date from remote sensing data in Canada to support studies of the interactions among seeding date, crop management and crop yield under climate change. It is anticipated that this method can be adapted to other crops in other locations using the same or different satellite data. Full article
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Open AccessArticle
Spatiotemporal Analysis of MODIS NDVI in the Semi-Arid Region of Kurdistan (Iran)
Remote Sens. 2019, 11(14), 1723; https://doi.org/10.3390/rs11141723 - 20 Jul 2019
Abstract
In this study, the spatiotemporal behavior of vegetation cover in the Kurdistan province of Iran was analyzed for the first time by TIMESAT and Breaks for Additive Season and Trend (BFAST) algorithms. They were applied on Normalized Vegetation Index (NDVI) time series from [...] Read more.
In this study, the spatiotemporal behavior of vegetation cover in the Kurdistan province of Iran was analyzed for the first time by TIMESAT and Breaks for Additive Season and Trend (BFAST) algorithms. They were applied on Normalized Vegetation Index (NDVI) time series from 2000 to 2016 derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The TIMESAT software package was used to estimate the seasonal parameters of NDVI and their relation to land covers. BFAST was applied for identifying abrupt changes (breakpoints) of NDVI and their magnitudes. The results from TIMESAT and BFAST were first reported separately, and then interpreted together. TMESAT outcomes showed that the lowest and highest amplitudes of NDVI during the whole time period happened in 2008 and 2010. The spatial distribution of the number of breakpoints showed different behaviors in the west and east of the study area, and the breakpoint frequency confirmed the extreme NDVI amplitudes in 2008 and 2010 found by TIMESAT. For the first time in Iran, a correlation analysis between accumulated precipitations and maximum NDVIs (from one to seven months before the NDVI maximum) was conducted. The results showed that precipitation one month before had a higher correlation with the maximum NDVIs in the region. Overall, the results describe the NDVI behavior in terms of greenness, lifetime, abrupt changes for the different land covers, and across the years, suggesting how the northwest and west of the study area can be more susceptible to drought conditions. Full article
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Open AccessArticle
Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method
Remote Sens. 2019, 11(14), 1699; https://doi.org/10.3390/rs11141699 - 18 Jul 2019
Abstract
Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice [...] Read more.
Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields. Full article
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Open AccessArticle
Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification
Remote Sens. 2019, 11(14), 1665; https://doi.org/10.3390/rs11141665 - 12 Jul 2019
Cited by 5
Abstract
Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the [...] Read more.
Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods. Full article
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Open AccessArticle
Relationship of Abrupt Vegetation Change to Climate Change and Ecological Engineering with Multi-Timescale Analysis in the Karst Region, Southwest China
Remote Sens. 2019, 11(13), 1564; https://doi.org/10.3390/rs11131564 - 02 Jul 2019
Abstract
Vegetation is known to be sensitive to both climate change and anthropogenic disturbance in the karst region. However, the relationship between an abrupt change in vegetation and its driving factors is unclear at multiple timescales. Based on the non-parametric Mann-Kendall test and the [...] Read more.
