Research

20 pages, 4409 KiB

Open AccessArticle

Calibration of MODIS-Derived Cropland Growing Season Using the Climotransfer Function and Ground Observations

by Liming Ye, Johan De Grave, Eric Van Ranst and Lijun Xu

Remote Sens. 2023, 15(1), 72; https://doi.org/10.3390/rs15010072 - 23 Dec 2022

Cited by 2 | Viewed by 1662

The global environment experienced notable changes in the recent past of planet Earth. Satellite remote sensing has played an increasingly important role in monitoring and characterizing these changes. Being recognized as a sensitive indicator of global climate change, land surface phenology (LSP) observations [...] Read more.

The global environment experienced notable changes in the recent past of planet Earth. Satellite remote sensing has played an increasingly important role in monitoring and characterizing these changes. Being recognized as a sensitive indicator of global climate change, land surface phenology (LSP) observations by satellite remote sensing have received much attention in recent years; however, much less attention has been paid to the calibration of these observations using standardized procedures. Here, we propose a new approach to calibrating the satellite LSP products by developing a climotransfer function (CTF) based on a polynomial regression of the satellite-ground observation difference in key crop phenophases against climatic factors. We illustrate the model development and evaluation process with a case study of the cropland growing season in Northeast China (NEC) from 2001 to 2010 using the MODIS LSP product MCD12Q2 Collection 6 and the ground-observed crop phenology and climatic data from 98 agrometeorological stations across the region. Our results showed that the start of the cropland growing season (SOS) derived from MODIS data compared well to the ground-observed SOS, whereas the MODIS-derived season end (EOS) was delayed by 15.5 d, relative to ground observation. The MODIS-derived EOS was, therefore, spatiotemporally calibrated using a CTF model fitted to the satellite-ground difference in EOS (∆EOS) versus two climatic factors, namely, the growing degree-days on the base temperature of 10 °C (GDD10) and cloud cover (CL). The calibrated MODIS data revealed that the cropland growing season in NEC tended to shorten at 4.5 d decade⁻¹ during 2001–2010, mainly driven by a significant delay in SOS at a similar rate, whereas no trend was detected for EOS. The calibrated data also revealed a significant shortening gradient of 1.7 d degree⁻¹ of latitude northward. These spatiotemporal patterns would have been erroneously characterized if calibration had not been applied. More attention is therefore called to the proper calibration of satellite LSP products prior to any meaningful applications. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)

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20 pages, 6369 KiB

Open AccessArticle

A Novel Workflow for Crop Type Mapping with a Time Series of Synthetic Aperture Radar and Optical Images in the Google Earth Engine

by Linghui Guo, Sha Zhao, Jiangbo Gao, Hebing Zhang, Youfeng Zou and Xiangming Xiao

Remote Sens. 2022, 14(21), 5458; https://doi.org/10.3390/rs14215458 - 30 Oct 2022

Cited by 5 | Viewed by 2059

Abstract

High-resolution crop type mapping is of importance for site-specific agricultural management and food security in smallholder farming regions, but is challenging due to limited data availability and the need for image-based algorithms. In this paper, we developed an efficient object- and pixel-based mapping [...] Read more.

High-resolution crop type mapping is of importance for site-specific agricultural management and food security in smallholder farming regions, but is challenging due to limited data availability and the need for image-based algorithms. In this paper, we developed an efficient object- and pixel-based mapping algorithm to generate a 10 m resolution crop type map over large spatial domains by integrating time series optical images (Sentinel-2) and synthetic aperture radar (SAR) images (Sentinel-1) using the Google Earth Engine (GEE) platform. The results showed that the proposed method was reliable for crop type mapping in the study area with an overall accuracy (OA) of 93.22% and a kappa coefficient (KC) of 0.89. Through experiments, we also found that the monthly median values of the vertical transmit/vertical receive (VV) and vertical transmit/horizontal receive (VH) bands were insensitive to crop type mapping itself, but adding this information to supplement the optical images improved the classification accuracy, with an OA increase of 0.09–2.98%. Adding the slope of vegetation index change (VI_slope) at the critical period to crop type classification was obviously better than that of relative change ratio of vegetation index (VI_ratio), both of which could make an OA improvement of 2.58%. These findings not only highlighted the potential of the VI_slope and VI_ratio indices during the critical period for crop type mapping in small plots, but suggested that SAR images could be included to supplement optical images for crop type classification. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)

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20 pages, 7934 KiB

Open AccessArticle

Satellite-Based Evidences to Improve Cropland Productivity on the High-Standard Farmland Project Regions in Henan Province, China

by Huimin Yan, Wenpeng Du, Ying Zhou, Liang Luo and Zhong’en Niu

Remote Sens. 2022, 14(7), 1724; https://doi.org/10.3390/rs14071724 - 02 Apr 2022

Cited by 8 | Viewed by 2291

Abstract

Under the pressure of limited arable land and increasing demand for food, improving the quality of existing arable land has become a priority to ensure food security. The Chinese government gives great importance to improving cropland productivity by focusing on the construction of [...] Read more.

