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19 pages, 13608 KiB

Open AccessArticle

Estimating Agricultural Cropping Intensity Using a New Temporal Mixture Analysis Method from Time Series MODIS

by Jianbin Tao, Xinyue Zhang, Yiqing Liu, Qiyue Jiang and Yang Zhou

Remote Sens. 2023, 15(19), 4712; https://doi.org/10.3390/rs15194712 - 26 Sep 2023

Viewed by 1043

Agricultural cropping intensity plays an important role in evaluating the food security and the sustainable development of agriculture. The existing indicators measuring cropping intensity include cropping frequency and multiple cropping index. As a nominal measurement, cropping frequency classifies crop patterns into single-cropping and/or [...] Read more.

Agricultural cropping intensity plays an important role in evaluating the food security and the sustainable development of agriculture. The existing indicators measuring cropping intensity include cropping frequency and multiple cropping index. As a nominal measurement, cropping frequency classifies crop patterns into single-cropping and/or double-cropping and leads to information loss. Multiple cropping index is calculated on the basis of statistical data, ignoring the spatial heterogeneity within the administrative region. Neither of these indicators can meet the requirements of precision agriculture, and new methods for fine cropping intensity mapping are still lacking. Time series remote sensing data provide vegetation phenology information and reveal temporal development of vegetation, which can be used to facilitate the fine cropping intensity mapping. In this study, a new temporal mixture analysis method is introduced to estimate the abundance level cropping intensity from time series remote sensing data. By analyzing phenological characteristics of major land-cover types in time series vegetatiosacan indices, a novel feature space was constructed by using the selected PCA components, and three unique endmembers (double-cropping, natural vegetations and water bodies) were found. Then, a linear spectral mixture analysis model was applied to decompose mixed pixels by replacing spectral data with multi-temporal data. The spatio-temporal continuous, fine resolution, abundance level cropping intensity maps were produced for the North China Plain and the middle and lower reaches of the Yangtze River Valley. The experiments indicate a good result at both county and pixel level validation. The method of manually delineating endmembers can well balance the accuracy and efficiency. We also found the size of the study area has little effect on the unmixing accuracy. The results demonstrated that the proposed method can model cropping intensity finely at large scale and long temporal span, at the same time with high efficiency and ease of implementation. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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17 pages, 10067 KiB

Open AccessArticle

Using a Vegetation Index as a Proxy for Reliability in Surface Reflectance Time Series Reconstruction (RTSR)

by Pieter Kempeneers, Martin Claverie and Raphaël d’Andrimont

Remote Sens. 2023, 15(9), 2303; https://doi.org/10.3390/rs15092303 - 27 Apr 2023

Viewed by 1553

Abstract

Time series of optical remote sensing data are instrumental for monitoring vegetation dynamics, but are hampered by missing or noisy observations due to varying atmospheric conditions. Reconstruction methods have been proposed, most of which focus on time series of a single vegetation index. [...] Read more.

Time series of optical remote sensing data are instrumental for monitoring vegetation dynamics, but are hampered by missing or noisy observations due to varying atmospheric conditions. Reconstruction methods have been proposed, most of which focus on time series of a single vegetation index. Under the assumption that relatively high vegetation index values can be considered as trustworthy, a successful approach is to adjust the smoothed value to the upper envelope of the time series. However, this assumption does not hold for surface reflectance in general. Clouds and cloud shadows result in, respectively, high and low values in the visible and near infrared part of the electromagnetic spectrum. A novel spectral Reflectance Time Series Reconstruction (RTSR) method is proposed. Smoothed values of surface reflectance values are adjusted to approach the trustworthy observations, using a vegetation index as a proxy for reliability. The Savitzky–Golay filter was used as the smoothing algorithm here, but different filters can be used as well. The RTSR was evaluated on 100 sites in Europe, with a focus on agriculture fields. Its potential was shown using different criteria, including smoothness and the ability to retain trustworthy observations in the original time series with RMSE values in the order of

