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Monitoring Water, Vegetation, and Soil Condition in Farmland Ecosystems: Integration of Multi-Source Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 6895

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Special Issue Editors

Prof. Dr. Ningbo Cui

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Guest Editor

College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
Interests: precision irrigation and deficit irrigation; agricultural water resources management; agrometeorology and evapotranspiration; drip fertigation regulation; modelling of crop growth
Special Issues, Collections and Topics in MDPI journals

Dr. Taifeng Dong

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Guest Editor

Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
Interests: remote sensing application in agriculture; bio-geophysical properties estimation; crop phenology; data assimilation
Special Issues, Collections and Topics in MDPI journals

Dr. Cong Wang

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Guest Editor

Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: agriculture; quantitative remote sensing; chlorophyll fluorescence; phenology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yulei Xie

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Guest Editor

Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
Interests: water resources system management; water system nexus simulation and planning; uncertainty optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The accurate and timely monitoring of water, vegetation, and soil variables in farmland ecosystems plays an irreplaceable role in optimizing farmland management for maximizing crop productivity. The recent and rapid development of different sensors (e.g., optical, SAR, and LiDAR) with different temporal and spatial resolutions has greatly promoted the potential of near real-time crop monitoring from space, which is demanded in precision agriculture. Multi-source remote sensing (RS) can provide near real-time and accurate spatial information on farmland surfaces in large areas, such as crop types, crop water deficits, leaf area indexes (LAIs), leaf chlorophyll contents, and soil moistures. The incorporation of dynamic crop traits from multi-source RS data into crop growth models to minimize discrete performance gaps and spatiotemporal gaps has been recently highlighted. However, differences in the association between different RS platforms and the dynamic inconsistencies in the correlation between crop varieties exist. New approaches for effectively fusing multi-source RS data to improve crop growth monitoring are required. Developing advanced approaches for quantifying the soil‒water‒plant relationship based on multi-source RS is urgent as well. This Special Issue will focus on the advancement of multi-source remote sensing in monitoring water, vegetation, and soil conditions in farmland ecosystems. We welcome novel research, reviews, and opinion pieces covering all the related topics, which include but are not limited to:

Retrieving soil moisture and nutrients in farmlands;
Cultivation structure extraction and area estimation;
Crop trait (crop types, LAIs, crop water deficits, and leaf chlorophyll contents) inversion;
Assimilation of multi-source RS into crop growth models;
Optimization of an irrigation system and the evaluation of water productivity;
Construction of a regional intelligent irrigation decision-making system;
Further understanding of the soil‒water‒crop relationship.

You may choose our Joint Special Issue in Earth.

Prof. Dr. Ningbo Cui
Dr. Taifeng Dong
Dr. Cong Wang
Prof. Dr. Yulei Xie
Guest Editors

Manuscript Submission Information

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Keywords

soil variables
crop traits
multi-source RS
crop growth model
irrigation system

Published Papers (7 papers)

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Research

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21 pages, 27025 KiB

Open AccessArticle

Raster Scale Farmland Productivity Assessment with Multi-Source Data Fusion—A Case of Typical Black Soil Region in Northeast China