Vegetation is known to be sensitive to both climate change and anthropogenic disturbance in the karst region. However, the relationship between an abrupt change in vegetation and its driving factors is unclear at multiple timescales. Based on the non-parametric Mann-Kendall test and the ensemble empirical mode decomposition (EEMD) method, the abrupt changes in vegetation and its possible relationships with the driving factors in the karst region of southwest China during 1982–2015 are revealed at multiple timescales. The results showed that: (1) the Normalized Difference Vegetation Index (NDVI) showed an overall increasing trend and had an abrupt change in 2001. After the abrupt change, the greening trend of the NDVI in the east and the browning trend in the west, both changed from insignificant to significant. (2) After the abrupt change, at the 2.5-year time scale, the correlation between the NDVI and temperature changed from insignificantly negative to significantly negative in the west. Over the long-term trend, it changed from significantly negative to significantly positive in the east, but changed from significantly positive to significantly negative in the west. The abrupt change primarily occurred on the long-term trend. (3) After the abrupt change, 1143.32 km2 farmland was converted to forests in the east, and the forest area had significantly increased. (4) At the 2.5-year time scale, the abrupt change in the relationships between the NDVI and climate factors was primarily driven by climate change in the west, especially rising temperatures. Over the long-term trend, it was caused by ecological protection projects in the east, but by rising temperatures in the west. The integration of the abrupt change analysis and multiple timescale analysis help assess the relationship of vegetation changes with climate changes and human activities accurately and comprehensively, and deepen our understanding of the driving mechanism of vegetation changes, which will further provide scientific references for the protection of fragile ecosystems in the karst region. Full article
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Open AccessArticle
Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture
Remote Sens. 2019, 11(13), 1548; https://doi.org/10.3390/rs11131548 - 29 Jun 2019
Cited by 3
Abstract
Unmanned aerial vehicle (UAV) platforms with sensors covering the red-edge and near-infrared (NIR) bands to measure vegetation indices (VIs) have been recently introduced in agriculture research. Consequently, VIs originally developed for traditional airborne and spaceborne sensors have become applicable to UAV systems. In [...] Read more.
Unmanned aerial vehicle (UAV) platforms with sensors covering the red-edge and near-infrared (NIR) bands to measure vegetation indices (VIs) have been recently introduced in agriculture research. Consequently, VIs originally developed for traditional airborne and spaceborne sensors have become applicable to UAV systems. In this study, we investigated the difference in tillage treatments for cotton and sorghum using various RGB and NIR VIs. Minimized tillage has been known to increase farm sustainability and potentially optimize productivity over time; however, repeated tillage is the most commonly-adopted management practice in agriculture. To this day, quantitative comparisons of plant growth patterns between conventional tillage (CT) and no tillage (NT) fields are often inconsistent. In this study, high-resolution and multi-temporal UAV data were used for the analysis of tillage effects on plant health and the performance of various vegetation indices investigated. Time series data over ten dates were acquired on a weekly basis by RGB and multispectral (MS) UAV platforms: a DJI Phantom 4 Pro and a DJI Matrice 100 with the SlantRange 3p sensor. Ground reflectance panels and an ambient illumination sensor were used for the radiometric calibration of RGB and MS orthomosaic images, respectively. Various RGB and NIR-based vegetation indices were then calculated for the comparison between CT and NT treatments. In addition, a one-tailed Z-test was conducted to check the significance of VIs’ difference between CT and NT treatments. The results showed distinct differences in VIs between tillage treatments during the whole growing season. NIR-based VIs showed better discrimination performance than RGB-based VIs. Out of 13 VIs, the modified soil adjusted vegetation index (MSAVI) and optimized soil adjusted vegetation index (OSAVI) showed better performance in terms of quantitative difference measurements and the Z-test between tillage treatments. The modified green red vegetation index (MGRVI) and excess green (ExG) showed reliable separability and can be an alternative for economic RGB UAV application. Full article
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Open AccessArticle
Evaluation of Vegetation Biophysical Variables Time Series Derived from Synthetic Sentinel-2 Images
Remote Sens. 2019, 11(13), 1547; https://doi.org/10.3390/rs11131547 - 29 Jun 2019
Abstract
Time series of vegetation biophysical variables (leaf area index (LAI), fraction canopy cover (FCOVER), fraction of absorbed photosynthetically active radiation (FAPAR), canopy chlorophyll content (CCC), and canopy water content (CWC)) were estimated from interpolated Sentinel-2 (S2-LIKE) surface reflectance images, for an agricultural region [...] Read more.