0.01

to

0.03

in terms of surface reflectance. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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16 pages, 13281 KiB

Open AccessArticle

Convolutional Neural Network Maps Plant Communities in Semi-Natural Grasslands Using Multispectral Unmanned Aerial Vehicle Imagery

by Maren Pöttker, Kathrin Kiehl, Thomas Jarmer and Dieter Trautz

Remote Sens. 2023, 15(7), 1945; https://doi.org/10.3390/rs15071945 - 06 Apr 2023

Viewed by 2145

Abstract

Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in [...] Read more.

Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in grasslands. These different plant communities offer multiple ecosystem services and also have an effect on the forage value of fodder for domestic livestock. However, with increasing intensification in agriculture and the loss of SNGs, the biodiversity of grasslands continues to decline. In this paper, we present a method to spatially classify plant communities in grasslands in order to identify and map plant communities and weed species that occur in a semi-natural meadow. For this, high-resolution multispectral remote sensing data were captured by an unmanned aerial vehicle (UAV) in regular intervals and classified by a convolutional neural network (CNN). As the study area, a heterogeneous semi-natural hay meadow with first- and second-growth vegetation was chosen. Botanical relevés of fixed plots were used as ground truth and independent test data. Accuracies up to 88% on these independent test data were achieved, showing the great potential of the usage of CNNs for plant community mapping in high-resolution UAV data for ecological and agricultural applications. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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17 pages, 5155 KiB

Open AccessArticle

A Novel Vegetation Index Approach Using Sentinel-2 Data and Random Forest Algorithm for Estimating Forest Stock Volume in the Helan Mountains, Ningxia, China

by Taiyong Ma, Yang Hu, Jie Wang, Mukete Beckline, Danbo Pang, Lin Chen, Xilu Ni and Xuebin Li

Remote Sens. 2023, 15(7), 1853; https://doi.org/10.3390/rs15071853 - 30 Mar 2023

Cited by 7 | Viewed by 2691

Abstract

Forest stock volume (FSV) is a major indicator of forest ecosystem health and it also plays an important part in understanding the worldwide carbon cycle. A precise comprehension of the distribution patterns and variations of FSV is crucial in the assessment of the [...] Read more.

Forest stock volume (FSV) is a major indicator of forest ecosystem health and it also plays an important part in understanding the worldwide carbon cycle. A precise comprehension of the distribution patterns and variations of FSV is crucial in the assessment of the sequestration potential of forest carbon and optimization of the management programs of the forest carbon sink. In this study, a novel vegetation index based on Sentinel-2 data for modeling FSV with the random forest (RF) algorithm in Helan Mountains, China has been developed. Among all the other variables and with a correlation coefficient of r = 0.778, the novel vegetation index (NDVI_RE) developed based on the red-edge bands of the Sentinel-2 data was the most significant. Meanwhile, the model that combined bands and vegetation indices (bands + VIs-based model, BVBM) performed best in the training phase (R² = 0.93, RMSE = 10.82 m³ha⁻¹) and testing phase (R² = 0.60, RMSE = 27.05 m³ha⁻¹). Using the best training model, the FSV of the Helan Mountains was first mapped and an accuracy of 80.46% was obtained. The novel vegetation index developed based on the red-edge bands of the Sentinel-2 data and RF algorithm is thus the most effective method to assess the FSV. In addition, this method can provide a new method to estimate the FSV in other areas, especially in the management of forest carbon sequestration. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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25 pages, 5554 KiB

Open AccessArticle

Optimizing Wheat Yield Prediction Integrating Data from Sentinel-1 and Sentinel-2 with CatBoost Algorithm

by Asier Uribeetxebarria, Ander Castellón and Ana Aizpurua

Remote Sens. 2023, 15(6), 1640; https://doi.org/10.3390/rs15061640 - 17 Mar 2023

Cited by 6 | Viewed by 1909

Abstract

Accurately estimating wheat yield is crucial for informed decision making in precision agriculture (PA) and improving crop management. In recent years, optical satellite-derived vegetation indices (Vis), such as Sentinel-2 (S2), have become widely used, but the availability of images depends on the weather [...] Read more.