by Yuwen Liu, Chengyuan Wang, Enheng Wang, Xuegang Mao, Yuan Liu and Zhibo Hu

Remote Sens. 2024, 16(8), 1435; https://doi.org/10.3390/rs16081435 - 18 Apr 2024

Viewed by 257

Abstract

Degradation of black soil areas is a serious threat to national food security and ecological safety; nevertheless, the current lack of information on the location, size, and condition of black soil farmland productivity is a major obstacle to the development of strategies for the sustainable utilization of black soil resources. We synthesized remote sensing data and geospatial thematic data to construct a farmland productivity assessment indicator system to assess the productivity of black soil cropland at the regional scale. Furthermore, we conducted research on the spatial differentiation patterns and a spatial autocorrelation analysis of the assessment results. We found that farmland productivity within this region exhibited a decline pattern from south to north, with superior productivity in the east as opposed to the west, and the distribution follows a “spindle-shaped” pattern. Notably, the Songnen and Sanjiang typical black soil subregions centrally hosted about 46.17% of high-quality farmland and 53.51% of medium-quality farmland, while the Mondong typical black soil subregion in the west predominantly consisted of relatively low-quality farmland productivity. Additionally, farmland productivity displayed a significant positive spatial correlation and spatial clustering, with more pronounced fluctuations in the northeast–southwest direction. The developed indicator system for farmland productivity can illustrate the spatial differentiation and thereby offer a valuable reference for the sustainable management of farmland resources. Full article

(This article belongs to the Special Issue Monitoring Water, Vegetation, and Soil Condition in Farmland Ecosystems: Integration of Multi-Source Remote Sensing)

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20 pages, 4454 KiB

Open AccessArticle

Machine Learning-Based Estimation of Daily Cropland Evapotranspiration in Diverse Climate Zones

by Changmin Du, Shouzheng Jiang, Chuqiang Chen, Qianyue Guo, Qingyan He and Cun Zhan

Remote Sens. 2024, 16(5), 730; https://doi.org/10.3390/rs16050730 - 20 Feb 2024

Viewed by 935

Abstract

The accurate prediction of cropland evapotranspiration (ET) is of utmost importance for effective irrigation and optimal water resource management. To evaluate the feasibility and accuracy of ET estimation in various climatic conditions using machine learning models, three-, six-, and nine-factor combinations (V3, V6, and V9) were examined based on the data obtained from global cropland eddy flux sites and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. Four machine learning models, random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and backpropagation neural network (BP), were used for this purpose. The input factors included daily mean air temperature (T_a), net radiation (R_n), soil heat flux (G), evaporative fraction (EF), leaf area index (LAI), photosynthetic photon flux density (PPFD), vapor pressure deficit (VPD), wind speed (U), and atmospheric pressure (P). The four machine learning models exhibited significant simulation accuracy across various climate zones, reflected by their global performance indicator (GPI) values ranging from −3.504 to 0.670 for RF, −3.522 to 1.616 for SVM, −3.704 to 0.972 for XGB, and −3.654 to 1.831 for BP. The choice of suitable models and the different input factors varied across different climatic regions. Specifically, in the temperate–continental zone (TCCZ), subtropical–Mediterranean zone (SMCZ), and temperate zone (TCZ), the models of BP_C-V9, SVM_S-V6, and SVM_T-V6 demonstrated the highest simulation accuracy, with average RMSE values of 0.259, 0.373, and 0.333 mm d⁻¹, average MAE values of 0.177, 0.263, and 0.248 mm d⁻¹, average R² values of 0.949, 0.819, and 0.917, and average NSE values of 0.926, 0.778, and 0.899, respectively. In climate zones with a lower average LAI (TCCZ), there was a strong correlation between LAI and ET, making LAI more crucial for ET predictions. Conversely, in climate zones with a higher average LAI (TCZ, SMCZ), the significance of the LAI for ET prediction was reduced. This study recognizes the impact of climate zones on ET simulations and highlights the necessity for region-specific considerations when selecting machine learning models and input factor combinations. Full article

(This article belongs to the Special Issue Monitoring Water, Vegetation, and Soil Condition in Farmland Ecosystems: Integration of Multi-Source Remote Sensing)

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25 pages, 11420 KiB

Open AccessArticle

Spatiotemporal Characteristics of Actual Evapotranspiration Changes and Their Climatic Causes in China

by Qin Dai, Hong Chen, Chenfeng Cui, Jie Li, Jun Sun, Yuxin Ma, Xuelian Peng, Yakun Wang and Xiaotao Hu

