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Remote Sensing of Soil Moisture for Agricultural Purposes

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

Deadline for manuscript submissions: closed (30 July 2024) | Viewed by 18890

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Dr. Rafał Pudełko

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Guest Editor

Institute of Soil Science and Plant Cultivation (IUNG-PIB), Department of Bioeconomy and Systems Analysis, Czartoryskich 8 Str., 24-100 Pulawy, Poland
Interests: remote sensing; monitoring of land use and land use change; precision agriculture; unmanned aerial vehicles; physical geography and agro-climatology; biomass and renewable energies

Dr. Kamil Szewczak

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Guest Editor

Institute of Agrophysics, Polish Academy of Sciences (Instytut Agrofizyki Polskiej Akademii Nauk), 20-280 Lublin, Poland
Interests: remote sensing; biochar application in agronomy; soil physical properties monitoring; drought monitoring; environmental radioactivity

Special Issue Information

Dear Colleagues,

Water is a key element in agriculture due to its essential role in plant production. The possibility of effective use of water by crops is mainly determined by the retention properties of the soil, thanks to which water can be stored in periods of drought. However, this property depends on many factors, which may additionally be spatially differentiated, even on the scale of small agricultural plots. Information on soil moisture and its variability during the growing season can be used in agriculture in many ways. It is essential for the proper conduct of agro-technical practices on the farm scale, provision of agricultural advisory services in the regions, as well as for the central planning of agricultural policy. Nowadays, modern methods allow for remote assessment of soil moisture and its mapping - in each of the abovementioned spatial scales. Also, forecasted climate changes and already observed water shortages in areas that have, so far, been free from such problems require urgent actions to prevent these adverse phenomena. In this regard, remote sensing is one of the most effective methods of providing data in high spatial resolution, which is highly required in agriculture.

Here, I invite you to publish works that present the use of any non-invasive method (satellite and aerial RS, UAV, field robot, sensors installed on agricultural machines) for direct assessment of soil moisture. Works devoted to the broadly understood modelling and mapping of physical and chemical properties of soil related to its retention properties are also welcome - e.g. mapping of soil mosaic based on the field images, re-scaling of agricultural soil maps based on aerial or satellite images, modelling the coherence of land relief with the variability of soil properties or mapping of soil moisture variation for the purpose of introducing precision farming methods.

In any case, the common denominator of the presented research should be remote sensing of soil moisture and application of this information for agricultural purposes,with respect to the following main topics:

Multisensory approaches to modelling
Temporal approach to soil moisture monitoring
Prediction of soil properties from different platforms
Soil mapping based on the field image of cultivated plants (canopy)
New remote sensing technology and image analysis methods with particular emphasis on machine and deep learning

Dr. Rafał Pudełko
Dr. Kamil Szewczak
Guest Editors

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Keywords

soil moisture
soil properties
active and passive remote sensing
multisensory analysis
soil mapping
soil mosaic detection
machine and deep learning

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Published Papers (6 papers)

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21 pages, 5682 KiB

Open AccessArticle

Retrieving Soil Moisture at the Field Scale from Sentinel-1 Data over a Semi-Arid Mediterranean Agricultural Area

by Giulia Graldi, Dino Zardi and Alfonso Vitti

Remote Sens. 2023, 15(12), 2997; https://doi.org/10.3390/rs15122997 - 8 Jun 2023

Cited by 1 | Viewed by 2981

Abstract

In this work, superficial soil moisture is estimated from SAR data at the field scale on agricultural fields over which the relationship between the co-polarized backscattering coefficient (

γ_{0}^{V V}

) and the measured soil moisture (

S S M_{v}

[...] Read more.

In this work, superficial soil moisture is estimated from SAR data at the field scale on agricultural fields over which the relationship between the co-polarized backscattering coefficient (

γ_{0}^{V V}

) and the measured soil moisture (

S S M_{v}

) is both direct and inverse. An inversion algorithm is adapted to the charateristics of the single field and applied to SAR signal differences. The differences of SAR signal are obtained from a change detection (CD) method applied on the VV band of the Sentinel-1 SAR mission. In the CD method, the variations of the total backscattered signal due to sharp changes in vegetation and soil roughness are excluded from the dataset by using a machine learning algorithm. The retrieval method is applied on a low vegetated agricultural area in Spain, characterized by a semi-arid mediterranean climate and where in situ soil moisture data are available. Good results are obtained not only over fields characterized by direct

γ_{0}^{V V} / S S M_{v}

relationship, reaching values of correlation coefficient and RMSE up to

r = 0.89

and

R M S E = 0.042

^{3}

^{3}

, but also over fields with inverse relationship, obtaining in this case values up to

r = 0.84

R M S E = 0.026

^{3}

^{3}

. Although the inverse relationship between the backscattering coefficient and the measured soil moisture is not yet well understood in the field of soil moisture estimation from radar data, for the present case, checking the nature of this relationship was fundamental in order to accordingly adapt the soil moisture retrieval algorithm to the dataset characteristics. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture for Agricultural Purposes)

