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Integrating Remote Sensing in Land Surface Monitoring and Agricultural Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 9046

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Special Issue Editors

Dr. Long Zhao

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Guest Editor

School of Geographical Sciences, Southwest University, Chongqing, China
Interests: soil moisture; land data assimilation; hydrometeorology; remote sensing

Prof. Dr. Maruthi Sridhar Balaji Bhaskar

E-Mail Website1 Website2
Guest Editor

Department of Earth and Environment, Florida International University, Miami, FL 33199, USA
Interests: remote sensing/GIS; soil and water quality; evapotranspiration; agricultural sustainability; land use and land cover change analysis; soil science; agriculture; forestry
Special Issues, Collections and Topics in MDPI journals

Dr. Yang Lu

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Guest Editor

School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: environmental data assimilation; spatial downscaling; crop modelling; drought monitoring

Special Issue Information

Dear Colleagues,

Remote sensing (RS) and Earth Observation (EO) information is central for environmental (e.g., soil and vegetation) monitoring at depth and in real time. The complexity of spatial data and modelling methods in land surface science imposes the need for combined integrated approaches of robust methods, leading to more accurate and reliable outcomes toward sustainable soil management and for hydrometeorological predictions.

The aim of this Special Issue is to publish original contributions or review articles that evaluate the integration of remote sensing, GIS, and data assimilation techniques in land surface monitoring and agricultural practice. The complexity of spatial data and modelling methods in soil science imposes the need for combined integrated approaches of robust methods, leading to more accurate and reliable outcomes toward sustainable soil management and for hydrometeorological predictions. More specifically, we are interested in studies that investigate the impact of widely applied geographical approaches and/or data assimilation methods in everyday soil research and activities. This Special Issue addresses many aspects, including the soil mapping and spatial modelling of land surface characteristics, precision agriculture, geostatistics, machine learning, and the development of software tools for data collection and processing.

Contributions can include, but are not limited to, the following topics:

Mapping and spatial modelling of soil properties using GIS and remote sensing;
New GIS and remote sensing approaches in agricultural applications that make use of trending techniques such as machine and deep learning algorithms;
Advances in remote sensing techniques to provide (time series of) spatially distributed soil moisture data;
Applications of remotely sensed soil moisture data, including data assimilation and disaster assessment;
Approaches for the harmonised processing of data coming from different sensors to construct longer, more coherent soil moisture records;
Studies using data assimilation, e.g., into hydrological models, plant growth models, or discussing concepts;
Retrieval algorithms, in particular using multi-wavelength, active, and passive data, both based on physical models and data-driven methods;
Contributions combining multi-sensor remote sensing observations, in situ measurements, and geographical data from multiple thematic scales to quantify spatial and temporal change patterns are also among our priorities.

Dr. Long Zhao
Prof. Dr. Maruthi Sridhar Balaji Bhaskar
Dr. Yang Lu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

soil mapping
soil moisture
multi-spectral imagery
data fusion and assimilations
crop modelling
yield mapping

Published Papers (7 papers)