Under the pressure of limited arable land and increasing demand for food, improving the quality of existing arable land has become a priority to ensure food security. The Chinese government gives great importance to improving cropland productivity by focusing on the construction of high-standard farmland (HSF). The government puts forward the goal of constructing 1.2 billion mu (100 mu ≈ 6.67 hectares) of HSF by 2030. Therefore, how to apply remote sensing to monitor the ability to increase and stabilize yields in HSF project regions has become an essential task for proving the efficiency of HSF construction. Based on HSF project distribution data, Moderate Resolution Imaging Spectroradiometer (MODIS) data and Landsat-8 Operational Land Imager (Landsat8-OLI) data, this study develops a method to monitor cropland productivity improvement by measuring cropland productivity level (CPL), disaster resistance ability (DRA) and homogeneous yield degree (HYD) in the HSF project region. Taking China’s largest grain production province (Henan Province) as a case study area, research shows that a light use efficiency model that includes multiple cropping data can effectively detect changes in cropland productivity before and after HSF construction. Furthermore, integrated Landsat8-OLI and MODIS data can detect changes in DRA and HYD before and after HSF construction with higher temporal and spatial resolution. In 109 HSF project regions concentrated and distributed in contiguous regions in Henan Province, the average cropland productivity increased by 145 kg/mu; among the eight sample project regions, DRA was improved in seven sample project regions; and the HYD in all eight sample project regions was greatly improved (the degree of increase is more than 75%). This evidence from satellites proves that the Chinese HSF project has significantly improved the CPL, DRA and HYD of cropland, while this study also verifies the practicability of the three indices to monitor the efficiency of HSF construction. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)

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16 pages, 2148 KiB

Open AccessArticle

Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data

by Babak Ghassemi, Aleksandar Dujakovic, Mateusz Żółtak, Markus Immitzer, Clement Atzberger and Francesco Vuolo

Remote Sens. 2022, 14(3), 541; https://doi.org/10.3390/rs14030541 - 23 Jan 2022

Cited by 27 | Viewed by 4986

Abstract

One of the most challenging aspects of obtaining detailed and accurate land-use and land-cover (LULC) maps is the availability of representative field data for training and validation. In this manuscript, we evaluate the use of the Eurostat Land Use and Coverage Area frame [...] Read more.

One of the most challenging aspects of obtaining detailed and accurate land-use and land-cover (LULC) maps is the availability of representative field data for training and validation. In this manuscript, we evaluate the use of the Eurostat Land Use and Coverage Area frame Survey (LUCAS) 2018 data to generate a detailed LULC map with 19 crop type classes and two broad categories for woodland and shrubland, and grassland. The field data were used in combination with Copernicus Sentinel-2 (S2) satellite data covering Europe. First, spatially and temporally consistent S2 image composites of (1) spectral reflectances, (2) a selection of spectral indices, and (3) several bio-geophysical indicators were created for the year 2018. From the large number of features, the most important were selected for classification using two machine-learning algorithms (support vector machine and random forest). Results indicated that the 19 crop type classes and the two broad categories could be classified with an overall accuracy (OA) of 77.6%, using independent data for validation. Our analysis of three methods to select optimum training data showed that by selecting the most spectrally different pixels for training data, the best OA could be achieved, and this already using only 11% of the total training data. Comparing our results to a similar study using Sentinel-1 (S1) data indicated that S2 can achieve slightly better results, although the spatial coverage was slightly reduced due to gaps in S2 data. Further analysis is ongoing to leverage synergies between optical and microwave data. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)

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17 pages, 16605 KiB

Open AccessArticle

Toward a Simple and Generic Approach for Identifying Multi-Year Cotton Cropping Patterns Using Landsat and Sentinel-2 Time Series

by Qiqi Li, Guilin Liu and Weijia Chen

Remote Sens. 2021, 13(24), 5183; https://doi.org/10.3390/rs13245183 - 20 Dec 2021

Cited by 5 | Viewed by 3289

Abstract

The sustainable development goals of the United Nations, as well as the era of pandemics have introduced serious challenges for agricultural production and management. Precise management of agricultural practices based on satellite-borne remote sensing has been considered an effective means for monitoring cropping [...] Read more.

