Mapping farmland soil organic carbon density in plains with combined cropping system extracted from NDVI time-series data

Wu, Zihao; Liu, Yanfang; Han, Yiran; Zhou, Junli; Liu, Jiamin; Wu, Jingan

doi:10.1016/j.scitotenv.2020.142120

Cited by 47 publications

(22 citation statements)

References 77 publications

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“…It not only ensures crop security production but also improves the income of farmers and effectively alleviates the social problems of rural laborers moving to cities and no production activities in cultivated land due to insufficient income (Xu et al, 2019a ). Different cropping systems not only lead to differences in soil physical, chemical, and biological properties (Viaud et al, 2018 ; Wu et al, 2021 ) but also affect crop yield and quality (Caviglia et al, 2011 ), as well as have important implications for greenhouse gas emissions (Zhang, 2019 ). Under multiple cropping system, different fertilization methods, crop residue management, and rhizosphere microecosystem mediated by root exudates lead to differences in soil nutrient cycle and microbial community (Arcand et al, 2016 ; Li et al, 2017 ; Guyonnet et al, 2018 ; Zhao et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Soil Bacterial Community in the Multiple Cropping System Increased Grain Yield Within 40 Cultivation Years

Chen

Zheng

et al. 2021

Front. Plant Sci.

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The shortage of land resources restricts the sustainable development of agricultural production. Multiple cropping has been widely used in Southern China, but whether the continuous planting will cause a decline in soil quality and crop yield is unclear. To test whether multiple cropping could increase grain yield, we investigated the farmlands with different cultivation years (10–20 years, 20–40 years, and >40 years). Results showed that tobacco-rice multiple cropping rotation significantly increased soil pH, nitrogen nutrient content, and grain yield, and it increased the richness of the bacterial community. The farmland with 20–40 years of cultivation has the highest soil organic carbon (SOC), ammonium nitrogen, and grain yield, but there is no significant difference in the diversity and structure of the bacterial community in farmlands with different cultivation years. The molecular ecological network indicated that the stability of the bacterial community decreased across the cultivation years, which may result in a decline of farmland yields in multiple cropping system> 40 years. The Acidobacteria members as the keystone taxa (Zi ≥ 2.5 or Pi ≥ 0.62) appeared in the tobacco-rice multiple cropping rotation farmlands, and the highest abundance of Acidobacteria was found in the farmland with the highest SOC and ammonium nitrogen content, suggesting Acidobacteria Gp4, GP7, GP12, and GP17 are important taxa involved in the soil carbon and nitrogen cycle. Therefore, in this study, the multiple cropping systems for 20 years will not reduce the crop production potential, but they cannot last for more than 40 years. This study provides insights for ensuring soil quality and enhancing sustainable agricultural production capacity.

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Section: Introductionmentioning

confidence: 99%

Soil Bacterial Community in the Multiple Cropping System Increased Grain Yield Within 40 Cultivation Years

Chen

Zheng

et al. 2021

Front. Plant Sci.

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“…And because the systems are predefined based on the actual farming situations, the resultant map could have more important practical benefits. Second, previous studies only focused on the crop sequences themselves [58], while this study suggests that temporal information (e.g., temporal crop diversity, and cropping intensity) could be integrated into crop rotation maps, which can produce more informative crop rotation maps with implications for multiple objectives, e.g., crop diversity analysis [59], [60], soil organic carbon mapping [61], [62]. This may be more consistent with the concept of land system science [63].…”

Section: Discussionmentioning

confidence: 93%

Mapping the Complex Crop Rotation Systems in Southern China Considering Cropping Intensity, Crop Diversity, and Their Seasonal Dynamics