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Research

0 pages, 4524 KiB

Open AccessArticle

Changes in Qinghai Lake Area and Their Interactions with Climatic Factors

by Xiaolu Ling, Zeyu Tang, Jian Gao, Chenggang Li and Wenhao Liu

Remote Sens. 2024, 16(1), 129; https://doi.org/10.3390/rs16010129 - 28 Dec 2023

Viewed by 562

Abstract

Lakes play a crucial role in the global water cycle and significantly contribute to enhancing regional ecological environments and simulating economic growth. In this study, based on the data from the Landsat TM 4-5, Landsat 7 ETM SLC-off, and Landsat 8-9 OLI/TIRS C2 L2 satellites, the surface area of Qinghai Lake is obtained by using the Normalized Difference Water Index (NDWI) method. Additionally, leveraging the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation land surface reanalysis dataset (ERA5-Land), we analyzed the interplay between lake area and related climate factors by using the Noise Assisted–Multivariate Empirical Mode Decomposition (NA-MEMD) and wavelet coherence analysis method. The surface area of Qinghai Lake showed an overall expansion trend from 1986 to 2022, with an expansion rate of 2.89 km²/a. Precipitation, temperature, and evapotranspiration (ET) also showed an increasing trend, with the largest increasing trend in autumn, summer, and summer, respectively. The area of Qinghai Lake did not demonstrate distinct periodic patterns from 1986 to 2022, in contrast to the marked 8–16 month oscillations observed in precipitation, temperature, and ET. In the phase of lake area expansion between 2008 and 2016, changes in the lake’s surface area were observed to trail behind variations in precipitation and temperature by approximately three months. Furthermore, the shift in ET was found to lag behind alterations in the lake area, displaying a delay of 3–6 months. Full article

(This article belongs to the Special Issue Integrating Remote Sensing in Land Surface Monitoring and Agricultural Applications)

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20 pages, 13154 KiB

Open AccessArticle

Surface Soil Moisture Determination of Irrigated and Drained Agricultural Lands with the OPTRAM Method and Sentinel-2 Observations

by Tomasz Stańczyk, Wiesława Kasperska-Wołowicz, Jan Szatyłowicz, Tomasz Gnatowski and Ewa Papierowska

Remote Sens. 2023, 15(23), 5576; https://doi.org/10.3390/rs15235576 - 30 Nov 2023

Viewed by 878

Abstract

Surface soil moisture (SSM) is one of the factors affecting plant growth. Methods involving direct soil moisture measurement in the field or requiring laboratory tests are commonly used. These methods, however, are laborious and time-consuming and often give only point-by-point results. In contrast, SSM can vary across a field due to uneven precipitation, soil variability, etc. An alternative is using satellite data, for example, optical data from Sentinel-2 (S2). The main objective of this study was to assess the accuracy of SSM determination based on S2 data versus standard measurement techniques in three different agricultural areas (with irrigation and drainage systems). In the field, we measured SSM manually using non-destructive techniques. Based on S2 data, we estimated SSM using the optical trapezoid model (OPTRAM) and calculated eighteen vegetation indices. Using the OPTRAM model gave a high SSM estimating accuracy (R² = 0.67, RMSE = 0.06). The use of soil porosity in the OPTRAM model significantly improved the results. Among the vegetation indices, at the NDVI ≤ 0.2, the highest value of R² was obtained for the STR to OPTRAM index, while at the NDVI > 0.2, the shadow index had the highest R² comparable with OPTRAM. Full article

(This article belongs to the Special Issue Integrating Remote Sensing in Land Surface Monitoring and Agricultural Applications)

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36 pages, 7613 KiB

Open AccessArticle

Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model

by Mohamad Hamze, Bruno Cheviron, Nicolas Baghdadi, Dominique Courault and Mehrez Zribi

Remote Sens. 2023, 15(16), 4081; https://doi.org/10.3390/rs15164081 - 19 Aug 2023

Cited by 1 | Viewed by 1335

Abstract

This study aimed to develop an approach using Sentinel-1 synthetic aperture radar (SAR) data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in the Occitanie region, Southern France. The surface soil moisture (SSM) derived from SAR data was analyzed for changes indicating irrigation events at the plot scale in four reference plots located in Montpellier (P1) and Tarbes (P2, P3, and P4). As rain most likely covers several square kilometers, while irrigation is decided at the plot scale, a difference between SSM signals at the grid scale (10 km × 10 km) and plot scale is a clear indication of a recent irrigation event. Its date and amount are then sought by forcing irrigation dates and amounts in Optirrig, selecting the most relevant (date, amount) combination from an appropriate criterion. As the observed SSM values hold for a depth of a few centimeters, while the modeled SSM values hold for exactly 10 cm, the best irrigation combination is the one that gives similar relative changes in SSM values rather than similar SSM values. The irrigation dates were detected with an overall accuracy (recall) of 86.2% and a precision of 85.7%, and thus, with relatively low numbers of missed or false irrigation detections, respectively. The performance of the method in detecting seasonal irrigation amounts varied with climatic conditions. For the P1 plot in the semi-arid climate of Montpellier, the mean absolute error percentage (MAE%) was 16.4%, showing a higher efficiency when compared with the humid climate of Tarbes (P2, P3, and P4 plots), where a higher MAE% of 50% was recorded, indicating a larger discrepancy between the detected and actual irrigation amounts. The limitations of the proposed method can be attributed to the characteristics of the Sentinel-1 constellation, including its 6-day revisit time and signal penetration challenges in dense maize cover, as well as the mismatch between the parameterization of Optirrig for SSM simulations and the actual irrigation practices followed by farmers. Despite these weaknesses, the results demonstrated the relevance of combining Optirrig and S1 SAR-derived SSM data for field-scale detection of irrigation dates and, potentially, irrigation amounts. Full article