Time series of vegetation biophysical variables (leaf area index (LAI), fraction canopy cover (FCOVER), fraction of absorbed photosynthetically active radiation (FAPAR), canopy chlorophyll content (CCC), and canopy water content (CWC)) were estimated from interpolated Sentinel-2 (S2-LIKE) surface reflectance images, for an agricultural region located in central Canada, using the Simplified Level 2 Product Prototype Processor (SL2P). S2-LIKE surface reflectance data were generated by blending clear-sky Sentinel-2 Multispectral Imager (S2-MSI) images with daily BRDF-adjusted Moderate Resolution Imaging Spectrometer images using the Prediction Smooth Reflectance Fusion Model (PSFRM), and validated using thirteen independent S2-MSI images (RMSE 6%). The uncertainty of S2-LIKE surface reflectance data increases with the time delay between the prediction date and the closest S2-MSI image used for training PSFRM. Vegetation biophysical variables from S2-LIKE products are validated qualitatively and quantitatively by comparison to the corresponding vegetation biophysical variables from S2-MSI products (RMSE = 0.55 for LAI, ~10% for FCOVER and FAPAR, and 0.13 g/m2 for CCC and 0.16 kg/m2 for CWC). Uncertainties of vegetation biophysical variables derived from S2-LIKE products are almost linearly related to the uncertainty of the input reflectance data. When compared to the in situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 field campaign, uncertainties of LAI (0.83) and FCOVER (13.73%) estimates from S2-LIKE products were slightly larger than uncertainties of LAI (0.57) and FCOVER (11.80%) estimates from S2-MSI products. However, equal uncertainties (0.32 kg/m2) were obtained for CWC estimates using SL2P with either S2-LIKE or S2-MSI input data. Full article
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Open AccessArticle
UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat
Remote Sens. 2019, 11(10), 1244; https://doi.org/10.3390/rs11101244 - 25 May 2019
Cited by 5
Abstract
Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial [...] Read more.
Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red–green–blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn’t any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield. Full article
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Open AccessArticle
Winter Wheat Canopy Height Extraction from UAV-Based Point Cloud Data with a Moving Cuboid Filter
Remote Sens. 2019, 11(10), 1239; https://doi.org/10.3390/rs11101239 - 24 May 2019
Cited by 2
Abstract
Plant height can be used as an indicator to estimate crop phenology and biomass. The Unmanned Aerial Vehicle (UAV)-based point cloud data derived from photogrammetry methods contains the structural information of crops which could be used to retrieve crop height. However, removing noise [...] Read more.
Plant height can be used as an indicator to estimate crop phenology and biomass. The Unmanned Aerial Vehicle (UAV)-based point cloud data derived from photogrammetry methods contains the structural information of crops which could be used to retrieve crop height. However, removing noise and outliers from the UAV-based crop point cloud data for height extraction is challenging. The objective of this paper is to develop an alternative method for canopy height determination from UAV-based 3D point cloud datasets using a statistical analysis method and a moving cuboid filter to remove outliers. In this method, first, the point cloud data is divided into many 3D columns. Secondly, a moving cuboid filter is applied in each column and moved downward to eliminate noise points. The threshold of point numbers in the filter is calculated based on the distribution of points in the column. After applying the moving cuboid filter, the crop height is calculated from the highest and lowest points in each 3D column. The proposed method achieved high accuracy for height extraction with low Root Mean Square Error (RMSE) of 6.37 cm and Mean Absolute Error (MAE) of 5.07 cm. The canopy height monitoring window for winter wheat using this method starts from the beginning of the stem extension stage to the end of the heading stage (BBCH 31 to 65). Since the height of wheat has limited change after the heading stage, this method could be used to retrieve the crop height of winter wheat. In addition, this method only requires one operation of UAV in the field. It could be an effective method that can be widely used to help end-user to monitor their crops and support real-time decision making for farm management. Full article
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Open AccessArticle
Evaluating the Potential of Multi-Seasonal CBERS-04 Imagery for Mapping the Quasi-Circular Vegetation Patches in the Yellow River Delta Using Random Forest
Remote Sens. 2019, 11(10), 1216; https://doi.org/10.3390/rs11101216 - 22 May 2019
Cited by 3
Abstract
High-resolution satellite imagery enables decametric-scale quasi-circular vegetation patch (QVP) mapping, which greatly aids the monitoring of vegetation restoration projects and the development of theories in pattern evolution and maintenance research. This study analyzed the potential of employing five seasonal fused 5 m spatial [...] Read more.