Accurately estimating wheat yield is crucial for informed decision making in precision agriculture (PA) and improving crop management. In recent years, optical satellite-derived vegetation indices (Vis), such as Sentinel-2 (S2), have become widely used, but the availability of images depends on the weather conditions. For its part, Sentinel-1 (S1) backscatter data are less used in agriculture due to its complicated interpretation and processing, but is not impacted by weather. This study investigates the potential benefits of combining S1 and S2 data and evaluates the performance of the categorical boosting (CatBoost) algorithm in crop yield estimation. The study was conducted utilizing dense yield data from a yield monitor, obtained from 39 wheat (Triticum spp. L.) fields. The study analyzed three S2 images corresponding to different crop growth stages (GS) GS30, GS39-49, and GS69-75, and 13 Vis commonly used for wheat yield estimation were calculated for each image. In addition, three S1 images that were temporally close to the S2 images were acquired, and the vertical-vertical (VV) and vertical-horizontal (VH) backscatter were calculated. The performance of the CatBoost algorithm was compared to that of multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) algorithms in crop yield estimation. The results showed that the combination of S1 and S2 data with the CatBoost algorithm produced a yield prediction with a root mean squared error (RMSE) of 0.24 t ha⁻¹, a relative RMSE (rRMSE) 3.46% and an R² of 0.95. The result indicates a decrease of 30% in RMSE when compared to using S2 alone. However, when this algorithm was used to estimate the yield of a whole plot, leveraging information from the surrounding plots, the mean absolute error (MAE) was 0.31 t ha⁻¹ which means a mean error of 4.38%. Accurate wheat yield estimation with a spatial resolution of 10 m becomes feasible when utilizing satellite data combined with CatBoost. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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20 pages, 5791 KiB

Open AccessArticle

How Sensitive Is Thermal Image-Based Orchard Water Status Estimation to Canopy Extraction Quality?

by Livia Katz, Alon Ben-Gal, M. Iggy Litaor, Amos Naor, Aviva Peeters, Eitan Goldshtein, Guy Lidor, Ohaliav Keisar, Stav Marzuk, Victor Alchanatis and Yafit Cohen

Remote Sens. 2023, 15(5), 1448; https://doi.org/10.3390/rs15051448 - 04 Mar 2023

Cited by 2 | Viewed by 2372

Abstract

Accurate canopy extraction and temperature calculations are crucial to minimizing inaccuracies in thermal image-based estimation of orchard water status. Currently, no quantitative comparison of canopy extraction methods exists in the context of precision irrigation. The accuracies of four canopy extraction methods were compared, [...] Read more.

Accurate canopy extraction and temperature calculations are crucial to minimizing inaccuracies in thermal image-based estimation of orchard water status. Currently, no quantitative comparison of canopy extraction methods exists in the context of precision irrigation. The accuracies of four canopy extraction methods were compared, and the effect on water status estimation was explored for these methods: 2-pixel erosion (2PE) where non-canopy pixels were removed by thresholding and morphological erosion; edge detection (ED) where edges were identified and morphologically dilated; vegetation segmentation (VS) using temperature histogram analysis and spatial watershed segmentation; and RGB binary masking (RGB-BM) where a binary canopy layer was statistically extracted from an RGB image for thermal image masking. The field experiments occurred in a four-hectare commercial peach orchard during the primary fruit growth stage (III). The relationship between stem water potential (SWP) and crop water stress index (CWSI) was established in 2018. During 2019, a large dataset of ten thermal infrared and two RGB images was acquired. The canopy extraction methods had different accuracies: on 12 August, the overall accuracy was 83% for the 2PE method, 77% for the ED method, 84% for the VS method, and 90% for the RGB-BM method. Despite the high accuracy of the RGB-BM method, canopy edges and between-row weeds were misidentified as canopy. Canopy temperature and CWSI were calculated using the average of 100% of canopy pixels (CWSI_T100%) and the average of the coolest 33% of canopy pixels (CWSI_T33%). The CWSI_T33% dataset produced similar SWP–CWSI models irrespective of the canopy extraction method used, while the CWSI_T100% yielded different and inferior models. The results highlighted the following: (1) The contribution of the RGB images is not significant for canopy extraction. Canopy pixels can be extracted with high accuracy and reliability solely with thermal images. (2) The T33% approach to canopy temperature calculation is more robust and superior to the simple mean of all canopy pixels. These noteworthy findings are a step forward in implementing thermal imagery in precision irrigation management. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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24 pages, 15367 KiB