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29 pages, 9281 KiB

Open AccessArticle

Evaluating the Hydrus-1D Model Optimized by Remote Sensing Data for Soil Moisture Simulations in the Maize Root Zone

by Jingxin Yu, Yong Wu, Linlin Xu, Junhuan Peng, Guangfeng Chen, Xin Shen, Renping Lan, Chunjiang Zhao and Lili Zhangzhong

Remote Sens. 2022, 14(23), 6079; https://doi.org/10.3390/rs14236079 - 30 Nov 2022

Cited by 8 | Viewed by 3613

Abstract

The Hydrus-1D model is widely used for soil water content (SWC) simulations, wherein the exact configuration of soil hydraulic parameters is key to accuracy. To assess the feasibility of using “low-cost” multi-source remote sensing data to optimize the parameters of the Hydrus-1D model, five types of soil hydrodynamic parameter acquisition methods were designed for comparative evaluation, including the use of default parameters for soil texture types (DSHP), predictions from three and five soil mechanical composition parameters (NNP3/NNP5), inverse solutions from measured historical data (ISHD), and innovative introduction of historical remote sensing data (ERA-5 land reanalysis information and MODIS LAI products) instead of ground measured data for the inverse solution (ISRS). Two spring maize crops were planted in Beijing, China, in 2021 and 2022. Meteorological, soil, and crop data were collected as real measurements of the true values during the growth period. The boundary flux characteristics of the model simulation results were analyzed. The accuracy differences in the five approaches were compared from three perspectives: overall root zone, growth stage, and soil depth. The results showed that (1) evapotranspiration was the main pathway for soil water depletion in the root zone of maize; the actual total evapotranspiration accounted for 68.26 and 69.43% of the total precipitation in 2012 and 2022, respectively. (2) The accuracy of the SWC simulations in the root zone was acceptable for different approaches in the following order: NNP5 (root mean squared error (RMSE) = 5.47%) > ISRS (RMSE = 5.48%) > NNP3 (RMSE = 5.66%) > ISHD (RMSE = 5.68%) > DSHP (RMSE = 6.57%). The ISRS approach based on remote sensing data almost achieved the best performance while effectively reducing the workload and cost. (3) The accuracy of the SWC simulation at different growth stages was ranked as follows: seedling stage (mean absolute error (MAE) = 3.29%) > tassel stage (MAE = 4.68%) > anthesis maturity stage (MAE = 5.52%). (4) All approaches’ simulation errors exhibited a decreasing trend with increasing soil depth. The ISHD approach, based on the measured data, achieved the best performance at a depth of 60 cm (MAE = 2.8%). The Hydrus-1D model optimized using multi-source remote sensing data can effectively simulate SWC in the maize root zone with low working cost, which is significant for applications in areas where it is difficult to obtain field soil hydrodynamic property parameters to simulate SWC at a global scale. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture for Agricultural Purposes)

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20 pages, 17719 KiB

Open AccessArticle

Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events

by Xingwang Fan, Yanyu Lu, Yongwei Liu, Tingting Li, Shangpei Xun and Xiaosong Zhao

Remote Sens. 2022, 14(14), 3339; https://doi.org/10.3390/rs14143339 - 11 Jul 2022

Cited by 10 | Viewed by 2557

Abstract

Remote sensing and land surface models promote the understanding of soil moisture dynamics by means of multiple products. These products differ in data sources, algorithms, model structures and forcing datasets, complicating the selection of optimal products, especially in regions with complex land covers. This study compared different products, algorithms and flagging strategies based on in situ observations in Anhui province, China, an intensive agricultural region with diverse landscapes. In general, models outperform remote sensing in terms of valid data coverage, metrics against observations or based on triple collocation analysis, and responsiveness to precipitation. Remote sensing performs poorly in hilly and densely vegetated areas and areas with developed water systems, where the low data volume and poor performance of satellite products (e.g., Soil Moisture Active Passive, SMAP) might constrain the accuracy of data assimilation (e.g., SMAP L4) and downstream products (e.g., Cyclone Global Navigation Satellite System, CYGNSS). Remote sensing has the potential to detect irrigation signals depending on algorithms and products. The single-channel algorithm (SCA) shows a better ability to detect irrigation signals than the Land Parameter Retrieval Model (LPRM). SMAP SCA-H and SCA-V products are the most sensitive to irrigation, whereas the LPRM-based Advanced Microwave Scanning Radiometer 2 (AMSR2) and European Space Agency (ESA) Climate Change Initiative (CCI) passive products cannot reflect irrigation signals. The results offer insight into optimal product selection and algorithm improvement. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture for Agricultural Purposes)

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27 pages, 7266 KiB

Open AccessArticle

Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China

by Adeel Ahmad Nadeem, Yuanyuan Zha, Liangsheng Shi, Gulin Ran, Shoaib Ali, Zahid Jahangir, Muhammad Mannan Afzal and Muhammad Awais