High-resolution satellite imagery enables decametric-scale quasi-circular vegetation patch (QVP) mapping, which greatly aids the monitoring of vegetation restoration projects and the development of theories in pattern evolution and maintenance research. This study analyzed the potential of employing five seasonal fused 5 m spatial resolution CBERS-04 satellite images to map QVPs in the Yellow River Delta, China, using the Random Forest (RF) classifier. The classification accuracies corresponding to individual and multi-season combined images were compared to understand the seasonal effect and the importance of optimal image timing and acquisition frequency for QVP mapping. For classification based on single season imagery, the early spring March imagery, with an overall accuracy (OA) of 98.1%, was proven to be more adequate than the other four individual seasonal images. The early spring (March) and winter (December) combined dataset produced the most accurate QVP detection results, with a precision rate of 66.3%, a recall rate of 43.9%, and an F measure of 0.528. For larger study areas, the gain in accuracy should be balanced against the increase in processing time and space when including the derived spectral indices in the RF classification model. Future research should focus on applying higher resolution imagery to QVP mapping. Full article
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Open AccessArticle
Quantitative Assessment of the Impact of Physical and Anthropogenic Factors on Vegetation Spatial-Temporal Variation in Northern Tibet
Remote Sens. 2019, 11(10), 1183; https://doi.org/10.3390/rs11101183 - 18 May 2019
Abstract
The alpine grassland on the Qinghai-Tibet Plateau covers an area of about 1/3 of China’s total grassland area and plays a crucial role in regulating grassland ecological functions. Both environmental changes and irrational use of the grassland can result in severe grassland degradation [...] Read more.
The alpine grassland on the Qinghai-Tibet Plateau covers an area of about 1/3 of China’s total grassland area and plays a crucial role in regulating grassland ecological functions. Both environmental changes and irrational use of the grassland can result in severe grassland degradation in some areas of the Qinghai-Tibet Plateau. However, the magnitude and patterns of the physical and anthropogenic factors in driving grassland variation over northern Tibet remain debatable, and the interactive influences among those factors are still unclear. In this study, we employed a geographical detector model to quantify the primary and interactive impacts of both the physical factors (precipitation, temperature, sunshine duration, soil type, elevation, slope, and aspect) and the anthropogenic factors (population density, road density, residential density, grazing density, per capita GDP, and land use type) on vegetation variation from 2000 to 2015 in northern Tibet. Our results show that the vegetation index in northern Tibet significantly decreased from 2000 to 2015. Overall, the stability of vegetation types was sorted as follows: the alpine scrub > the alpine steppe > the alpine meadow. The physical factors, rather than the anthropogenic factors, have been the primary driving factors for vegetation dynamics in northern Tibet. Specifically, meteorological factors best explained the alpine meadow and alpine steppe variation. Precipitation was the key factor that influenced the alpine meadow variation, whereas temperature was the key factor that contributed to the alpine steppe variation. The anthropogenic factors, such as population density, grazing density and per capita GDP, influenced the alpine scrub variation most. The influence of population density is highly similar to that of grazing density, which may provide convenient access to simplify the study of the anthropogenic activities in the Tibet plateau. The interactions between the driving factors had larger effects on vegetation than any single factor. In the alpine meadow, the interaction between precipitation and temperature can explain 44.6% of the vegetation variation. In the alpine scrub, the interaction between temperature and GDP was the highest, accounting for 27.5% of vegetation variation. For the alpine steppe, the interaction between soil type and population density can explain 29.4% of the vegetation variation. The highest value of vegetation degradation occurred in the range of 448–469 mm rainfall in the alpine meadow, 0.61–1.23 people/km2 in the alpine scrub and –0.83–0.15 °C in the alpine steppe, respectively. These findings could contribute to a better understanding of degradation prevention and sustainable development of the alpine grassland ecosystem in northern Tibet. Full article
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Open AccessArticle
The Mangrove Forests Change and Impacts from Tropical Cyclones in the Philippines Using Time Series Satellite Imagery
Remote Sens. 2019, 11(6), 688; https://doi.org/10.3390/rs11060688 - 22 Mar 2019
Cited by 1
Abstract
The Philippines is rich in mangrove forests, containing 50% of the total mangrove species of the world. However, the vast mangrove areas of the country have declined to about half of its cover in the past century. In the 1970s, action was taken [...] Read more.