Open AccessArticle

UAV-Hyperspectral Imaging to Estimate Species Distribution in Salt Marshes: A Case Study in the Cadiz Bay (SW Spain)

by Andrea Celeste Curcio, Luis Barbero and Gloria Peralta

Remote Sens. 2023, 15(5), 1419; https://doi.org/10.3390/rs15051419 - 02 Mar 2023

Cited by 7 | Viewed by 1782

Abstract

Salt marshes are one of the most productive ecosystems and provide numerous ecosystem services. However, they are seriously threatened by human activities and sea level rise. One of the main characteristics of this environment is the distribution of specialized plant species. The environmental [...] Read more.

Salt marshes are one of the most productive ecosystems and provide numerous ecosystem services. However, they are seriously threatened by human activities and sea level rise. One of the main characteristics of this environment is the distribution of specialized plant species. The environmental conditions governing the distribution of this vegetation, as well as its variation over time and space, still need to be better understood. In this way, these ecosystems will be managed and protected more effectively. Low-altitude remote sensing techniques are excellent for rapidly assessing salt marsh vegetation coverage. By applying a high-resolution hyperspectral imaging system onboard a UAV (UAV-HS), this study aims to differentiate between plant species and determine their distribution in salt marshes, using the salt marshes of Cadiz Bay as a case study. Hyperspectral processing techniques were used to find the purest spectral signature of each species. Continuum removal and second derivative transformations of the original spectral signatures highlight species-specific spectral absorption features. Using these methods, it is possible to differentiate salt marsh plant species with adequate precision. The elevation range occupied by these species was also estimated. Two species of Sarcocornia spp. were identified on the Cadiz Bay salt marsh, along with a class for Sporobolus maritimus. An additional class represents the transition areas from low to medium marsh with different proportions of Sarcocornia spp. and S. maritimus. S. maritimus can be successfully distinguished from soil containing microphytobenthos. The final species distribution map has up to 96% accuracy, with 43.5% of the area occupied by medium marsh species (i.e., Sarcocornia spp.) in the 2.30–2.80 m elevation range, a 29% transitional zone covering in 1.91–2.78 m, and 25% covered by S. maritims (1.22–2.35 m). Basing a method to assess the vulnerability of the marsh to SLR scenarios on the relationship between elevation and species distribution would allow prioritizing areas for rehabilitation. UAV-HS techniques have the advantage of being easily customizable and easy to execute (e.g., following extreme events or taking regular measurements). The UAV-HS data is expected to improve our understanding of coastal ecosystem responses, as well as increase our capacity to detect small changes in plant species distribution through monitoring. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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17 pages, 9160 KiB

Open AccessArticle

Cropland Productivity Evaluation: A 100 m Resolution Country Assessment Combining Earth Observation and Direct Measurements

by Nándor Csikós, Brigitta Szabó, Tamás Hermann, Annamária Laborczi, Judit Matus, László Pásztor, Gábor Szatmári, Katalin Takács and Gergely Tóth

Remote Sens. 2023, 15(5), 1236; https://doi.org/10.3390/rs15051236 - 23 Feb 2023

Cited by 2 | Viewed by 1549

Abstract

A methodology is presented for the quantitative assessment of soil biomass productivity at 100 m spatial resolution on a national scale. The traditional land evaluation approach—where crop yield is the dependent variable—was followed using measured yield and net primary productivity data derived from [...] Read more.