Remote Sens. 2024, 16(1), 8; https://doi.org/10.3390/rs16010008 - 19 Dec 2023

Viewed by 633

Abstract

As the main expenditure item in water balance, evapotranspiration has an important impact on the surface ecosystem. Assessing the impact of changes in meteorological elements on evapotranspiration is essential to identify the spatiotemporal heterogeneity of hydrographic responses to climate changes. Based on the actual evapotranspiration (ET_a) product (GPR-ET) generated by Gaussian process regression (GPR), as well as temperature and precipitation datasets, our study employed various statistical analysis methods, including geographic detector, the center of gravity migration model, spatial variation coefficients, and partial differential models, to investigate the spatiotemporal variation in ET_a in China from 2000 to 2018. The analysis covered future trends in ET_a changes and the contribution of meteorological factors. Our results showed that the ET_a in northwest China had stronger spatial heterogeneity and the mean value was generally lower than that in the southeast. But the center of gravity of ET_a was shifting towards the northwest. In most areas, the future trend was expected to be inconsistent with the current stage. ET_a in the regions of north and west was mainly driven by precipitation, while its increase in southeast China was largely attributed to temperature. In addition to spatial variations, the joint enhancement effect of temperature and precipitation on ET_a exists. According to the contribution analysis, precipitation contributed more to the change in ET_a than temperature. These findings have enhanced our comprehension of the contribution of climate variability to ET_a changes, providing scientific proof for the optimization apportion of future water resources. Full article

(This article belongs to the Special Issue Monitoring Water, Vegetation, and Soil Condition in Farmland Ecosystems: Integration of Multi-Source Remote Sensing)

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17 pages, 6188 KiB

Open AccessArticle

Evaluation of Ecosystem Water Use Efficiency Based on Coupled and Uncoupled Remote Sensing Products for Maize and Soybean

by Lingxiao Huang, Meng Liu and Na Yao

Remote Sens. 2023, 15(20), 4922; https://doi.org/10.3390/rs15204922 - 12 Oct 2023

Cited by 1 | Viewed by 791

Abstract

Accurate quantification of ecosystem water use efficiency (eWUE) over agroecosystems is crucial for managing water resources and assuring food security. Currently, the uncoupled Moderate Resolution Imaging Spectroradiometer (MODIS) product is the most widely applied dataset for simulating local, regional, and global eWUE across different plant functional types. However, it has been rarely investigated as to whether the coupled product can outperform the uncoupled product in eWUE estimations for specific C4 and C3 crop species. Here, the eWUE as well as gross primary production (GPP) and evapotranspiration (ET) from the uncoupled MODIS product and the coupled Penman–Monteith–Leuning version 2 (PMLv2) product were evaluated against the in-situ observations on eight-day and annual scales (containing 1902 eight-day and 61 annual samples) for C4 maize and C3 soybean at the five cropland sites from the FLUXNET2015 and AmeriFlux datasets. Our results show the following: (1) For GPP estimates, the PMLv2 product showed paramount improvements for C4 maize and slight improvements for C3 soybean, relative to the MODIS product. (2) For ET estimates, both products performed similarly for both crop species. (3) For eWUE estimates, the coupled PMLv2 product achieved higher-accuracy eWUE estimates than the uncoupled MODIS product at both eight-day and annual scales. Taking the result at an eight-day scale for example, compared to the MODIS product, the PMLv2 product could reduce the root mean square error (RMSE) from 2.14 g C Kg⁻¹ H₂O to 1.36 g C Kg⁻¹ H₂O and increase the coefficient of determination (R²) from 0.06 to 0.52 for C4 maize, as well as reduce the RMSE from 1.33 g C Kg⁻¹ H₂O to 0.89 g C Kg⁻¹ H₂O and increase the R² from 0.05 to 0.49 for C3 soybean. (4) Despite the outperformance of the PMLv2 product in eWUE estimations, both two products failed to differentiate C4 and C3 crop species in their model calibration and validation processes, leading to a certain degree of uncertainties in eWUE estimates. Our study not only provides an important reference for applying remote sensing products to derive reliable eWUE estimates over cropland but also indicates the future modification of the current remote sensing models for C4 and C3 crop species. Full article