Remote Sens. 2022, 14(4), 982; https://doi.org/10.3390/rs14040982 - 17 Feb 2022

Cited by 18 | Viewed by 3247

Abstract

The Soil Moisture Active Passive (SMAP) mission with high-precision soil moisture (SM) retrieval products provides global daily composites of SM at 3, 9, and 36 km earth grids measured by L-band active and passive microwave sensors. The capability of passive microwave remote sensing has been recognized for the estimation of SM variations. The purpose of this work was to establish an interaction between the highly variable SM spatial distribution on the ground and the SMAP’s coarse resolution radiometer-based SM retrievals. In this work, SMAP Level 3 (L3) and Level 4 (L4) SM products are validated with in situ datasets observed from the different locations of the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin over the period of January 2018 to December 2019. The values of the unbiased root mean square error (ubRMSE) for L3 (SPL3SMP_E) SM retrievals are close to the standard SMAP mission SM accuracy requirement of 0.04

m^{3} / m^{3}

at the 9-km scale, with an averaged ubRMSE value of 0.041

m^{3} / m^{3}

(0.050

m^{3} / m^{3}

) for descending (ascending) SM with the correlation (R) values of 0.62 (0.42) against the sparse network sites. The L4 (SPL4SMGP) Surface and Root-zone SM (RZSM) estimates show less error (ubRMSE < 0.04) and high correlation (R > 0.60) values, and are consistent with the previous SMAP-based SM estimations. The SMAP L4 SM products (SPL4SMGP) performed well compared to the L3 SM retrieval products (SPL3SMP_E). In vegetated land, the variability and compatibility of the SMAP SM estimates with the evaluation metrics for both products (L3 and L4) showed a good performance in the grassland, then in the farmland, and worst in the woodlands. Finally, SMAP algorithm parameters sensitivity analysis of the satellite products was conducted to produce time-series and highly precise SM datasets in China. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture for Agricultural Purposes)

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23 pages, 4670 KiB

Open AccessArticle

Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models

by Qiang Yin, Junlang Li, Fei Ma, Deliang Xiang and Fan Zhang

Remote Sens. 2021, 13(22), 4503; https://doi.org/10.3390/rs13224503 - 9 Nov 2021

Cited by 3 | Viewed by 2107

Abstract

The polarimetric synthetic aperture radar (PolSAR) can be used to obtain soil moisture by inverting scattering models at high resolution. The convolutional neural network (CNN) has been recently introduced to estimate soil roughness for PolSAR data, which need to be driven by a large amount of data. In this paper, a dual-channel CNN based on polarimetric models is proposed for soil moisture inversion, and it aims to further expand the applicable range of roughness in the X-Bragg model by integration with the integral equation model (IEM). Meanwhile, it fully utilizes the spatial information of PolSAR images to relax the number of required training samples when real data on the surface are difficult to obtain. Besides, we designed a framework based on this network. Coarse-grained inversion and fine-grained inversion of soil moisture are carried out through the qualitative classification network and the quantitative regression network, respectively. Experiments on simulated and airborne E-SAR data show that the proposed network can accurately fit the nonlinear relationship between polarization parameters and soil moisture, so as to improve the inversion accuracy with a small number of samples. In our experiments, the average inversion accuracy reached 95.39%, and the root mean square error (RMSE) of the regression network was 0.98%. This method can be applied to a wide range of soil moisture monitoring applications. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture for Agricultural Purposes)

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18 pages, 6941 KiB

Open AccessTechnical Note

Study on Rapid Inversion of Soil Water Content from Ground-Penetrating Radar Data Based on Deep Learning

by Zhilian Li, Zhaofa Zeng, Hongqiang Xiong, Qi Lu, Baizhou An, Jiahe Yan, Risheng Li, Longfei Xia, Haoyu Wang and Kexin Liu

Remote Sens. 2023, 15(7), 1906; https://doi.org/10.3390/rs15071906 - 2 Apr 2023

Cited by 8 | Viewed by 2416

Abstract

Ground-penetrating radar (GPR) is an efficient and nondestructive geophysical method with great potential for detecting soil water content at the farmland scale. However, a key challenge in soil detection is obtaining soil water content rapidly and in real-time. In recent years, deep learning methods have become more widespread in the earth sciences, making it possible to use them for soil water content inversion from GPR data. In this paper, we propose a neural network framework GPRSW based on deep learning of GPR data. GPRSW is an end-to-end network that directly inverts volumetric soil water content (VSWC) through single-channel GPR data. Synthetic experiments show that GPRSW accurately identifies different VSWC boundaries in the model in time depth. The predicted VSWC and model fit well within 40 ns, with a maximum error after 40 ns of less than 0.10 cm³ × cm⁻³. To validate our method, we conducted GPR measurements at the experimental field of the Academy of Agricultural Sciences in Gongzhuling City, Jilin Province and applied GPRSW to VSWC measurements. The results show that predicted values of GPRSW match with field soil samples and are consistent with the overall trend of the TDR soil probe samples, with a maximum difference not exceeding 0.03 cm³ × cm⁻³. Therefore, our study shows that GPRSW has the potential to be applied to obtain soil water content from GPR data on farmland. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Moisture for Agricultural Purposes)

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