A methodology is presented for the quantitative assessment of soil biomass productivity at 100 m spatial resolution on a national scale. The traditional land evaluation approach—where crop yield is the dependent variable—was followed using measured yield and net primary productivity data derived from satellite images, together with digital soil and climate maps. In addition to characterizing of soil biomass productivity based on measured data, the weight of soil properties on productivity was also quantified to provide measured soil health and soil quality indicators as an information base for designing sustainable land management practices. To produce these results, we used only the Random Forest method for our calculations. The study considers high-input agriculture, which is predominant in the country. Biomass productivity indices for the main crops (wheat, maize and sunflowers) and general productivity indices were calculated for the whole agricultural area of Hungary. Results can be implemented in cadastral systems, in applied in agricultural and rural development programs. The assessment can be repeated for monitoring purposes to support general monitoring objectives as well as for reporting in relation to the United Nations Sustainable Development Goals. However, on the basis of the results, we also propose a method for periodically updating the assessment, which can also be used for monitoring biomass productivity in the context of climate change, land degradation and the development of cultivation technology. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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22 pages, 12350 KiB

Open AccessArticle

Potential of Satellite Spectral Resolution Vegetation Indices for Estimation of Canopy Chlorophyll Content of Field Crops: Mitigating Effects of Leaf Angle Distribution

by Xiaochen Zou, Jun Jin and Matti Mõttus

Remote Sens. 2023, 15(5), 1234; https://doi.org/10.3390/rs15051234 - 23 Feb 2023

Cited by 4 | Viewed by 1884

Abstract

Accurate estimation of canopy chlorophyll content (CCC) is critically important for agricultural production management. However, vegetation indices derived from canopy reflectance are influenced by canopy structure, which limits their application across species and seasonality. For horizontally homogenous canopies such as field crops, LAI [...] Read more.

Accurate estimation of canopy chlorophyll content (CCC) is critically important for agricultural production management. However, vegetation indices derived from canopy reflectance are influenced by canopy structure, which limits their application across species and seasonality. For horizontally homogenous canopies such as field crops, LAI and leaf inclination angle distribution or leaf mean tilt angle (MTA) are two biophysical characteristics determining canopy structure. Since CCC is relevant to LAI, MTA is the only structural parameter affecting the correlation between CCC and vegetation indices. To date, there are few vegetation indices designed to minimize MTA effects for CCC estimation. Herein, in this study, CCC-sensitive and MTA-insensitive satellite broadband vegetation indices are developed for crop canopy chlorophyll content estimation. The most efficient broadband vegetation indices for four satellite sensors (Sentinel-2, RapidEye, WorldView-2 and GaoFen-6) with red edge channels were identified (in the context of various vegetation index types) using simulated satellite broadband reflectance based on field measurements and validated with PROSAIL model simulations. The results indicate that developed vegetation indices present strong correlations with CCC and weak correlations with MTA, with overall R² of 0.76–0.80 and 0.84–0.95 for CCC and R² of 0.00 and 0.00–0.04 in the field measured data and model simulations, respectively. The best vegetation indices identified in this study are the soil-adjusted index type index SAI (B6, B7) for Sentinel-2, Verrelts’s three-band spectral index type index BSI-V (NIR1, Red, Red Edge) for WorldView-2, Tian’s three-band spectral index type index BSI-T (Red Edge, Green, NIR) for RapidEye and difference index type index DI (B6, B4) for GaoFen-6. The identified indices can potentially be used for crop CCC estimation across species and seasonality. However, real satellite datasets and more crop species need to be tested in further studies. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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25 pages, 16931 KiB