The sustainable development goals of the United Nations, as well as the era of pandemics have introduced serious challenges for agricultural production and management. Precise management of agricultural practices based on satellite-borne remote sensing has been considered an effective means for monitoring cropping patterns and crop-farming patterns. Therefore, we proposed a simple and generic approach to identify multi-year cotton-cropping patterns based on time series of Landsat and Sentinel-2 images, with few ground samples that covered many years, a simple classification algorithm, and had a high classification accuracy. In this approach, we extended the size of training samples using active learning, and we employed a random forest algorithm to extract multi-year cotton planting patterns based on dense time series of Landsat and Sentinel-2 data from 2014 to 2018. We created annual crop cultivation maps based on training samples with an accuracy greater than 95.69%. The accuracy of multi-year cotton cropping patterns was 96.93%. The proposed approach was effective and robust in identifying multi-year cropping patterns, and it could be applied in other regions. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)

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18 pages, 6857 KiB

Open AccessArticle

Spatiotemporal Changes and Driving Factors of Cultivated Soil Organic Carbon in Northern China’s Typical Agro-Pastoral Ecotone in the Last 30 Years

by Liping Wang, Xiang Wang, Dianyao Wang, Beisong Qi, Shufeng Zheng, Huanjun Liu, Chong Luo, Houxuan Li, Linghua Meng, Xiangtian Meng and Yihao Wang

Remote Sens. 2021, 13(18), 3607; https://doi.org/10.3390/rs13183607 - 10 Sep 2021

Cited by 18 | Viewed by 1968

Abstract

In order to explore the spatiotemporal changes and driving factors of soil organic carbon (SOC) in the agro-pastoral ecotone of northern China, we took Aohan banner, Chifeng City, Inner Mongolia Autonomous Region as the study area, used the random forest (RF) method to [...] Read more.

In order to explore the spatiotemporal changes and driving factors of soil organic carbon (SOC) in the agro-pastoral ecotone of northern China, we took Aohan banner, Chifeng City, Inner Mongolia Autonomous Region as the study area, used the random forest (RF) method to predict the SOC from 1989 to 2018, and the geographic detector method (GDM) was applied to analyze quantitatively the natural and anthropogenic factors that are affecting Aohan banner. The results indicated that: (1) After adding the terrain factors, the R² and residual predictive deviation (RPD) of the RF model increased by 1.178 and 0.39%, with root mean square errors (RMSEs) of 1.42 g/kg and 1.05 g/kg, respectively; (2) The spatial distribution of SOC was higher in the south and lower in the north; the negative growth of SOC accounted for 55.923% of the total area, showing a trend of degradation; (3) Precipitation was the main driving factor of SOC spatial variation in the typical agro-pastoral ecotone of northern China, which was also affected by temperature, elevation, soil type and soil texture (p < 0.01). (4). Anthropogenic factors (carbon input and gross domestic product (GDP)) had a greater impact on SOC than did climate factors (temperature and precipitation), making anthropogenic factors the dominant factors affecting SOC temporal variation (p < 0.01). The results of this work constitute a basis for a regional assessment of the temporal evolution of organic carbon in the soil surface, which is a key tool for monitoring the sustainable development of agropastoral ecotones. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)

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19 pages, 7481 KiB

Open AccessArticle

Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing

by Zhijie Liu, Pengju Guo, Heng Liu, Pan Fan, Pengzong Zeng, Xiangyang Liu, Ce Feng, Wang Wang and Fuzeng Yang

Remote Sens. 2021, 13(16), 3263; https://doi.org/10.3390/rs13163263 - 18 Aug 2021

Cited by 22 | Viewed by 4615

Abstract

The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been [...] Read more.

The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R²) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R² of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)

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16 pages, 9034 KiB

Open AccessArticle

Vine Identification and Characterization in Goblet-Trained Vineyards Using Remotely Sensed Images

by Chantal Hajjar, Ghassan Ghattas, Maya Kharrat Sarkis and Yolla Ghorra Chamoun

Remote Sens. 2021, 13(15), 2992; https://doi.org/10.3390/rs13152992 - 29 Jul 2021

Cited by 1 | Viewed by 1751

Abstract

This paper proposes a novel approach for living and missing vine identification and vine characterization in goblet-trained vine plots using aerial images. Given the periodic structure of goblet vineyards, the RGB color coded parcel image is analyzed using proper processing techniques in order [...] Read more.

This paper proposes a novel approach for living and missing vine identification and vine characterization in goblet-trained vine plots using aerial images. Given the periodic structure of goblet vineyards, the RGB color coded parcel image is analyzed using proper processing techniques in order to determine the locations of living and missing vines. Vine characterization is achieved by implementing the marker-controlled watershed transform where the centers of the living vines serve as object markers. As a result, a precise mortality rate is calculated for each parcel. Moreover, all vines, even the overlapping ones, are fully recognized providing information about their size, shape, and green color intensity. The presented approach is fully automated and yields accuracy values exceeding 95% when the obtained results are assessed with ground-truth data. This unsupervised and automated approach can be applied to any type of plots presenting similar spatial patterns requiring only the image as input. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Agricultural Ecosystems)

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