(This article belongs to the Special Issue Integrating Remote Sensing in Land Surface Monitoring and Agricultural Applications)

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20 pages, 8204 KiB

Open AccessArticle

Uncertainty Quantification of Satellite Soil Moisture Retrieved Precipitation in the Central Tibetan Plateau

by Ke Zhang, Long Zhao, Kun Yang, Lisheng Song, Xiang Ni, Xujun Han, Mingguo Ma and Lei Fan

Remote Sens. 2023, 15(10), 2600; https://doi.org/10.3390/rs15102600 - 16 May 2023

Cited by 1 | Viewed by 1369

Abstract

SM2RAIN is a well-established methodology for estimating precipitation from satellite or observed soil moisture and it has been applied as a complementary approach to conventional precipitation monitoring methods. However, satellite soil moisture retrievals are usually subject to various biases and limited number of retrievals (and therefore large intervals) in remote areas, such as the Tibetan Plateau (TP), and little is known about their potential impacts on precipitation estimation. This study seeks to quantify the uncertainties in Soil Moisture Active and Passive (SMAP) soil moisture estimated precipitation through the commonly used SM2RAIN by referring to in situ soil moisture observations from the central Tibetan Plateau soil moisture network. The estimated precipitation is evaluated against rain gauge observations. Additional attention is paid to different orbits of the SMAP retrievals. Results show that the original SM2RAIN algorithm tends to underestimate the precipitation amount in the central TP when using SMAP soil moisture retrievals as input. The retrieval accuracy and sampling interval of SMAP soil moisture from ascending (descending) orbits each count for 1.04 mm/5 d (−0.18 mm/5 d) and 1.67 mm/5 d (0.72 mm/5 d) of estimated precipitation uncertainties as represented by root mean square error. Besides, the descending product of SMAP with a relatively less sampling interval and higher retrieval accuracy outperforms the ascending one in estimating precipitation, and the combination of both two orbits does add value to the overall SM2RAIN estimation. This study is expected to provide guidance for future applications of SM2RAIN-derived precipitation. Meanwhile, more reliable SM2RAIN precipitation estimations are desired when using higher quality satellite soil moisture products with better retrieval accuracy and smaller intervals. Full article

(This article belongs to the Special Issue Integrating Remote Sensing in Land Surface Monitoring and Agricultural Applications)

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23 pages, 9701 KiB

Open AccessArticle

Comparing Machine Learning Algorithms for Soil Salinity Mapping Using Topographic Factors and Sentinel-1/2 Data: A Case Study in the Yellow River Delta of China