(This article belongs to the Special Issue Monitoring Water, Vegetation, and Soil Condition in Farmland Ecosystems: Integration of Multi-Source Remote Sensing)

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22 pages, 1757 KiB

Open AccessArticle

Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China

by Quanshan Liu, Zongjun Wu, Ningbo Cui, Xiuliang Jin, Shidan Zhu, Shouzheng Jiang, Lu Zhao and Daozhi Gong

Remote Sens. 2023, 15(17), 4214; https://doi.org/10.3390/rs15174214 - 27 Aug 2023

Viewed by 1667

Abstract

Soil moisture is a key parameter for the circulation of water and energy exchange between surface and the atmosphere, playing an important role in hydrology, agriculture, and meteorology. Traditional methods for monitoring soil moisture suffer from spatial discontinuity, time-consuming processes, and high costs. Remote sensing technology enables the non-destructive and efficient retrieval of land information, allowing rapid soil moisture monitoring to schedule crop irrigation and evaluate the irrigation efficiency. Satellite data with different resolutions provide different observation scales. Evaluating the accuracy of estimating soil moisture based on open and free satellite data, as well as exploring the comprehensiveness and adaptability of different satellites for soil moisture temporal and spatial observations, are important research contents of current soil moisture monitoring. The study utilized three types of satellite data, namely GF-1, Landsat-8, and GF-4, with respective temporal and spatial resolutions of 16 m (every 4 days), 30 m (every 16 days), and 50 m (daily). The gray relational analysis (GRA) was employed to identify vegetation indices that selected sensitivity to soil moisture at varying depths (3 cm, 10 cm, and 20 cm). Then, this study employed random forest (RF), Extra Tree (ETr), and linear regression (LR) algorithms to estimate soil moisture at different depths with optical satellite data sources. The results showed that the accuracy of soil moisture estimation was different at different growth stages. The model accuracy exhibited an upward trend during the middle and late growth stages, coinciding with higher vegetation coverage; however, it demonstrated a decline in accuracy during the early and late growth stages due to either the absence or limited presence of vegetation. Among the three satellite images, the vegetation indices derived from GF-1 exhibited were more sensitive to vegetation characteristics and demonstrated superior soil moisture estimation accuracy (with R² ranging 0.129–0.928, RMSE ranging 0.017–0.078), followed by Landsat-8 (with R² ranging 0.117–0.862, RMSE ranging 0.017–0.088). The soil moisture estimation accuracy of GF-4 was the worst (with R² ranging 0.070–0.921, RMSE ranging 0.020–0.140). Thus, GF-1 is suitable for vegetated areas. In addition, the ETr model outperformed the other models in both accuracy and stability (ETr model: R² ranging from 0.117 to 0.928, RMSE ranging from 0.021 to 0.091; RF model: R² ranging from 0.225 to 0.926, RMSE ranging from 0.019 to 0.085; LR model: R² ranging from 0.048 to 0.733, RMSE ranging from 0.030 to 0.144). Utilizing GF-1 is recommended to construct the ETr model for assessing soil moisture variations in the farming land of northern China. Therefore, in cases where there are limited ground sample data, it is advisable to utilize high-spatiotemporal-resolution remote sensing data, along with machine learning algorithms such as ETr and RF, which are suitable for small samples, for soil moisture estimation. Full article

(This article belongs to the Special Issue Monitoring Water, Vegetation, and Soil Condition in Farmland Ecosystems: Integration of Multi-Source Remote Sensing)

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24 pages, 12920 KiB

Open AccessArticle

Response of Evapotranspiration (ET) to Climate Factors and Crop Planting Structures in the Shiyang River Basin, Northwestern China

by Xueyi Yang, Xiaojing Shi, Yaling Zhang, Fei Tian and Samuel Ortega-Farias

Remote Sens. 2023, 15(16), 3923; https://doi.org/10.3390/rs15163923 - 08 Aug 2023