The Philippines is rich in mangrove forests, containing 50% of the total mangrove species of the world. However, the vast mangrove areas of the country have declined to about half of its cover in the past century. In the 1970s, action was taken to protect the remaining mangrove forests under a government initiative, recognizing the ecological benefits mangrove forests can bring. Here, we examine two mangrove areas in the Philippines—Coron in Palawan and Balangiga-Lawaan in Eastern Samar over a 30-year period. Sets of Landsat images from 1987 to 2016 were classified and spatially analyzed using four landscape metrics. Additional analyses of the mangrove areas’ spatiotemporal dynamics were conducted. The impact of typhoon landfall on the mangrove areas was also analyzed in a qualitative manner. Spatiotemporal changes indicate that both the Coron and Balangiga-Lawaan mangrove forests, though declared as protected areas, are still suffering from mangrove area loss. Mangrove areal shrinkage and expansion can be attributed to both typhoon occurrence and management practices. Overall, our study reveals which mangrove forests need more responsive action, and provides a different perspective in understanding the spatiotemporal dynamics of these mangrove areas. Full article
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Review

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Open AccessReview
A Review of the Application of Remote Sensing Data for Abandoned Agricultural Land Identification with Focus on Central and Eastern Europe
Remote Sens. 2019, 11(23), 2759; https://doi.org/10.3390/rs11232759 - 23 Nov 2019
Abstract
This study aims to analyze and assess studies published from 1992 to 2019 and listed in the Web of Science (WOS) and Current Contents (CC) databases, and to identify agricultural abandonment by application of remote sensing (RS) optical and microwave data. We selected [...] Read more.
This study aims to analyze and assess studies published from 1992 to 2019 and listed in the Web of Science (WOS) and Current Contents (CC) databases, and to identify agricultural abandonment by application of remote sensing (RS) optical and microwave data. We selected 73 studies by applying structured queries in a field tag form and Boolean operators in the WOS portal and by expert analysis. An expert assessment yielded the topical picture concerning the definitions and criteria for the identification of abandoned agricultural land (AAL). The analysis also showed the absence of similar field research, which serves not only for validation, but also for understanding the process of agricultural abandonment. The benefit of the fusion of optical and radar data, which supports the application of Sentinel-1 and Sentinel-2 data, is also evident. Knowledge attained from the literary sources indicated that there exists, in the world literature, a well-covered problem of abandonment identification or biomass estimation, as well as missing works dealing with the assessment of the natural accretion of biomass in AAL. Full article
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Open AccessTechnical Note
A New Approach for Estimating Soil Salinity Using A Low-Cost Soil Sensor In Situ: A Case Study in Saline Regions of China’s East Coast
Remote Sens. 2020, 12(2), 239; https://doi.org/10.3390/rs12020239 - 10 Jan 2020
Abstract
Accurate and timely information on soil salinity is crucial for vegetation growth and agricultural productivity in coastal regions. This study investigates the potential of using Wifi POGO, an in situ electromagnetic sensor, for soil salinity assessment over saline coastal regions in eastern China. [...] Read more.