by Jie Li, Tingting Zhang, Yun Shao and Zhengshan Ju

Remote Sens. 2023, 15(9), 2332; https://doi.org/10.3390/rs15092332 - 28 Apr 2023

Cited by 3 | Viewed by 1393

Abstract

Soil salinization is a critical and global environmental problem. Effectively mapping and monitoring the spatial distribution of soil salinity is essential. The main aim of this work was to map soil salinity in Shandong Province located on the Yellow River Delta of China using Sentinel-1/2 remote sensing data and digital elevation model (DEM) data, coupled with soil sampling data, and combined with four regression models: support vector regression (SVR), stepwise multi-regression (SMR), partial least squares regression (PLSR) and random forest regression (RFR). For these purposes, 60 soil samples were collected during the field survey conducted from 9 to 14 October 2019, corresponding to the Sentinel-1/2 and DEM data. Then we established a soil salinity and feature dataset based on the sampled data and the features extracted from Sentinel-1/2 and DEM data. This study adopted the feature importance of the RF model to screen all features. The results showed that the CRSI index made the greatest contribution in retrieving soil salinity in this region. In this paper, 18 sampling points were used to validate and compare the performance of the four models. The results reveal that, compared with the other regression models, the PLSR model has the best performance (R² = 0.66, and RMSE = 1.30). Finally, the PLSR method was used to predict the spatial distribution of soil salinity in the Yellow River Delta. We concluded that the model can be used effectively for the quantitative estimation of soil salinity and provides a useful tool for ecological construction. Full article

(This article belongs to the Special Issue Integrating Remote Sensing in Land Surface Monitoring and Agricultural Applications)

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26 pages, 8512 KiB

Open AccessArticle

Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method

by Pei Mi, Chaolei Zheng, Li Jia and Yu Bai

Remote Sens. 2023, 15(8), 2116; https://doi.org/10.3390/rs15082116 - 17 Apr 2023

Viewed by 1460

Abstract

Global, long-term, gap-free, high quality soil moisture products are extremely important for hydrological monitoring and climate change research. However, soil moisture products produced from satellite observations have data gaps due to the limited capabilities of satellite orbit/swath and retrieval algorithms, which limit the regional and global applications of soil moisture data in hydrology and agriculture studies. To solve this problem, we proposed a gap-filling method to reconstruct a global gap-free surface soil moisture product by applying the machine learning (Random Forest) algorithm on a pixel-by-pixel basis, taking into account the nonlinear relationship between surface soil moisture and the related surface environmental variables. The gap-filling method was applied to the NN-SM surface soil moisture product, which has a fraction of data gaps of around 50% globally on a multi-year average. A global daily gap-free surface soil moisture dataset from 2002 to 2020 was then generated. The reconstructed values of several sub-regions after manually eliminating the original values were cross-verified with the original data, and this clearly demonstrated the reliability of the reconstruction method with the correlation coefficient (R) ranging between 0.770 and 0.918, the Root Mean Square Error (RMSE) between 0.057 and 0.082 m³/m³, the unbiased Root Mean Square Error (ubRMSE) between 0.053 and 0.081 m³/m³, and Bias between −0.012 and 0.008 m³/m³. The accuracy of the reconstructed surface soil moisture dataset was evaluated using in situ observations of surface soil moisture at 12 sites from the International Soil Moisture Network (ISMN) and the Long-Term Agroecosystem Research (LTAR) network, and the results showed good accuracy in terms of R (0.610), RMSE (0.067 m³/m³), ubRMSE (0.045 m³/m³) and Bias (0.031 m³/m³). Overall, the reconstructed surface soil moisture dataset retained the characteristics of the NN-SM product, such as high accuracy and good spatiotemporal pattern. However, with the advantage of continuous spatiotemporal coverage, it is more suitable for further applications in the analysis of global surface soil moisture trends, land surface hydrological processes, and land-atmosphere energy and water exchanges, etc. Full article

(This article belongs to the Special Issue Integrating Remote Sensing in Land Surface Monitoring and Agricultural Applications)

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21 pages, 8879 KiB

Open AccessArticle

The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model

by Suping Nie, Xiaolong Jia, Weitao Deng, Yixiong Lu, Dongyan He, Liang Zhao, Weihua Cao and Xueliang Deng

Remote Sens. 2023, 15(1), 59; https://doi.org/10.3390/rs15010059 - 22 Dec 2022

Cited by 1 | Viewed by 1146

Abstract

Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST. Full article

(This article belongs to the Special Issue Integrating Remote Sensing in Land Surface Monitoring and Agricultural Applications)

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