Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Accurate determination of rice residue cover (RRC) can improve the monitoring of tillage information. Currently, the accurate determination of RRC using optical remote sensing is hindered by variations in cropland moisture and cover of following crops. The fractional cover (FC) of the soil (fS), crop (fC), and crop residue (fCR) changes (fS + fC + fCR = 1) after the following crop is planted, which increases the difficulty of remote-sensing RRC estimation. Cropland soil moisture and crop residue moisture affect the values of cropland and crop residue spectral indices (CRSIs), thereby reducing the accuracy of remote-sensing RRC estimation. Deep learning techniques (e.g., convolutional neural networks [CNN] and transfer learning [TL]) have been proven to extract the deep features of input images with distortion invariance, such as displacement and scaling, which are similar to moisture and the following crop effects on remote-sensing CRSIs. This study aimed to evaluate the combined use of deep features of cropland spectra extracted by deep learning techniques to estimate the cropland RRC under the effects of variations in cropland moisture and cover of the following crops. This study proposes an RRCNet CNN that fuses deep and shallow features to improve RRC estimation. A PROSAIL radiative transfer model was employed to simulate a cropland “soil–crop–crop residue” mixed spectra dataset (n = 103,068), considering the variations in cropland moisture and the cover of the following crop. The RRCNet was first pre-trained using the simulated dataset, and then the knowledge from the pre-trained RRCNet was updated based on field experimental FCs, RRCs, and Sentinel-2 image spectra using the TL technique. Our study indicates that RRCNet can incorporate shallow and deep spectral features of cropland “soil–crop–crop residue” mixed spectra, providing high-performance FCs and RRC estimation. The FCs and RRC estimates from RRCNet + TL (FCs: R2 = 0.939, root mean squared error (RMSE) = 0.071; RRC: R2 = 0.891, RMSE = 0.083) were more accurate than those from CRSI + multiple linear regression, CRSI + random forest, and CRSI + support vector machine (FCs: R2 = 0.877–907, RMSE = 0.086–0.101; RRC: R2 = 0.378–0.714, RMSE = 0.133–0.229). We mapped the multistage RRC based on Sentinel-2 multispectral instrument (MSI) images and RRCNet. Tillage information can be inferred from RRC and RRC difference maps changes. 

Mapping cropland rice residue cover using a radiative transfer model and deep learning

This research aims to find crop-based residue burning (CRB) amounts for the Southeastern Anatolia Region of Türkiye, where versatile and diverse of agricultural fields are available. We used the time series of Sentinel-2 images to identify spatial distribution of different crop types and to determine the burned agricultural fields in 2019 using Google Earth Engine platform. We implemented Intergovernmental Panel on Climate Change standards to calculate the emissions from Greenhouse gases and Particulate Matter. We also analyzed Sentinel-5P images to generate Nitrogen Dioxide maps and compared them with crop-based emission calculations. Our analysis illustrated that 14,444.307 Gg of Greenhouse Gases and 117.809 Gg of Particulate Matters were released in 2019 due to CRB practices. Our results can potentially improve the national statistics by providing spatio-temporal field-based CRB emissions and supporting country-level agricultural decision-making processes. Researchers could implement our proposed data and methodology in other regions suffering from the CRB problem. 

Estimation of emissions from crop residue burning in Türkiye using remotely sensed data and the Google Earth Engine platform

As the population grows, the development of conservation tillage offers a means of promoting the sustainability of agricultural engineering. Remote sensing images with high spatial and temporal resolutions enable the accurate monitoring of conservation tillage on a broad spatial scale, further promoting conservation tillage research. This paper describes using streamlined time series Sentinel-2 images based on the Google Earth Engine (GEE) cloud platform for mapping maize tillage practices in the Songnen Plain region of Northeast China. Based on the correlation with the normalized difference tillage index (NDTI) and maize residue coverage (MRC) data, the optimal time series and streamlining functions in the GEE cloud platform are determined. Estimates of MRC and the mapping of tillage practices in the Songnen Plain for 2019–2022 are then determined using GEE and a previous model. Geostatistical analysis using ArcGIS is applied to analyze the spatial and temporal distribution characteristics of MRC and conservation tillage over the Songnen Plain. The results show that time series images from 20–30 May achieve an r value of 0.902 and an R2 value of 0.8136 when using the median streamlining function. The mean MRC for the study area in 2022 is 2.3%, and an overall upward trend in conservation tillage is observed (from 0.08% in 2019 to 0.25% in 2022). Our analysis shows that MRC monitoring and conservation tillage mapping can be performed over a broad spatial scale using remote sensing technology based on the GEE cloud platform. Spatial and temporal information on farm practices provides a theoretical basis for agricultural development planning efforts, which can promote sustainable agricultural development.