Open AccessArticle

Early Yield Forecasting of Maize by Combining Remote Sensing Images and Field Data with Logistic Models

by Hongfang Chang, Jiabing Cai, Baozhong Zhang, Zheng Wei and Di Xu

Remote Sens. 2023, 15(4), 1025; https://doi.org/10.3390/rs15041025 - 13 Feb 2023

Cited by 2 | Viewed by 1965

Abstract

Early forecasting of crop yield from field to region is important for stabilizing markets and safeguarding food security. Producing a precise forecasting result with fewer inputs is an ongoing goal for the large-area yield evaluation. We present one approach of yield prediction for [...] Read more.

Early forecasting of crop yield from field to region is important for stabilizing markets and safeguarding food security. Producing a precise forecasting result with fewer inputs is an ongoing goal for the large-area yield evaluation. We present one approach of yield prediction for maize that was explored by incorporating remote-sensing-derived land surface temperature (LST) and field in-season data into a series of logistic models with only a few parameters. Continuous observation data of maize were utilized to calibrate and validate the corresponding logistic models for regional biomass estimating based on field temperatures (including crop canopy temperature (Tc)) and relative dry/fresh biomass accumulation. The LST maps from MOD11A1 products, which are considered to be matched as Tc in large irrigation districts, were assimilated into the validated models to estimate the biomass accumulation. It was found that the temporal-scale difference between the instantaneous LST and the daily average value of field-measured Tc was eliminated by data normalization method, indicating that the normalized LST could be input directly into the model as an approximation of the normalized Tc. Making one observed biomass in-season as the driving force, the maximum of dry/fresh biomass accumulation (DBA/FBA) at harvest could be estimated. Then, grain yield forecasting could be achieved according to the local harvest index of maize. Silage and grain yields were evaluated reasonably well compared with field observations based on the regional map of LST values obtained in 2017 in Changchun, Jilin Province, China. Here, satisfactory grain and silage yield forecasting was provided by assimilating once measured value of DBA/FBA at the middle growth period (early August) into the model in advance of harvest. Meanwhile, good results were obtained in the application of this approach using field data in 2016 to predict grain yield ahead of harvest in the Jiefangzha sub-irrigation district, Inner Mongolia, China. This study demonstrated that maize yield can be forecasted accurately prior to harvest by assimilating remote-sensing-derived LST and field data into the logistic models at a regional scale considering the spatio-temporal scale extension of ground information and crop dynamic growth in real time. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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18 pages, 11528 KiB

Open AccessArticle

Quantifying Temperate Forest Diversity by Integrating GEDI LiDAR and Multi-Temporal Sentinel-2 Imagery

by Chunying Ren, Hailing Jiang, Yanbiao Xi, Pan Liu and Huiying Li

Remote Sens. 2023, 15(2), 375; https://doi.org/10.3390/rs15020375 - 07 Jan 2023

Cited by 6 | Viewed by 2545

Abstract

Remotely sensed estimates of forest diversity have become increasingly important in assessing anthropogenic and natural disturbances and their effects on biodiversity under limited resources. Whereas field inventories and optical images are generally used to estimate forest diversity, studies that combine vertical structure information [...] Read more.