Viewed by 892

Abstract

Evapotranspiration (ET) is an essential part of energy flow between the surface of the earth and the atmosphere, simultaneously involving the water, carbon, and energy cycles. It is mainly determined by climate, land use, and land cover changes. Additionally, there is still a need for quantitative characterization of the impacts of climate factors and human activities on ET and regional water resource efficiency in arid and semiarid regions. Based on Landsat-8 remote sensing imagery and land use data, the crop planting structures in the Liangzhou District of the middle reaches of the Shiyang River Basin were identified using a multiband and multi-temporal approach in this study. Subsequently, the ET of major cash crops was inverted using the three-temperature model. This research quantitatively describes the responses of wheat and corn to the climate and human activities over a two-year period. Furthermore, the impact of crop planting structures and climatic factors on ET was elucidated. The results indicate that a combination of multi-temporal green and shortwave infrared 1 bands is the optimal spectral combination to extract the planting structures. Compared to 2019, the wheat area decreased by 23.27% in 2020, while the corn area increased by 5.96%. Both crops exhibited significant spatial heterogeneity in ET during the growing season. The typical daily range of ET for wheat was 0.4–7.2 mm/day, and for corn, it was 1.5–4.0 mm/day. Among the climatic factors, temperature showed the highest correlation with ET (R = 0.80, p ≤ 0.05). Our research findings provide valuable insights for the fine identification of crop planting structures and a better understanding of the response of ET to climatic factors and planting structures. Full article

(This article belongs to the Special Issue Monitoring Water, Vegetation, and Soil Condition in Farmland Ecosystems: Integration of Multi-Source Remote Sensing)

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14 pages, 4736 KiB

Open AccessTechnical Note

Mapping Soil Organic Matter Using Different Modeling Techniques in the Dryland Agroecosystem of Huang-Huai-Hai Plain, Eastern China

by Hua Jin, Xuefeng Xie, Lijie Pu, Zhenyi Jia and Fei Xu

Remote Sens. 2023, 15(20), 4945; https://doi.org/10.3390/rs15204945 - 13 Oct 2023

Viewed by 796

Abstract

Accurately mapping the spatial distribution and variation of soil organic matter (SOM) is of great significance for guiding regional soil management. However, the applicability and prediction performance of machine learning techniques in dryland agroecosystems still needs to be further studied. In this study, we collected a total of 733 topsoil samples from the farmland in Xiao County, Anhui Province, which is a typical dryland agroecosystem in the Huang-Huai-Hai Plain. Then, the environmental covariates were selected, and the ordinary kriging (OK), multiple linear stepwise regression (MLR), regression kriging (RK), radial basis function neural network (RBFNN), and random forest (RF) models were conducted to map the SOM content, and the optimal model was ascertained. The results demonstrated that the alkali-hydrolyzable nitrogen (26.11%), available potassium (17.73%), mean annual precipitation (13.26%), and pH (11.80%) were the main controlling factors affecting the spatial distribution of SOM in the study area. Meanwhile, the introduction of environmental covariates can effectively improve the SOM prediction accuracy, and the RF model (R² = 0.48, MAE = 2.38 g kg⁻¹, MRE = 12.99%, RMSE = 3.14 g kg⁻¹) has a better performance than the RFBNN, MLR, RK, and OK methods. Although there are local differences in the spatial distribution of SOM predicted by the five methods, the overall spatial distribution of SOM was characterized by the low concentration area (13.44–20.00 g kg⁻¹) distributed in the central and northwest of study area, and the high concentration area (24.00–28.95 g kg⁻¹) distributed in the southeast. Overall, our study demonstrated that machine learning-based models could accurately predict the SOM content in dryland agroecosystem, and the produced maps function as baseline maps for sustainable agricultural management. Full article

(This article belongs to the Special Issue Monitoring Water, Vegetation, and Soil Condition in Farmland Ecosystems: Integration of Multi-Source Remote Sensing)

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