Accurate and timely information on soil salinity is crucial for vegetation growth and agricultural productivity in coastal regions. This study investigates the potential of using Wifi POGO, an in situ electromagnetic sensor, for soil salinity assessment over saline coastal regions in eastern China. The sensor readings, soil moisture, and temperature-corrected apparent electrical conductivity (ECa) were used to generate models for EC1:5 (a surrogate for soil salinity) estimation. Two salty areas with distinct soil textures, sandy loam (Shuntai) and clay (Dongxin), were selected. This study revealed that the difference between soil salinity and the in situ measured soil ECa (i.e., EC1:5-ECa) had a strong curvilinear relationship with soil moisture. Such a relationship allows for the direct estimation of soil salinity from soil ECa with the aid of soil moisture information. Both ECa and soil moisture can be measured in situ using a Wifi POGO, a low-cost ground-based soil sensor. By using the leave-one-out cross-validation (LOOCV), the achieved root mean square error (RMSE) and relative RMSE (RRMSE) in EC1:5 estimation were 0.0109 S/m and 19.24% respectively in Shuntai, and 0.0157 S/m and 16.05%, in Dongxin. This new method offers a simple, cost-effective and reliable tool for assessing soil salinity in dynamic coastal regions. Full article
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Open AccessTechnical Note
Field-Scale Rice Yield Estimation Using Sentinel-1A Synthetic Aperture Radar (SAR) Data in Coastal Saline Region of Jiangsu Province, China
Remote Sens. 2019, 11(19), 2274; https://doi.org/10.3390/rs11192274 - 29 Sep 2019
Abstract
In recent years, a large number of salterns have been converted into rice fields in the coastal region of Jiangsu Province, Eastern China. The high spatial heterogeneity of soil salinity has caused large within-field variabilities in grain yield of rice. The identification of [...] Read more.
In recent years, a large number of salterns have been converted into rice fields in the coastal region of Jiangsu Province, Eastern China. The high spatial heterogeneity of soil salinity has caused large within-field variabilities in grain yield of rice. The identification of low-yield areas within a field is an important initial step for precision farming. While optical satellite remote sensing can provide valuable information on crop growth and yield potential, the availability of cloud-free optical image data is often hampered by unfavorable weather conditions. Synthetic aperture radar (SAR) offers an alternative due to its nearly day-and-night and all-weather capability in data acquisition. Given the free data access of the Sentinels, this study aimed at developing a Sentinel-1A-based SAR index for rice yield estimation. The proposed SAR simple difference (SSD) index uses the change of the Sentinel-1A backscatter in vertical-horizontal (VH) polarization between the end of the tillering stage and the end of grain filling stage (SSDVH). A strong exponential relationship has been identified between the SSDVH and rice yield, producing accurate yield estimation with a root mean square error (RMSE) of 0.74 t ha−1 and a relative error (RE) of 7.93%. Full article
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Open AccessCase Report
An Agricultural Drought Index for Assessing Droughts Using a Water Balance Method: A Case Study in Jilin Province, Northeast China
Remote Sens. 2019, 11(9), 1066; https://doi.org/10.3390/rs11091066 - 06 May 2019
Cited by 2
Abstract
Drought, which causes the economic, social, and environmental losses, also threatens food security worldwide. In this study, we developed a vegetation-soil water deficit (VSWD) method to better assess agricultural droughts. The VSWD method considers precipitation, potential evapotranspiration (PET) and soil moisture. The soil [...] Read more.
Drought, which causes the economic, social, and environmental losses, also threatens food security worldwide. In this study, we developed a vegetation-soil water deficit (VSWD) method to better assess agricultural droughts. The VSWD method considers precipitation, potential evapotranspiration (PET) and soil moisture. The soil moisture from different soil layers was compared with the in situ drought indices to select the appropriate depths for calculating soil moisture during growing seasons. The VSWD method and other indices for assessing the agricultural droughts, i.e., Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI), were compared with the in situ and multi-scales of Standardized Precipitation Evapotranspiration Index (SPEIs). The results show that the VSWD method has better performance than SDCI, VHI, and TVDI. Based on the drought events collected from field sampling, it is found that the VSWD method can better distinguish the severities of agricultural droughts than other indices mentioned here. Moreover, the performances of VSWD, SPEIs, SDCI and VHI in the major historical drought events recorded in the study area show that VSWD has generated the most sensible results than others. However, the limitation of the VSWD method is also discussed. Full article
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