Remotely sensed estimates of forest diversity have become increasingly important in assessing anthropogenic and natural disturbances and their effects on biodiversity under limited resources. Whereas field inventories and optical images are generally used to estimate forest diversity, studies that combine vertical structure information and multi-temporal phenological characteristics to accurately quantify diversity in large, heterogeneous forest areas are still lacking. In this study, combined with regression models, three different diversity indices, namely Simpson (λ), Shannon (H′), and Pielou (J′), were applied to characterize forest tree species diversity by using GEDI LiDAR data and Sentinel-2 imagery in temperate natural forest, northeast China. We used Mean Decrease Gini (MDG) and Boosted Regression Tree (BRT) to assess the importance of certain variables including monthly spectral bands, vegetation indices, foliage height diversity (FHD), and plant area index (PAI) of growing season and non-growing seasons (68 variables in total). We produced 12 forest diversity maps on three different diversity indices using four regression algorithms: Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Lasso Regression (LR). Our study concluded that the most important variables are FHD, NDVI, NDWI, EVI, short-wave infrared (SWIR) and red-edge (RE) bands, especially in the growing season (May and June). In terms of algorithms, the estimation accuracies of the RF (averaged R² = 0.79) and SVM (averaged R² = 0.76) models outperformed the other models (R² of KNN and LR are 0.68 and 0.57, respectively). The study demonstrates the accuracy of GEDI LiDAR data and multi-temporal Sentinel-2 images in estimating forest diversity over large areas, advancing the capacity to monitor and manage forest ecosystems. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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24 pages, 6281 KiB

Open AccessArticle

Effect of Snow Cover on Detecting Spring Phenology from Satellite-Derived Vegetation Indices in Alpine Grasslands

by Yiting Wang, Yuanyuan Chen, Pengfei Li, Yinggang Zhan, Rui Zou, Bo Yuan and Xiaode Zhou

Remote Sens. 2022, 14(22), 5725; https://doi.org/10.3390/rs14225725 - 12 Nov 2022

Cited by 3 | Viewed by 1479

Abstract

The accurate estimation of phenological metrics from satellite data, especially the start of season (SOS), is of great significance to enhance our understanding of trends in vegetation phenology under climate change at regional or global scales. However, for regions with winter snow cover, [...] Read more.

The accurate estimation of phenological metrics from satellite data, especially the start of season (SOS), is of great significance to enhance our understanding of trends in vegetation phenology under climate change at regional or global scales. However, for regions with winter snow cover, such as the alpine grasslands on the Tibetan Plateau, the presence of snow inevitably contaminates satellite signals and introduces bias into the detection of the SOS. Despite recent progress in eliminating the effect of snow cover on SOS detection, the mechanism of how snow cover affects the satellite-derived vegetation index (VI) and the detected SOS remains unclear. This study investigated the effect of snow cover on both VI and SOS detection by combining simulation experiments and real satellite data. Five different VIs were used and compared in this study, including four structure-based (i.e., NDVI, EVI2, NDPI, NDGI) VIs and one physiological-based (i.e., NIRv) VI. Both simulation experiments and satellite data analysis revealed that the presence of snow can significantly reduce the VI values and increase the local gradient of the growth curve, allowing the SOS to be detected. The bias in the detected SOS caused by snow cover depends on the end of the snow season (ESS), snow duration parameters, and the snow-free SOS. An earlier ESS results in an earlier estimate of the SOS, a later ESS results in a later estimate of the SOS, and an ESS close to the snow-free SOS results in small bias in the detected SOS. The sensitivity of the five VIs to snow cover in SOS detection is NDPI/NDGI < NIRv < EVI2 < NDVI, which has been verified in both simulation experiments and satellite data analysis. These findings will significantly advance our research on the feedback mechanisms between vegetation, snow, and climate change for alpine ecosystems. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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Review

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29 pages, 2071 KiB

Open AccessReview

Remote Sensing Monitoring of Rice and Wheat Canopy Nitrogen: A Review

by Jie Zheng, Xiaoyu Song, Guijun Yang, Xiaochu Du, Xin Mei and Xiaodong Yang

Remote Sens. 2022, 14(22), 5712; https://doi.org/10.3390/rs14225712 - 11 Nov 2022

Cited by 14 | Viewed by 3591

Abstract

Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for [...] Read more.

Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality. Full article

(This article belongs to the Special Issue Crops and Vegetation Monitoring with Remote/Proximal Sensing)

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