/pdf/mapping-maize-tillage-practices-over-the-songnen-plain-in-cp69c5xo.pdf

Mapping Maize Tillage Practices over the Songnen Plain in Northeast China Using GEE Cloud Platform

Conservation tillage mapping and monitoring using remote sensing

Crop residues are effective for the prevention of soil erosion. The crop residue cover (CRC) can be mapped by remote sensing. Different morphologies of crop residue will affect the spectral reflectance, reducing the accuracy of CRC estimation by multispectral data. However, the influence of residue morphology is not fully considered on the accuracy of CRC mapping using satellite images. In addition, the spectral indices are easily saturated and less sensitive to high-density areas of crop residue. This study selected four maize planting sites to obtain hyperspectral reflectance and unmanned aerial vehicle (UAV) images. The effects of CRC and residue morphology on spectral reflectance were analyzed, and a new Residue Adjust Normalized Difference Residue Index (RANDRI) was proposed. UAV images were used to extract ground CRC data for training a CRC prediction model based on Sentinel-2 MSI data. Finally, the piecewise prediction model based on different residue indices was used to map CRC. The study results highlighted a linear relationship between the reflectance intersection of shortwave infrared 2 reflectance and red edge 3 of Sentinel-2 MSI with different residual morphologies, called the residue line. The model accuracy of RANDRI optimized by residual line parameters was better than that of the Normalized Difference Residue Index and Soil Adjust Normalized Difference Residue Index (SANDRI) in the high-density area of crop residue. RANDRI can weaken the influence of residue morphologies on modeling accuracy. The CRC spatial distribution by the piecewise SANDRI+RANDRI model was more consistent with CRC measured than that of the RANDRI models individually. The determination coefficient of the piecewise model was 0.82, and the relative error was 10.66%. The piecewise model can effectively improve the anti-saturation ability of the spectral indices. We suggest a rapid and accurate approach for monitoring the CRC and provide a more suitable CRC mapping strategy for high-density areas of crop residue using multispectral remote sensing data. • Crop residue cover was mapped using remote sensing. • The effects of crop residue cover and residue morphology on spectral reflectance were analyzed. • Novel crop residue cover prediction models were suggested and analyzed. 

Mapping crop residue cover using Adjust Normalized Difference Residue Index based on Sentinel-2 MSI data

Conservation farming systems with straw return and no-tillage are recommended as effective methods for soil quality improvement, and they are often accompanied by increases in soil fauna populations. Recently, attempts have been made to improve the soil fertility in conservation agriculture through enhanced bioturbation by adding earthworms to no-till fields. However, the mechanisms associated with the responses in terms of soil functions, such as microbial diversity and microbial nutrient metabolism, to the synergistic effects of biotic and abiotic factors remain unclear. In this study, plow layer and plow pan soils were collected from subtropic cropland, and a full factorial microcosm experiment was conducted to quantify the effects of straw mulching and adding earthworms on the soil nutrient contents, microbial diversity, and extracellular enzymatic activities. The results showed that the single application of straw significantly increased the soil organic carbon (C), the ratio of C to nitrogen (N), and the ratio of C to phosphorus (P) in the plow layer soil (p < 0.05). The combined addition of straw and earthworms significantly increased the microbial diversity (Observed species, Chao 1 and Shannon), C/N/P-acquiring enzymatic activities, and microbial C limitation (p < 0.05), but decreased the microbial P limitation (p < 0.05) in the plow pan soil. Adding straw or earthworms did not affect the soil nutrient content in the plow pan soil (p > 0.05), microbial diversity, C-acquiring enzymatic activities, and microbial C and P limitations in the plow layer soil (p > 0.05). Structural equation models indicated that straw mulching indirectly affected the soil microbial diversity and microbial nutrient limitation through the buffering effects of aggregate stability and the C/P ratio in the plow layer soil. By contrast, the soil microbial activities were directly influenced by straw and earthworm addition in the plow pan soil. These findings indicate that earthworms enhanced soil microbial metabolism when straw was added by improving the substrate availability, and the soil physicochemical properties determined the asynchronous responses of the soil functions to land management. We suggest that the combined addition of organic matter and earthworms can improve the quality of soil in infertile land. 

Synergistic effects of straw and earthworm addition on microbial diversity and microbial nutrient limitation in a subtropical conservation farming system

Development of a new index for automated mapping of ratoon rice areas using time-series normalized difference vegetation index imagery

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Mapping crop residue cover using Adjust Normalized Difference Residue Index based on Sentinel-2 MSI data

Synergistic effects of straw and earthworm addition on microbial diversity and microbial nutrient limitation in a subtropical conservation farming system

Development of a new index for automated mapping of ratoon rice areas using time-series normalized difference vegetation index imagery