Liu

Zhou

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Crop rotation increases crop yield, improves soil health, and reduces plant disease. Mapping crop rotation is difficult because crop data from a single time point do not sufficiently represent the dynamics of a system. Studies have tried to map crop rotation by sequentially combining crop maps. However, this produced a large number of meaningless crop sequences, hindering the assessment of rotational benefits at regional scales. Here, we propose a crop rotation classification scheme that integrates temporal information into static crop maps and develop an innovative approach to map crop rotation. We chose a typical multiple cropping region in southern China. Given that the landscape is characterized by high crop diversity (e.g., food crops, cash crops, and permanent crops) and variable cropping intensity, our classification scheme first defines three main rotation systems, i.e., paddy, vegetable, and orchard systems, and then further divides the systems into nine subsystems according to their seasonal dynamics. Finally, we apply time series of Sentinel-1 and Sentinel-2 images to identify the systems by a hierarchical rule-based method. The map of crop rotation systems in 2020 had producer, user, and overall accuracies of 81%, 79%, and 81%, respectively. The results indicate that integrating temporal information into the classification scheme is vital to representing complex rotation systems and that remotely sensed temporal dynamics of crops are useful to characterize these systems. It also shows that crop rotation can be mapped directly rather than aggregating multiple crop layers, thus providing a new perspective for mapping and understanding crop rotation systems.Index terms-Land monitoring, cropping intensity, crop diversity, crop rotation, Sentinel-1/2, Google Earth Engine (GEE) I. INTRODUCTIONROP rotation is the practice of growing a series of crop types on the same land across consecutive seasons or years [1]. It has been found to increase crop yield, improve soil health, and reduce plant disease [2]-[4], suggesting that crop rotation could be considered an option for ensuring food production without compromising the environment. Crop rotation practices can be found all over the world. Amidst concerns over the impacts of increased chemical inputs on soil and water quality, the United States is Manuscript received May 19, 2022.

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“…Our results support our hypothesis that modeling strategies that include variables with physical meaning have the highest accuracy, presumably due to an improved capacity to interpret SOC variation using remote sensing data. The application of remote sensing data to estimate SOC has received much attention, as some studies have demonstrated that the ability to model the spatial variability in SOC can be improved by using phenological parameters extracted from remote sensing data (He et al, 2021; Wu et al, 2021; Yang et al, 2020). This study provides evidence that the inclusion of refined Sentinel‐1 images associated with phenological stages in models could improve the prediction of SOC stocks in salt marsh environments.…”

Section: Discussionmentioning

confidence: 99%

Monitoring soil organic carbon under coastal restoration using time series Sentinel‐1

Yang,

Huang

2024

Land Degrad Dev

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Salt marsh soils are key reservoirs of organic carbon. Although the potential for using Sentinel‐1 data to estimate organic carbon in marsh soils has been recognized, there is still a need to find suitable proxies and effective algorithms that enhance predictions. In this study, we assessed the effectiveness of Sentinel‐1 data for predicting soil organic carbon (SOC) stocks in salt marshes located on the coast of eastern‐central China. We defined the relations between SOC stocks and remotely sensed data using a knowledge‐based approach (i.e., structural equation modeling (SEM)), and the results were compared with those obtained using a common data‐driven approach (i.e., random forest (RF)). Predictive models were developed using (1) refined images associated with phenological stages (phenological variables), (2) refined images that were classified as statistically important using Monte Carlo feature selection (MCFS‐based variables), and (3) images from the combination of these refined datasets. The predictions were validated using a leave‐one‐out cross‐validation approach. The results showed that SEM was more accurate than RF when predicting SOC stocks using the same type of predictor variables. Models using phenological variables alone yielded the least accurate predictions. Adding phenological variables to the structural equation model with MCFS‐based variables increased the prediction accuracy by 36% in terms of the R2. The results of the case study suggest that images related to phenological stages are good predictors for mapping SOC stocks in marshes. This study highlights the superiority of SEM over RF for developing effective remote sensing‐based models to quantify and map SOC stocks in salt marshes.

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Mapping farmland soil organic carbon density in plains with combined cropping system extracted from NDVI time-series data

Cited by 47 publications

References 77 publications

Soil Bacterial Community in the Multiple Cropping System Increased Grain Yield Within 40 Cultivation Years

Soil Bacterial Community in the Multiple Cropping System Increased Grain Yield Within 40 Cultivation Years

Mapping the Complex Crop Rotation Systems in Southern China Considering Cropping Intensity, Crop Diversity, and Their Seasonal Dynamics

Monitoring soil organic carbon under coastal restoration using time series Sentinel‐1

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